R Notebook
Part A
Descriptive Analysis
#load package
library(haven)#download datasets - AddHealth
wave1<-read_dta("/projects/add_health/data/11613344/ICPSR_27021/DS0001/da27021p1.dta")
wave3<-read_dta("/projects/add_health/data/11613344/ICPSR_27021/DS0003/da27021p3.dta")
wave4<-read_dta("/projects/add_health/data/11613344/ICPSR_27021/DS0012/da27021p12.dta")
addhealth.wghtW4<-read_dta("/projects/add_health/data/11613344/ICPSR_27021/DS0028/da27021p28.dta")
#addhealth<-merge(wave1,wave3,by="aid")
addhealth<-merge(wave1,wave3,by="aid")
addhealth<-merge(addhealth,wave4,by="aid")
addhealth<-merge(addhealth,addhealth.wghtW4,by="aid")#install and load packages
install.packages("rlang", repos="http://cran.us.r-project.org", dependencies = TRUE)## Installing package into '/storage01/users/izf381/R/x86_64-redhat-linux-gnu-library/3.6'
## (as 'lib' is unspecified)#library(rlang)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union#select variables to use
addhealth<-addhealth%>%
select(aid,H3ID15,H4SE31,H1GI6A,H1GI6B,H1GI4,H3OD4A,H3OD4B,H3OD2,H1GI1M,H1GI1Y,H3OD1M,H3OD1Y,H4OD1M,H4OD1Y,BIO_SEX,H4ID5H,iyear,imonth,iday,H1FV6,H3DS18F,H3DS18G,H4DS18,H4SE1,H4SE2,H3TO130,H3TO131,IMONTH4,IYEAR4,H3SE13,IYEAR3,IMONTH3,H1SU1,H4HS1,H4HS4,H4ED2,H4EC1,H4EC4,H4MH2,H4MH28,H4SE2,H4SE32,H4SE34,H4DS15,H4DS16,H4DS17,H4DS18,H4TO3,H4TO35,H4TO36,H4TO65B,H4TO65C,H4TO65D,H4MA1,H4MA3,H4MA5,H4PE40,H4ID5H,H4ID5I,H4ID5J,H4EC18,H4OD4,H3HS5,H3HS6,H3OD38,H3ID33,H3SP3,H3EC2,H3DS16,H3DS18B,H3DS18C,H3DS18D,H3DS18E,H3DS18F,H3DS18G,H3TO38,H3TO39,H3MA3,H3MA4,H4TR1,H3SE14,H1GI6D,H1GI6C,H1GI6E,H4OD4,H1PL1,H1WP9,H1WP13,psuscid,region,GSWGT4,H3SP9,H4MH3,H4MH4,H4MH5,H4MH6,H4MH7,H4MH8,H3SP3,H3TO38,H3SP4,H3SP5,H3SP6,H3SP10,H3SP13,H3SP24,H3GH8, H3MN1,H4TO53,H4TO65C,H4TO65D,H4TO65E,H4TO66,H4TO64A,H4TO64B,H4TO64C,H4TO64D,H3TO111,H3SP9,H4TO54,H4TR1,H4SE2,H4LM4,H3LM1,H4ED2,H4MH2,H4WS4,H4GH1,H4CJ1,H3OD31,H4TR1,H4RD6,H4LM17,H3LM3,H4TR3,H4TR10,H4ID1,H4ID4,H4ID5A,H4ID5B,H4ID5C,H4ID5D,H4ID5E,H4ID5F,H4ID5G,H4ID5M,H4TR3,H4TR10,H4SE1,H4SE2,H4CJ17)
#complete cases
addhealth<-addhealth%>%
filter(complete.cases(.))#load package
library(car)## Loading required package: carData##
## Attaching package: 'car'## The following object is masked from 'package:dplyr':
##
## recode#code variables
#sexual orientation from wave 3
addhealth$sexorient <- factor(ifelse(addhealth$H3SE13=="(1) 100% heterosexual (straight)",1,
ifelse(addhealth$H3SE13=="(2) Mostly #heterosexual/somewhat attracted to people of own",2,
ifelse(addhealth$H3SE13=="(3) Bisexual-attracted #to men and women equally",3,
ifelse(addhealth$H3SE13=="(4) Mostly #homosexual/somewhat attracted to opposite sex",4,
ifelse(addhealth$H3SE13=="(5) 100% #homosexual (gay)",5,NA))))))
addhealth$sexorient<-Recode(addhealth$H3SE13, recodes="1:2='a_straight'; 3='b_bisexual';4:5='c_LGB';else=NA",as.factor=T)#code variables
addhealth$social_isolationW4<-Recode(addhealth$H4MH2,recodes="0:1='a_never/rarely';2:3='b_sometimes/often';else=NA")
addhealth$forcedsex_verbalW4<-Recode(addhealth$H4SE32,recodes="0='a_no';1='b_yes';else=NA")
addhealth$forcedsex_physicalW4<-Recode(addhealth$H4SE34,recodes="0='a_no';1='b_yes';else=NA")
addhealth$educ<-Recode(addhealth$H4ED2,recodes="1:2='a_less_highschool';3:6='b_highschool_grad';7:8='c_college_bach';8:13='d_college+';else=NA")
addhealth$depressionW4<-Recode(addhealth$H4ID5H,recodes="0='a_no';1='b_yes';else=NA")
addhealth$anxiety.panic_disorder_W4<-Recode(addhealth$H4ID5J,recodes="0='a_no';1='b_yes';else=NA")
addhealth$things.going.mywayW4<-Recode(addhealth$H4MH5,recodes="0:1='a_no';2='b_sometimes';3:4='c_often';else=NA")
addhealth$unmet_medcareW4 <- Recode(addhealth$H4HS4,recodes="0='a_no';1='b_yes';else=NA")
addhealth$insurance_statusW4 <- Recode(addhealth$H4HS1,recodes="1='a_no_insurance';2:10='b_yes_insurance';else=NA")
addhealth$threatened_gun_knifeW4<-Recode(addhealth$H4DS15,recodes="0='a_no';1='b_yes';else=NA")
addhealth$harmed_gun_knifeW4<-Recode(addhealth$H4DS16,recodes="0='a_no';1='b_yes';else=NA")
addhealth$beaten_upW4<-Recode(addhealth$H4DS18,recodes="0='a_no';1='b_yes';else=NA")
addhealth$lack_autonomyW4<-Recode(addhealth$H4PE40,recodes="1:2='b_lacks_autonomy';3='a_nuetral';4:5='c_has_autonomy';else=NA")
addhealth$alcohol_day_permonthW4<-Recode(addhealth$H4TO35,recodes="0:3='a_>12days/year';4='b_1or2days/week';5='c_3to5days/week';6='d_daily';else=NA")
addhealth$try.cocaineW4<-Recode(addhealth$H4TO65C,recodes="0='a_no';1='b_yes';else=NA")
addhealth$try.methW4<-Recode(addhealth$H4TO65D,recodes="0='a_no';1='b_yes';else=NA")
addhealth$try.herionW4<-Recode(addhealth$H4TO65E,recodes="0='a_no';1='b_yes';7='skip';else=NA")
addhealth$had.injected.drugsW4<-Recode(addhealth$H4TO66,recodes="0='a_no';1='b_yes';7='c_skip';else=NA")
addhealth$sedatives.downersW4<-Recode(addhealth$H4TO64A,recodes="0='a_no';1='b_yes';7='c_skip';else=NA")
addhealth$opioidsW4<-Recode(addhealth$H4TO64D,recodes="0='a_no';1='b_yes';7='c_skip';else=NA")
addhealth$general_health<-Recode(addhealth$H4GH1,recodes="1:3='a_good/excellent';4:5='b_poor/bad';else=NA")
addhealth$friendships<-Recode(addhealth$H4WS4,recodes="1='a_none';2='b_1or2';3:4='c_3to9';5='d_10+';else=NA")
addhealth$arrested<-Recode(addhealth$H4CJ1,recodes="0='a_no';1='b_yes';else=NA")
addhealth$foster_home<-Recode(addhealth$H3OD31,recodes="0='a_no';1='b_yes';else=NA")
addhealth$unloved_bycaretakerW4<-Recode(addhealth$H4MA1,recodes="1:4='b_>10_times';5='c_10+_times';6='a_never';else=NA")
addhealth$hit_kick_thrown_bycaretakerW4<-Recode(addhealth$H4MA3,recodes="1:4='b_>10_times';5='c_10+_times';6='a_never';else=NA")
addhealth$sex_abuse_bycaretakerW4<-Recode(addhealth$H4MA5,recodes="1:4='b_>10_times';5='c_10+_times';6='a_never';else=NA")
addhealth$marriedW4a<-Recode(addhealth$H4TR1,recodes="0='b_married';1:4='a_nevermarried';else=NA")
addhealth$marriedW4b<-Recode(addhealth$H4RD6,recodes="1='a_together';2:3='b_seperated/divorced';7='c_skip';else=NA")addhealth$incarcerated<-Recode(addhealth$H4CJ17,recodes="0='a_no';1='b_yes';7='skip';else=NA")
addhealth$physical_limitation<-Recode(addhealth$H4ID1,recodes="1='a_not limited';2='b_limited';else=NA")
addhealth$brace_wheelchair<-Recode(addhealth$H4ID4,recodes="0='a_no';1='b_yes';else=NA")
addhealth$cancer<-Recode(addhealth$H4ID5A,recodes="0='a_no';1='b_yes';else=NA")
addhealth$high_chol<-Recode(addhealth$H4ID5B,recodes="0='a_no';1='b_yes';else=NA")
addhealth$high_bp<-Recode(addhealth$H4ID5C,recodes="0='a_no';1='b_yes';else=NA")
addhealth$diabetes<-Recode(addhealth$H4ID5D,recodes="0='a_no';1='b_yes';else=NA")
addhealth$heart_disease<-Recode(addhealth$H4ID5E,recodes="0='a_no';1='b_yes';else=NA")
addhealth$asthm<-Recode(addhealth$H4ID5F,recodes="0='a_no';1='b_yes';else=NA")
addhealth$migrane<-Recode(addhealth$H4ID5G,recodes="0='a_no';1='b_yes';else=NA")
addhealth$HIV<-Recode(addhealth$H4ID5M,recodes="0='a_no';1='b_yes';5='not.asked';else=NA")
addhealth$pregnancy<-Recode(addhealth$H4TR3,recodes="0='a_none';1:15='b_yes';else=NA")
addhealth$births<-Recode(addhealth$H4TR10,recodes="0='a_none';1:7='b_yes';97='skip';else=NA")
addhealth$suicideW4think<-Recode(addhealth$H4SE1,recodes="0='a_no';1='b_yes';7='skip';else=NA")
addhealth$suicideW4attempt<-Recode(addhealth$H4SE2,recodes="0='a_no';1:4='b_yes';7='skip';else=NA")#Date of birth variables; Wave 1: H1GI1M (month), H1GI1Y (year); Wave 3: H3OD1M (month), H3OD1Y (year); Wave 4: H4OD1M (month), H4OD1Y (year)
#birth year w1
addhealth$birthyearw1 <- factor(ifelse(addhealth$H1GI1Y=="74",1974,
ifelse(addhealth$H1GI1Y=="75",1975,
ifelse(addhealth$H1GI1Y=="76",1976,
ifelse(addhealth$H1GI1Y=="77",1977,
ifelse(addhealth$H1GI1Y=="78",1978,
ifelse(addhealth$H1GI1Y=="79",1979,
ifelse(addhealth$H1GI1Y=="80",1980,
ifelse(addhealth$H1GI1Y=="81",1981,
ifelse(addhealth$H1GI1Y=="82",1982,
ifelse(addhealth$H1GI1Y=="83",1983,1979)))))))))))
#birth month w1
addhealth$birthmonthw1 <- Recode(addhealth$H1GI1M,recodes="1=1;2=2;3=3;4=4;5=5;6=6;7=7;8=8;9=9;10=10;11=11;12=12;else=NA")
#combine year and month for a birth date wave 1
library(zoo)##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numericaddhealth$birthdatew1 <- as.yearmon(paste(addhealth$birthyearw1, addhealth$birthmonthw1), "%Y %m")
#birth month wave 4
addhealth$birthmonthw4 <- Recode(addhealth$H4OD1M,recodes="1=1;2=2;3=3;4=4;5=5;6=6;7=7;8=8;9=9;10=10;11=11;12=12")
#birth year wave 4
addhealth$birthyearw4<-addhealth$H4OD1Y
addhealth$birthyearw4 <- Recode(addhealth$H4OD1Y,recodes="1974=1974;1975=1975;1976=1976;1977=1977;1978=1978;1979=1979;1980=1980;1981=1981;1982=1982;1983=1983")
#combine year and month for birth date wave 4
addhealth$birthdatew4 <- as.yearmon(paste(addhealth$birthyearw4, addhealth$birthmonthw4), "%Y %m")
#birth date wave 3
addhealth$birthdatew3<-as.yearmon(paste(addhealth$H3OD1Y,addhealth$H3OD1M),"%Y %m")
#interview date w1
addhealth$iyearfix <- Recode(addhealth$iyear,recodes="'94'='1994';'95'='1995'")
addhealth$interviewdatew1 <- as.yearmon(paste(addhealth$iyearfix, addhealth$imonth), "%Y %m")
#interview date w4
addhealth$interviewmonthw4 <- Recode(addhealth$IMONTH4,recodes="1=1;2=2;3=3;4=4;5=5;6=6;7=7;8=8;9=9;10=10;11=11;12=12")
addhealth$interviewyearw4 <- Recode(addhealth$IYEAR4,recodes="2007=2007;2008=2008;2009=2009")
addhealth$interviewdatew4<-as.yearmon(paste(addhealth$interviewyearw4,addhealth$interviewmonthw4),"%Y %m")
#interview date w3
addhealth$interviewdatew3<-as.yearmon(paste(addhealth$IYEAR3,addhealth$IMONTH3 ),"%Y %m")
#age - derived from date of birth subtracted from wave interview date
addhealth$agew4<-(as.numeric(round(addhealth$interviewdatew4 - addhealth$birthdatew4)))
addhealth$agew3<-(as.numeric(round(addhealth$interviewdatew3 - addhealth$birthdatew3)))
addhealth$agew1<-(as.numeric(round(addhealth$interviewdatew1 - addhealth$birthdatew1)))#race variables
#nhwhite
addhealth$nhwhite <- ifelse(addhealth$H1GI6A==1,1,0)
addhealth$nhwhite<-Recode(addhealth$nhwhite, recodes="1='nhwhite'; 0='nonnhwhite'; 6:8=NA",as.factor=T)
#nhblack
addhealth$nhblack <- ifelse(addhealth$H1GI6B==1,1,0)
addhealth$nhblack<-Recode(addhealth$nhblack, recodes="1='nhblack'; 0='nonnhblack'; 6:8=NA",as.factor=T)
#Hispanic
addhealth$hispanic <- ifelse(addhealth$H1GI4==1,1,0)
addhealth$hispanic<-Recode(addhealth$hispanic, recodes="1='hispanic'; 0='nonhispanic'; 6:8=NA",as.factor=T)
#Asian
addhealth$asian <- ifelse(addhealth$H1GI6D==1,1,0)
addhealth$asian<-Recode(addhealth$asian, recodes="1='asian'; 0='nonasian'; 6:8=NA",as.factor=T)
#Native American
addhealth$native_american <- ifelse(addhealth$H1GI6C==1,1,0)
addhealth$native_american<-Recode(addhealth$native_american, recodes="1='native_american'; 0='nonnative_american'; 6:8=NA",as.factor=T)
#other
addhealth$other <- ifelse(addhealth$H1GI6E==1,1,0)
addhealth$other<-Recode(addhealth$other, recodes="1='other'; 0='nonother'; 6:8=NA",as.factor=T)
#combine race to one variable
addhealth$racethnic <- factor(ifelse(addhealth$nhwhite=="nhwhite","a-nhwhite",
ifelse(addhealth$nhblack=="nhblack", "b-nhblack",
ifelse(addhealth$hispanic=="hispanic", "c-hispanic",
ifelse(addhealth$asian=="asian","d-asian",
ifelse(addhealth$native_american=="native_american","e-native_american",
ifelse(addhealth$other=="other","f-other",NA)))))))
#sex variable
addhealth$sex <- ifelse(addhealth$BIO_SEX==2,2,1)
addhealth$sex<-Recode(addhealth$sex, recodes="1='a_male'; 2='b_female'",as.factor=T) #income variable
addhealth$incomeW4<-Recode(addhealth$H4EC1,recodes="1='a_>$15,000';4:6='b_$15,000<$30,000';7:8='c_$30,000<$50,000';9='d_$50,000<$75,000';10='e_$75,000<$100,000';11='f_$100,000<$150,000';12='g_$150,000+';else=NA")
addhealth$treated_lessrespectW4<-Recode(addhealth$H4MH28,recodes="0:1='a_never/rarely';2:3='b_sometimes/often';else=NA")
addhealth$suicideW4think<-Recode(addhealth$H4SE1, recodes="0='a_no';1='b_yes';else=NA",as.factor=T)
addhealth$suicideW4attempt<-Recode(addhealth$H4SE2, recodes="0='a_no';1='once';2:3='b_2-4_times';4='c_5+times';else=NA",as.factor=T)#variables for having a job and losing a job
addhealth$joblossW4<-Recode(addhealth$H4LM4,recodes="0='a_no';1:50='b_yes';else=NA")
addhealth$jobW3<-Recode(addhealth$H3LM1,recodes="0='b_not_employeed';1='a_employed';else=NA")Definitions
1) Define your event variable - The event variable is the transition of employement in Wave 3 to unemployment (fired, layed off, let go) in Wave 4
#transition from job to no job
addhealth$job_transition<-ifelse(addhealth$jobW3=='a_employed'&addhealth$joblossW4=='b_yes',1,0)2) Define a duration or time variable - If the age at unemployment is censored, then the age at the date of interview is the censored age at unemployment, or if not censored, the actual age at umeployment.
#age at job loss
addhealth$job.loss.age<-ifelse(addhealth$job_transition==1,
addhealth$agew3,addhealth$agew4)3) Define a censoring indicator - If someone has experienced the event the are assigned a “0”, if they experience an event they are assigned a “1”.
#select variables to use
addhealth<-addhealth%>%
select(psuscid,region,GSWGT4,agew4,agew3,social_isolationW4,forcedsex_verbalW4,forcedsex_physicalW4,educ,depressionW4,anxiety.panic_disorder_W4,things.going.mywayW4,unmet_medcareW4,insurance_statusW4,threatened_gun_knifeW4,harmed_gun_knifeW4,beaten_upW4,lack_autonomyW4,alcohol_day_permonthW4,try.cocaineW4,try.methW4,try.herionW4,had.injected.drugsW4,sedatives.downersW4,opioidsW4,general_health,friendships,arrested,foster_home,unloved_bycaretakerW4,hit_kick_thrown_bycaretakerW4,sex_abuse_bycaretakerW4,racethnic,sex,incomeW4,treated_lessrespectW4,suicideW4attempt,suicideW4think,joblossW4,jobW3,job_transition,sexorient,marriedW4a,marriedW4b,job.loss.age,job_transition,physical_limitation,brace_wheelchair,cancer,high_chol,high_bp,diabetes,heart_disease,asthm,migrane,HIV,pregnancy,births,suicideW4think,suicideW4attempt,incarcerated)
#filter complete cases
addhealth<-addhealth%>%
filter(complete.cases(.))Descriptive estimates
#load packages
library(survival)
library(survminer)## Loading required package: ggplot2## Registered S3 methods overwritten by 'ggplot2':
## method from
## [.quosures rlang
## c.quosures rlang
## print.quosures rlang## Loading required package: ggpubr## Loading required package: magrittr4) Estimate the following functions of survival time for that variable and plot them:
a. Survival
fittt<-survfit(Surv(time = agew3,time2 = agew4,event = job_transition)~1,data=addhealth)
summary(fittt)## Call: survfit(formula = Surv(time = agew3, time2 = agew4, event = job_transition) ~
## 1, data = addhealth)
##
## time n.risk n.event censored survival std.err lower 95% CI upper 95% CI
## 25 5763 8 22 0.999 0.00049 0.9977 1.000
## 26 5986 183 559 0.968 0.00227 0.9636 0.973
## 27 5291 282 580 0.916 0.00368 0.9093 0.924
## 28 4431 334 898 0.847 0.00498 0.8377 0.857
## 29 3199 344 884 0.756 0.00643 0.7438 0.769
## 30 1971 366 960 0.616 0.00844 0.5995 0.633
## 31 645 126 380 0.496 0.01177 0.4730 0.519
## 32 139 44 83 0.339 0.02114 0.2997 0.383
## 33 12 6 6 0.169 0.05001 0.0949 0.302ggsurvplot(fittt,data = addhealth,risk.table = T,title="Survival function for job transition",xlim=c(20,35))
library(muhaz)b. Hazard
haz<-kphaz.fit(time=addhealth$job.loss.age,status = addhealth$job_transition,method = "product-limit")
haz## $time
## [1] 19.5 20.5 21.5 22.5 23.5 24.5 25.5
##
## $haz
## [1] 0.046535937 0.048623803 0.071241083 0.068640374 0.058342355 0.015398618
## [7] 0.005153801
##
## $var
## [1] 7.963177e-06 8.725978e-06 1.357604e-05 1.402783e-05 1.270448e-05
## [6] 3.487141e-06 1.266909e-06kphaz.plot(haz,main="Hazard Function Plot")
data.frame(haz)c. Cumulative Hazard
ggsurvplot(fittt,data = addhealth,risk.table = T,fun="cumhaz",title="Cumulative Hazard function for job transition",xlim=c(20,35))
plot(cumsum(haz$haz)~haz$time,
main="Cumulative Hazard Function",
ylab="H(t)",xlab="Time in Years",
type=NULL,lwd=2,col=3)
5) Carry out the following analysis
a. Kaplan-Meier survival analysis of the outcome
fit.kaplan<-survfit(Surv(job.loss.age,job_transition)~1,data=addhealth)
summary(fit.kaplan)## Call: survfit(formula = Surv(job.loss.age, job_transition) ~ 1, data = addhealth)
##
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 19 6065 83 0.986 0.00149 0.983 0.989
## 20 5982 272 0.941 0.00301 0.936 0.947
## 21 5710 271 0.897 0.00391 0.889 0.904
## 22 5439 374 0.835 0.00476 0.826 0.845
## 23 5065 336 0.780 0.00532 0.769 0.790
## 24 4729 268 0.736 0.00566 0.725 0.747
## 25 4460 68 0.724 0.00574 0.713 0.736
## 26 4371 21 0.721 0.00576 0.710 0.732b. Define a grouping variable, this can be dichotomous or categorical.
The grouping variabe is sexual orientation
d. Comparison of Kaplan-Meier survival across grouping variable in your data. Interpret your results. It would appear across all groups that the age at highest risk is around 22-23 years old.
fit.kaplan.LGB<-survfit(Surv(job.loss.age,job_transition)~sexorient,data=addhealth)
summary(fit.kaplan.LGB)## Call: survfit(formula = Surv(job.loss.age, job_transition) ~ sexorient,
## data = addhealth)
##
## sexorient=a_straight
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 19 5870 81 0.986 0.00152 0.983 0.989
## 20 5789 257 0.942 0.00304 0.936 0.948
## 21 5532 262 0.898 0.00395 0.890 0.906
## 22 5270 359 0.837 0.00483 0.827 0.846
## 23 4911 325 0.781 0.00540 0.771 0.792
## 24 4586 255 0.738 0.00574 0.727 0.749
## 25 4330 63 0.727 0.00581 0.716 0.739
## 26 4247 20 0.724 0.00584 0.712 0.735
##
## sexorient=b_bisexual
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 19 100 2 0.98 0.0140 0.953 1.000
## 20 98 8 0.90 0.0300 0.843 0.961
## 21 90 8 0.82 0.0384 0.748 0.899
## 22 82 13 0.69 0.0462 0.605 0.787
## 23 69 1 0.68 0.0466 0.594 0.778
## 24 68 5 0.63 0.0483 0.542 0.732
## 25 63 3 0.60 0.0490 0.511 0.704
## 26 60 1 0.59 0.0492 0.501 0.695
##
## sexorient=c_LGB
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 20 95 7 0.926 0.0268 0.875 0.980
## 21 88 1 0.916 0.0285 0.862 0.973
## 22 87 2 0.895 0.0315 0.835 0.959
## 23 85 10 0.789 0.0418 0.712 0.876
## 24 75 8 0.705 0.0468 0.619 0.803
## 25 67 2 0.684 0.0477 0.597 0.784ggsurvplot(fit.kaplan.LGB,conf.int = T,risk.table = F,title="Survivorship Function for Job Transition",xlab="Wave of Survey",ylim=c(.50,1),xlim=c(15,35))
coxph(Surv(time=agew3,time2 = agew4 , event = job_transition) ~ factor(sexorient), data=addhealth) ## Call:
## coxph(formula = Surv(time = agew3, time2 = agew4, event = job_transition) ~
## factor(sexorient), data = addhealth)
##
## coef exp(coef) se(coef) z p
## factor(sexorient)b_bisexual 0.58826 1.80084 0.15821 3.718 0.000201
## factor(sexorient)c_LGB 0.05563 1.05721 0.18427 0.302 0.762731
##
## Likelihood ratio test=11.62 on 2 df, p=0.002997
## n= 6065, number of events= 1693 fit0 <- coxph(Surv(time=agew3, time2 = agew4, event = job_transition) ~ factor(sexorient), data=addhealth,
iter=0, na.action=na.exclude)
o.minus.e <- tapply(resid(fit0), addhealth$sexorient, sum)
obs <- tapply(addhealth$job_transition, addhealth$sexorient, sum)
cbind(observed=obs, expected= obs- o.minus.e, "o-e"=o.minus.e) ## observed expected o-e
## a_straight 1622 1641.24159 -19.241591
## b_bisexual 41 23.04697 17.953025
## c_LGB 30 28.71143 1.288566fit00<-survfit(Surv(time = agew3,time2 = agew4,event = job_transition)~sexorient+sex,addhealth)
summary(fit00)## Call: survfit(formula = Surv(time = agew3, time2 = agew4, event = job_transition) ~
## sexorient + sex, data = addhealth)
##
## sexorient=a_straight, sex=a_male
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 25 2639 3 0.999 0.000656 0.9976 1.000
## 26 2773 96 0.964 0.003525 0.9574 0.971
## 27 2491 147 0.907 0.005633 0.8964 0.918
## 28 2119 172 0.834 0.007468 0.8192 0.848
## 29 1602 178 0.741 0.009323 0.7230 0.760
## 30 1030 213 0.588 0.011923 0.5649 0.612
## 31 358 74 0.466 0.015740 0.4365 0.498
## 32 94 32 0.308 0.025044 0.2622 0.361
## 33 9 4 0.171 0.052811 0.0932 0.313
##
## sexorient=a_straight, sex=b_female
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 25 2941 5 0.998 0.00076 0.9968 1.000
## 26 3022 80 0.972 0.00301 0.9660 0.978
## 27 2621 122 0.927 0.00492 0.9170 0.936
## 28 2173 147 0.864 0.00678 0.8508 0.877
## 29 1503 152 0.777 0.00907 0.7590 0.795
## 30 880 139 0.654 0.01223 0.6304 0.678
## 31 266 46 0.541 0.01823 0.5063 0.578
## 32 41 10 0.409 0.03880 0.3395 0.493
## 33 3 2 0.136 0.11204 0.0272 0.683
##
## sexorient=b_bisexual, sex=a_male
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 26 15 1 0.933 0.0644 0.815 1
## 27 12 1 0.856 0.0950 0.688 1
## 28 9 1 0.760 0.1232 0.554 1
## 30 4 1 0.570 0.1888 0.298 1
##
## sexorient=b_bisexual, sex=b_female
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 26 84 3 0.964 0.0202 0.925 1.000
## 27 79 8 0.867 0.0374 0.796 0.943
## 28 60 12 0.693 0.0539 0.595 0.807
## 29 35 6 0.574 0.0628 0.464 0.712
## 30 21 4 0.465 0.0708 0.345 0.627
## 31 7 3 0.266 0.0959 0.131 0.539
## 32 1 1 0.000 NaN NA NA
##
## sexorient=c_LGB, sex=a_male
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 26 58 2 0.966 0.0240 0.920 1.000
## 27 57 1 0.949 0.0289 0.894 1.000
## 28 48 1 0.929 0.0344 0.864 0.999
## 29 38 6 0.782 0.0621 0.669 0.914
## 30 24 7 0.554 0.0849 0.410 0.748
## 31 9 1 0.492 0.0952 0.337 0.719
## 32 3 1 0.328 0.1483 0.135 0.796
##
## sexorient=c_LGB, sex=b_female
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 26 34 1 0.971 0.0290 0.915 1.000
## 27 31 3 0.877 0.0578 0.770 0.998
## 28 22 1 0.837 0.0675 0.714 0.980
## 29 17 2 0.738 0.0885 0.584 0.934
## 30 12 2 0.615 0.1084 0.436 0.869
## 31 4 2 0.308 0.1631 0.109 0.870#let's briefly explore parametric hazard models
#install.packages("eha")
library(eha)#exponential model
#interval censored
fitl1<-phreg(Surv(time=agew3,time2 = agew4,event = job_transition)~sex+sexorient,data=addhealth,dist="weibull",shape=1)
summary(fitl1)## Call:
## phreg(formula = Surv(time = agew3, time2 = agew4, event = job_transition) ~
## sex + sexorient, data = addhealth, dist = "weibull", shape = 1)
##
## Covariate W.mean Coef Exp(Coef) se(Coef) Wald p
## sex
## a_male 0.477 0 1 (reference)
## b_female 0.523 -0.330 0.719 0.049 0.000
## sexorient
## a_straight 0.968 0 1 (reference)
## b_bisexual 0.016 0.516 1.676 0.159 0.001
## c_LGB 0.015 0.099 1.104 0.184 0.592
##
## log(scale) 3.002 0.033 0.000
##
## Shape is fixed at 1
##
## Events 1693
## Total time at risk 39477
## Max. log. likelihood -6998.9
## LR test statistic 51.48
## Degrees of freedom 3
## Overall p-value 3.8583e-11#weibull model
fitl1a<-phreg(Surv(time=agew3,time2 = agew4,event = job_transition)~sex+sexorient,data=addhealth,dist="weibull")
summary(fitl1a)## Call:
## phreg(formula = Surv(time = agew3, time2 = agew4, event = job_transition) ~
## sex + sexorient, data = addhealth, dist = "weibull")
##
## Covariate W.mean Coef Exp(Coef) se(Coef) Wald p
## sex
## a_male 0.477 0 1 (reference)
## b_female 0.523 -0.129 0.879 0.050 0.009
## sexorient
## a_straight 0.968 0 1 (reference)
## b_bisexual 0.016 0.658 1.931 0.159 0.000
## c_LGB 0.015 0.002 1.002 0.184 0.990
##
## log(scale) 3.442 0.002 0.000
## log(shape) 2.941 0.020 0.000
##
## Events 1693
## Total time at risk 39477
## Max. log. likelihood -5382.2
## LR test statistic 19.07
## Degrees of freedom 3
## Overall p-value 0.000264868#piecewise constant
fitl1b<-phreg(Surv(time=agew3,time2 = agew4,event = job_transition)~sex+sexorient,data=addhealth,dist="pch")
summary(fitl1b)## Call:
## phreg(formula = Surv(time = agew3, time2 = agew4, event = job_transition) ~
## sex + sexorient, data = addhealth, dist = "pch")
##
## Covariate W.mean Coef Exp(Coef) se(Coef) Wald p
## sex
## a_male 0.477 0 1 (reference)
## b_female 0.523 -0.330 0.719 0.049 0.000
## sexorient
## a_straight 0.968 0 1 (reference)
## b_bisexual 0.016 0.516 1.676 0.159 0.001
## c_LGB 0.015 0.099 1.104 0.184 0.592
##
##
## Events 1693
## Total time at risk 39477
## Max. log. likelihood -6998.9
## LR test statistic 51.48
## Degrees of freedom 3
## Overall p-value 3.8583e-11#AIC for exponential
-2*fitl1$loglik[2]+2*length(fitl1$coefficients)## [1] 14005.76#AIC for weibull
-2*fitl1a$loglik[2]+2*length(fitl1a$coefficients)## [1] 10774.36#AIC for piecewise constant
-2*fitl1b$loglik[2]+2*length(fitl1b$coefficients)## [1] 14003.76#plot parametric hazard models
fitl1c<-coxreg(Surv(time = agew3,time2 = agew4,event = job_transition)~sex+sexorient,data=addhealth)
check.dist(fitl1c,fitl1, main="Exponential")
check.dist(fitl1c,fitl1a,main = "Weibull")
check.dist(fitl1c,fitl1b,main="Piecewise Exponential")
e. Plot the hazard function for the analysis for each level of the group variable (continued assignment from part d)
fit.haz<-survfit(Surv(time = agew3,time2 = agew4,event = job_transition)~sexorient,data=addhealth)
ggsurvplot(fit.haz,data = addhealth,risk.table=T,fun="cumhaz",title="Hazard Function for Job Transition",xlim=c(20,35))
#Part B #Parametric models ### 1) Carry out the following analysis: Define your outcome as in part A. Also consider what covariates are hypothesized to affect the outcome variable. Define these and construct a parametric model for your outcome. Fit the parametric model of your choosing to the data. #### a. Did you choose an AFT or PH model and why? I chose a Cox Proportional Hazard Model to analyzing the effect of several covariates #### b. Justify what parametric distribution you choose #### c. Carry out model fit diagnostics for the model #### d. Include all main effects in the model #### e. Test for an interaction between at least two of the predictors #### f. Interpret your results and write them up
#job transition based on sexual orientation
fit<-survfit(Surv(time=agew3,time2 = agew4, event=job_transition)~sexorient,addhealth)
ggsurvplot(fit,data = addhealth,risk.table=T,title="Survivorship Function for LGB job retention",xlim=c(20,35))
#Survey design
library(survey)## Loading required package: grid## Loading required package: Matrix##
## Attaching package: 'survey'## The following object is masked from 'package:graphics':
##
## dotchartdes2<-svydesign(ids=~psuscid,
strata = ~region,
weights=~GSWGT4,
data=addhealth,
nest=T)fit.cox<-svycoxph(Surv(time=agew3,time2=agew4,event=job_transition)~sex+sexorient,design=des2)
summary(fit.cox)## Stratified 1 - level Cluster Sampling design (with replacement)
## With (130) clusters.
## svydesign(ids = ~psuscid, strata = ~region, weights = ~GSWGT4,
## data = addhealth, nest = T)## Call:
## svycoxph(formula = Surv(time = agew3, time2 = agew4, event = job_transition) ~
## sex + sexorient, design = des2)
##
## n= 6065, number of events= 1693
##
## coef exp(coef) se(coef) z Pr(>|z|)
## sexb_female -0.31092 0.73277 0.06242 -4.981 6.33e-07 ***
## sexorientb_bisexual 0.77158 2.16319 0.19879 3.881 0.000104 ***
## sexorientc_LGB -0.21242 0.80863 0.24960 -0.851 0.394752
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## sexb_female 0.7328 1.3647 0.6484 0.8281
## sexorientb_bisexual 2.1632 0.4623 1.4652 3.1938
## sexorientc_LGB 0.8086 1.2367 0.4958 1.3189
##
## Concordance= 0.552 (se = 0.009 )
## Likelihood ratio test= NA on 3 df, p=NA
## Wald test = 37.42 on 3 df, p=4e-08
## Score (logrank) test = NA on 3 df, p=NAplot(survfit(fit.cox,conf.int = F),ylab = "S(t)",xlab="Age",xlim=c(20,35))
#model with demographic variables
fit1a<-svycoxph(Surv(time=agew3,time2=agew4,event=job_transition)~sex+racethnic+sexorient,design=des2)
summary(fit1a)## Stratified 1 - level Cluster Sampling design (with replacement)
## With (130) clusters.
## svydesign(ids = ~psuscid, strata = ~region, weights = ~GSWGT4,
## data = addhealth, nest = T)## Call:
## svycoxph(formula = Surv(time = agew3, time2 = agew4, event = job_transition) ~
## sex + racethnic + sexorient, design = des2)
##
## n= 6065, number of events= 1693
##
## coef exp(coef) se(coef) z Pr(>|z|)
## sexb_female -0.31108 0.73266 0.06224 -4.998 5.78e-07 ***
## racethnicb-nhblack 0.19136 1.21090 0.11862 1.613 0.10669
## racethnicc-hispanic -0.03207 0.96844 0.16656 -0.193 0.84731
## racethnicd-asian -0.38944 0.67744 0.14946 -2.606 0.00917 **
## racethnice-native_american 0.40497 1.49925 0.35379 1.145 0.25236
## racethnicf-other 0.31728 1.37338 0.42782 0.742 0.45833
## sexorientb_bisexual 0.76723 2.15379 0.19952 3.845 0.00012 ***
## sexorientc_LGB -0.20571 0.81407 0.25074 -0.820 0.41197
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## sexb_female 0.7327 1.3649 0.6485 0.8277
## racethnicb-nhblack 1.2109 0.8258 0.9597 1.5278
## racethnicc-hispanic 0.9684 1.0326 0.6987 1.3423
## racethnicd-asian 0.6774 1.4761 0.5054 0.9080
## racethnice-native_american 1.4993 0.6670 0.7494 2.9993
## racethnicf-other 1.3734 0.7281 0.5938 3.1765
## sexorientb_bisexual 2.1538 0.4643 1.4567 3.1845
## sexorientc_LGB 0.8141 1.2284 0.4980 1.3307
##
## Concordance= 0.562 (se = 0.01 )
## Likelihood ratio test= NA on 8 df, p=NA
## Wald test = 47.36 on 8 df, p=1e-07
## Score (logrank) test = NA on 8 df, p=NA#model with demographic variables, plus social demographic variables
fit1b<-svycoxph(Surv(time=agew3,time2=agew4,event=job_transition)~sex+racethnic+sexorient+educ+incomeW4,design=des2)
summary(fit1b)## Stratified 1 - level Cluster Sampling design (with replacement)
## With (130) clusters.
## svydesign(ids = ~psuscid, strata = ~region, weights = ~GSWGT4,
## data = addhealth, nest = T)## Call:
## svycoxph(formula = Surv(time = agew3, time2 = agew4, event = job_transition) ~
## sex + racethnic + sexorient + educ + incomeW4, design = des2)
##
## n= 6065, number of events= 1693
##
## coef exp(coef) se(coef) z Pr(>|z|)
## sexb_female -0.26169 0.76975 0.06495 -4.029 5.60e-05
## racethnicb-nhblack 0.06211 1.06408 0.11363 0.547 0.584666
## racethnicc-hispanic -0.11656 0.88997 0.15910 -0.733 0.463769
## racethnicd-asian -0.18463 0.83141 0.13187 -1.400 0.161507
## racethnice-native_american 0.27289 1.31376 0.34374 0.794 0.427266
## racethnicf-other 0.31860 1.37521 0.40722 0.782 0.433992
## sexorientb_bisexual 0.64966 1.91489 0.18153 3.579 0.000345
## sexorientc_LGB -0.13175 0.87656 0.24318 -0.542 0.587977
## educb_highschool_grad -0.08864 0.91518 0.10142 -0.874 0.382139
## educc_college_bach -0.39819 0.67153 0.14167 -2.811 0.004942
## educd_college+ -1.35634 0.25760 0.20007 -6.779 1.21e-11
## incomeW4b_$15,000<$30,000 0.15776 1.17088 0.23769 0.664 0.506868
## incomeW4c_$30,000<$50,000 -0.14521 0.86484 0.23602 -0.615 0.538380
## incomeW4d_$50,000<$75,000 -0.42673 0.65264 0.24205 -1.763 0.077899
## incomeW4e_$75,000<$100,000 -0.48228 0.61737 0.23275 -2.072 0.038253
## incomeW4f_$100,000<$150,000 -0.66945 0.51199 0.26669 -2.510 0.012065
## incomeW4g_$150,000+ -0.58034 0.55971 0.28256 -2.054 0.039993
##
## sexb_female ***
## racethnicb-nhblack
## racethnicc-hispanic
## racethnicd-asian
## racethnice-native_american
## racethnicf-other
## sexorientb_bisexual ***
## sexorientc_LGB
## educb_highschool_grad
## educc_college_bach **
## educd_college+ ***
## incomeW4b_$15,000<$30,000
## incomeW4c_$30,000<$50,000
## incomeW4d_$50,000<$75,000 .
## incomeW4e_$75,000<$100,000 *
## incomeW4f_$100,000<$150,000 *
## incomeW4g_$150,000+ *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## sexb_female 0.7698 1.2991 0.6777 0.8743
## racethnicb-nhblack 1.0641 0.9398 0.8516 1.3295
## racethnicc-hispanic 0.8900 1.1236 0.6516 1.2156
## racethnicd-asian 0.8314 1.2028 0.6420 1.0766
## racethnice-native_american 1.3138 0.7612 0.6698 2.5770
## racethnicf-other 1.3752 0.7272 0.6191 3.0549
## sexorientb_bisexual 1.9149 0.5222 1.3416 2.7332
## sexorientc_LGB 0.8766 1.1408 0.5442 1.4118
## educb_highschool_grad 0.9152 1.0927 0.7502 1.1164
## educc_college_bach 0.6715 1.4891 0.5087 0.8864
## educd_college+ 0.2576 3.8820 0.1740 0.3813
## incomeW4b_$15,000<$30,000 1.1709 0.8541 0.7348 1.8657
## incomeW4c_$30,000<$50,000 0.8648 1.1563 0.5445 1.3735
## incomeW4d_$50,000<$75,000 0.6526 1.5322 0.4061 1.0488
## incomeW4e_$75,000<$100,000 0.6174 1.6198 0.3912 0.9742
## incomeW4f_$100,000<$150,000 0.5120 1.9532 0.3036 0.8635
## incomeW4g_$150,000+ 0.5597 1.7866 0.3217 0.9738
##
## Concordance= 0.638 (se = 0.01 )
## Likelihood ratio test= NA on 17 df, p=NA
## Wald test = 206.3 on 17 df, p=<2e-16
## Score (logrank) test = NA on 17 df, p=NA#model with demographic variables, plus social demographic variables, plus family circumstances,
fit1c<-svycoxph(Surv(time=agew3,time2=agew4,event=job_transition)~sex+racethnic+sexorient+educ+incomeW4+pregnancy+births+foster_home,design=des2)
summary(fit1c)## Stratified 1 - level Cluster Sampling design (with replacement)
## With (130) clusters.
## svydesign(ids = ~psuscid, strata = ~region, weights = ~GSWGT4,
## data = addhealth, nest = T)## Call:
## svycoxph(formula = Surv(time = agew3, time2 = agew4, event = job_transition) ~
## sex + racethnic + sexorient + educ + incomeW4 + pregnancy +
## births + foster_home, design = des2)
##
## n= 6065, number of events= 1693
##
## coef exp(coef) se(coef) z Pr(>|z|)
## sexb_female -0.18015 0.83514 0.06605 -2.728 0.006379
## racethnicb-nhblack 0.09293 1.09738 0.11317 0.821 0.411569
## racethnicc-hispanic -0.12452 0.88292 0.16757 -0.743 0.457417
## racethnicd-asian -0.25879 0.77199 0.13352 -1.938 0.052598
## racethnice-native_american 0.23833 1.26913 0.37477 0.636 0.524826
## racethnicf-other 0.33342 1.39573 0.40009 0.833 0.404644
## sexorientb_bisexual 0.58657 1.79781 0.17626 3.328 0.000875
## sexorientc_LGB -0.31139 0.73243 0.24352 -1.279 0.201010
## educb_highschool_grad -0.17313 0.84102 0.09811 -1.765 0.077615
## educc_college_bach -0.61664 0.53975 0.14020 -4.398 1.09e-05
## educd_college+ -1.59084 0.20375 0.20383 -7.805 5.97e-15
## incomeW4b_$15,000<$30,000 0.09128 1.09557 0.23931 0.381 0.702884
## incomeW4c_$30,000<$50,000 -0.19792 0.82043 0.23904 -0.828 0.407679
## incomeW4d_$50,000<$75,000 -0.48579 0.61521 0.24508 -1.982 0.047458
## incomeW4e_$75,000<$100,000 -0.54756 0.57836 0.23180 -2.362 0.018168
## incomeW4f_$100,000<$150,000 -0.76776 0.46405 0.26754 -2.870 0.004109
## incomeW4g_$150,000+ -0.69597 0.49859 0.27981 -2.487 0.012872
## pregnancyb_yes 0.02218 1.02243 0.09782 0.227 0.820630
## birthsb_yes -0.45580 0.63394 0.10912 -4.177 2.95e-05
## birthsskip 0.01741 1.01756 0.10545 0.165 0.868872
## foster_homeb_yes -0.20105 0.81787 0.23936 -0.840 0.400926
##
## sexb_female **
## racethnicb-nhblack
## racethnicc-hispanic
## racethnicd-asian .
## racethnice-native_american
## racethnicf-other
## sexorientb_bisexual ***
## sexorientc_LGB
## educb_highschool_grad .
## educc_college_bach ***
## educd_college+ ***
## incomeW4b_$15,000<$30,000
## incomeW4c_$30,000<$50,000
## incomeW4d_$50,000<$75,000 *
## incomeW4e_$75,000<$100,000 *
## incomeW4f_$100,000<$150,000 **
## incomeW4g_$150,000+ *
## pregnancyb_yes
## birthsb_yes ***
## birthsskip
## foster_homeb_yes
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## sexb_female 0.8351 1.1974 0.7337 0.9506
## racethnicb-nhblack 1.0974 0.9113 0.8791 1.3699
## racethnicc-hispanic 0.8829 1.1326 0.6358 1.2262
## racethnicd-asian 0.7720 1.2954 0.5942 1.0029
## racethnice-native_american 1.2691 0.7879 0.6088 2.6455
## racethnicf-other 1.3957 0.7165 0.6372 3.0575
## sexorientb_bisexual 1.7978 0.5562 1.2727 2.5397
## sexorientc_LGB 0.7324 1.3653 0.4544 1.1805
## educb_highschool_grad 0.8410 1.1890 0.6939 1.0193
## educc_college_bach 0.5398 1.8527 0.4101 0.7104
## educd_college+ 0.2038 4.9079 0.1366 0.3038
## incomeW4b_$15,000<$30,000 1.0956 0.9128 0.6854 1.7512
## incomeW4c_$30,000<$50,000 0.8204 1.2189 0.5135 1.3107
## incomeW4d_$50,000<$75,000 0.6152 1.6255 0.3806 0.9946
## incomeW4e_$75,000<$100,000 0.5784 1.7290 0.3672 0.9110
## incomeW4f_$100,000<$150,000 0.4641 2.1549 0.2747 0.7840
## incomeW4g_$150,000+ 0.4986 2.0057 0.2881 0.8628
## pregnancyb_yes 1.0224 0.9781 0.8440 1.2385
## birthsb_yes 0.6339 1.5774 0.5119 0.7851
## birthsskip 1.0176 0.9827 0.8276 1.2512
## foster_homeb_yes 0.8179 1.2227 0.5116 1.3075
##
## Concordance= 0.654 (se = 0.01 )
## Likelihood ratio test= NA on 21 df, p=NA
## Wald test = 318.8 on 21 df, p=<2e-16
## Score (logrank) test = NA on 21 df, p=NA#model with demographic variables, plus social demographic variables, plus family circumstances, plus structural bias variables
fit1d<-svycoxph(Surv(time=agew3,time2=agew4,event=job_transition)~sex+racethnic+sexorient+educ+incomeW4+pregnancy+births+foster_home+marriedW4a+marriedW4b+insurance_statusW4+unmet_medcareW4+incarcerated,design=des2)
summary(fit1d)## Stratified 1 - level Cluster Sampling design (with replacement)
## With (130) clusters.
## svydesign(ids = ~psuscid, strata = ~region, weights = ~GSWGT4,
## data = addhealth, nest = T)## Call:
## svycoxph(formula = Surv(time = agew3, time2 = agew4, event = job_transition) ~
## sex + racethnic + sexorient + educ + incomeW4 + pregnancy +
## births + foster_home + marriedW4a + marriedW4b + insurance_statusW4 +
## unmet_medcareW4 + incarcerated, design = des2)
##
## n= 6065, number of events= 1693
##
## coef exp(coef) se(coef) z
## sexb_female -0.055512 0.946001 0.066519 -0.835
## racethnicb-nhblack 0.043374 1.044328 0.112033 0.387
## racethnicc-hispanic -0.114436 0.891869 0.159874 -0.716
## racethnicd-asian -0.253826 0.775826 0.133622 -1.900
## racethnice-native_american 0.267135 1.306217 0.348141 0.767
## racethnicf-other 0.233797 1.263388 0.427399 0.547
## sexorientb_bisexual 0.515939 1.675211 0.189714 2.720
## sexorientc_LGB -0.423826 0.654538 0.250698 -1.691
## educb_highschool_grad -0.054467 0.946990 0.108777 -0.501
## educc_college_bach -0.433585 0.648181 0.149685 -2.897
## educd_college+ -1.352682 0.258546 0.206849 -6.539
## incomeW4b_$15,000<$30,000 0.048464 1.049658 0.238873 0.203
## incomeW4c_$30,000<$50,000 -0.145553 0.864544 0.238849 -0.609
## incomeW4d_$50,000<$75,000 -0.364665 0.694429 0.245620 -1.485
## incomeW4e_$75,000<$100,000 -0.355227 0.701014 0.236906 -1.499
## incomeW4f_$100,000<$150,000 -0.608297 0.544277 0.271021 -2.244
## incomeW4g_$150,000+ -0.561173 0.570539 0.286475 -1.959
## pregnancyb_yes -0.128192 0.879684 0.100108 -1.281
## birthsb_yes -0.338741 0.712667 0.116344 -2.912
## birthsskip -0.028690 0.971718 0.110036 -0.261
## foster_homeb_yes -0.292761 0.746200 0.234770 -1.247
## marriedW4ab_married 0.309016 1.362084 0.135823 2.275
## marriedW4bb_seperated/divorced 0.007701 1.007731 0.192431 0.040
## marriedW4bc_skip 0.092757 1.097195 0.137411 0.675
## insurance_statusW4b_yes_insurance -0.225975 0.797738 0.077444 -2.918
## unmet_medcareW4b_yes 0.096451 1.101255 0.073721 1.308
## incarceratedb_yes -0.002233 0.997770 0.105609 -0.021
## incarceratedskip -0.269455 0.763796 0.089524 -3.010
## Pr(>|z|)
## sexb_female 0.40398
## racethnicb-nhblack 0.69865
## racethnicc-hispanic 0.47412
## racethnicd-asian 0.05749 .
## racethnice-native_american 0.44289
## racethnicf-other 0.58436
## sexorientb_bisexual 0.00654 **
## sexorientc_LGB 0.09092 .
## educb_highschool_grad 0.61657
## educc_college_bach 0.00377 **
## educd_college+ 6.17e-11 ***
## incomeW4b_$15,000<$30,000 0.83922
## incomeW4c_$30,000<$50,000 0.54226
## incomeW4d_$50,000<$75,000 0.13763
## incomeW4e_$75,000<$100,000 0.13376
## incomeW4f_$100,000<$150,000 0.02480 *
## incomeW4g_$150,000+ 0.05013 .
## pregnancyb_yes 0.20036
## birthsb_yes 0.00360 **
## birthsskip 0.79430
## foster_homeb_yes 0.21239
## marriedW4ab_married 0.02290 *
## marriedW4bb_seperated/divorced 0.96808
## marriedW4bc_skip 0.49965
## insurance_statusW4b_yes_insurance 0.00352 **
## unmet_medcareW4b_yes 0.19076
## incarceratedb_yes 0.98313
## incarceratedskip 0.00261 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## sexb_female 0.9460 1.0571 0.8304 1.0777
## racethnicb-nhblack 1.0443 0.9576 0.8384 1.3008
## racethnicc-hispanic 0.8919 1.1212 0.6520 1.2201
## racethnicd-asian 0.7758 1.2889 0.5971 1.0081
## racethnice-native_american 1.3062 0.7656 0.6602 2.5844
## racethnicf-other 1.2634 0.7915 0.5467 2.9197
## sexorientb_bisexual 1.6752 0.5969 1.1550 2.4297
## sexorientc_LGB 0.6545 1.5278 0.4004 1.0699
## educb_highschool_grad 0.9470 1.0560 0.7652 1.1720
## educc_college_bach 0.6482 1.5428 0.4834 0.8692
## educd_college+ 0.2585 3.8678 0.1724 0.3878
## incomeW4b_$15,000<$30,000 1.0497 0.9527 0.6572 1.6764
## incomeW4c_$30,000<$50,000 0.8645 1.1567 0.5414 1.3807
## incomeW4d_$50,000<$75,000 0.6944 1.4400 0.4291 1.1238
## incomeW4e_$75,000<$100,000 0.7010 1.4265 0.4406 1.1153
## incomeW4f_$100,000<$150,000 0.5443 1.8373 0.3200 0.9258
## incomeW4g_$150,000+ 0.5705 1.7527 0.3254 1.0003
## pregnancyb_yes 0.8797 1.1368 0.7230 1.0704
## birthsb_yes 0.7127 1.4032 0.5674 0.8952
## birthsskip 0.9717 1.0291 0.7832 1.2056
## foster_homeb_yes 0.7462 1.3401 0.4710 1.1822
## marriedW4ab_married 1.3621 0.7342 1.0437 1.7775
## marriedW4bb_seperated/divorced 1.0077 0.9923 0.6911 1.4694
## marriedW4bc_skip 1.0972 0.9114 0.8381 1.4363
## insurance_statusW4b_yes_insurance 0.7977 1.2535 0.6854 0.9285
## unmet_medcareW4b_yes 1.1013 0.9081 0.9531 1.2724
## incarceratedb_yes 0.9978 1.0022 0.8112 1.2272
## incarceratedskip 0.7638 1.3093 0.6409 0.9103
##
## Concordance= 0.682 (se = 0.009 )
## Likelihood ratio test= NA on 28 df, p=NA
## Wald test = 415.9 on 28 df, p=<2e-16
## Score (logrank) test = NA on 28 df, p=NA#model with demographic variables, plus social demographic variables, plus family circumstances, plus structural bias variables, plus health variables,
fit1e<-svycoxph(Surv(time=agew3,time2=agew4,event=job_transition)~sex+racethnic+sexorient+educ+incomeW4+pregnancy+births+foster_home+marriedW4a+marriedW4b+insurance_statusW4+unmet_medcareW4+incarcerated+general_health+social_isolationW4+things.going.mywayW4+depressionW4+suicideW4think+physical_limitation+cancer+high_bp+high_chol+diabetes+heart_disease+asthm+migrane+HIV,design=des2)
summary(fit1e)## Stratified 1 - level Cluster Sampling design (with replacement)
## With (130) clusters.
## svydesign(ids = ~psuscid, strata = ~region, weights = ~GSWGT4,
## data = addhealth, nest = T)## Call:
## svycoxph(formula = Surv(time = agew3, time2 = agew4, event = job_transition) ~
## sex + racethnic + sexorient + educ + incomeW4 + pregnancy +
## births + foster_home + marriedW4a + marriedW4b + insurance_statusW4 +
## unmet_medcareW4 + incarcerated + general_health + social_isolationW4 +
## things.going.mywayW4 + depressionW4 + suicideW4think +
## physical_limitation + cancer + high_bp + high_chol +
## diabetes + heart_disease + asthm + migrane + HIV, design = des2)
##
## n= 6065, number of events= 1693
##
## coef exp(coef) se(coef) z
## sexb_female -0.113209 0.892964 0.072565 -1.560
## racethnicb-nhblack 0.065711 1.067918 0.109879 0.598
## racethnicc-hispanic -0.106173 0.899269 0.159924 -0.664
## racethnicd-asian -0.227081 0.796856 0.135922 -1.671
## racethnice-native_american 0.255670 1.291326 0.368383 0.694
## racethnicf-other 0.172826 1.188659 0.428207 0.404
## sexorientb_bisexual 0.469650 1.599434 0.199968 2.349
## sexorientc_LGB -0.485992 0.615086 0.245567 -1.979
## educb_highschool_grad -0.044804 0.956185 0.106897 -0.419
## educc_college_bach -0.398449 0.671360 0.151233 -2.635
## educd_college+ -1.310448 0.269699 0.205313 -6.383
## incomeW4b_$15,000<$30,000 0.093884 1.098432 0.227179 0.413
## incomeW4c_$30,000<$50,000 -0.106907 0.898609 0.223497 -0.478
## incomeW4d_$50,000<$75,000 -0.324193 0.723110 0.231500 -1.400
## incomeW4e_$75,000<$100,000 -0.302302 0.739115 0.224422 -1.347
## incomeW4f_$100,000<$150,000 -0.525941 0.590999 0.256325 -2.052
## incomeW4g_$150,000+ -0.491393 0.611773 0.270483 -1.817
## pregnancyb_yes -0.149090 0.861492 0.101588 -1.468
## birthsb_yes -0.314916 0.729850 0.118450 -2.659
## birthsskip -0.006176 0.993843 0.110532 -0.056
## foster_homeb_yes -0.316110 0.728980 0.231011 -1.368
## marriedW4ab_married 0.337446 1.401365 0.133201 2.533
## marriedW4bb_seperated/divorced -0.033783 0.966782 0.193598 -0.174
## marriedW4bc_skip 0.055590 1.057164 0.135694 0.410
## insurance_statusW4b_yes_insurance -0.214159 0.807220 0.081073 -2.642
## unmet_medcareW4b_yes 0.045797 1.046862 0.078037 0.587
## incarceratedb_yes -0.012659 0.987421 0.101366 -0.125
## incarceratedskip -0.281066 0.754978 0.087955 -3.196
## general_healthb_poor/bad -0.052885 0.948489 0.115822 -0.457
## social_isolationW4b_sometimes/often 0.049389 1.050629 0.068078 0.725
## things.going.mywayW4b_sometimes -0.173836 0.840435 0.104452 -1.664
## things.going.mywayW4c_often -0.258579 0.772148 0.108510 -2.383
## depressionW4b_yes 0.057417 1.059097 0.094699 0.606
## suicideW4thinkb_yes 0.013442 1.013532 0.109607 0.123
## physical_limitationb_limited 0.186552 1.205087 0.124455 1.499
## cancerb_yes 0.368466 1.445515 0.273108 1.349
## high_bpb_yes 0.021540 1.021774 0.114493 0.188
## high_cholb_yes 0.036325 1.036993 0.101376 0.358
## diabetesb_yes -0.088387 0.915406 0.202042 -0.437
## heart_diseaseb_yes -0.042183 0.958694 0.258643 -0.163
## asthmb_yes 0.201559 1.223309 0.081770 2.465
## migraneb_yes 0.173226 1.189135 0.098193 1.764
## HIVb_yes 0.619510 1.858018 0.760346 0.815
## Pr(>|z|)
## sexb_female 0.11874
## racethnicb-nhblack 0.54982
## racethnicc-hispanic 0.50676
## racethnicd-asian 0.09479 .
## racethnice-native_american 0.48766
## racethnicf-other 0.68650
## sexorientb_bisexual 0.01884 *
## sexorientc_LGB 0.04781 *
## educb_highschool_grad 0.67512
## educc_college_bach 0.00842 **
## educd_college+ 1.74e-10 ***
## incomeW4b_$15,000<$30,000 0.67942
## incomeW4c_$30,000<$50,000 0.63241
## incomeW4d_$50,000<$75,000 0.16139
## incomeW4e_$75,000<$100,000 0.17797
## incomeW4f_$100,000<$150,000 0.04018 *
## incomeW4g_$150,000+ 0.06926 .
## pregnancyb_yes 0.14221
## birthsb_yes 0.00785 **
## birthsskip 0.95544
## foster_homeb_yes 0.17119
## marriedW4ab_married 0.01130 *
## marriedW4bb_seperated/divorced 0.86147
## marriedW4bc_skip 0.68205
## insurance_statusW4b_yes_insurance 0.00825 **
## unmet_medcareW4b_yes 0.55729
## incarceratedb_yes 0.90062
## incarceratedskip 0.00140 **
## general_healthb_poor/bad 0.64796
## social_isolationW4b_sometimes/often 0.46816
## things.going.mywayW4b_sometimes 0.09606 .
## things.going.mywayW4c_often 0.01717 *
## depressionW4b_yes 0.54431
## suicideW4thinkb_yes 0.90240
## physical_limitationb_limited 0.13389
## cancerb_yes 0.17729
## high_bpb_yes 0.85077
## high_cholb_yes 0.72010
## diabetesb_yes 0.66177
## heart_diseaseb_yes 0.87044
## asthmb_yes 0.01370 *
## migraneb_yes 0.07771 .
## HIVb_yes 0.41520
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95
## sexb_female 0.8930 1.1199 0.7746
## racethnicb-nhblack 1.0679 0.9364 0.8610
## racethnicc-hispanic 0.8993 1.1120 0.6573
## racethnicd-asian 0.7969 1.2549 0.6105
## racethnice-native_american 1.2913 0.7744 0.6273
## racethnicf-other 1.1887 0.8413 0.5135
## sexorientb_bisexual 1.5994 0.6252 1.0808
## sexorientc_LGB 0.6151 1.6258 0.3801
## educb_highschool_grad 0.9562 1.0458 0.7754
## educc_college_bach 0.6714 1.4895 0.4991
## educd_college+ 0.2697 3.7078 0.1804
## incomeW4b_$15,000<$30,000 1.0984 0.9104 0.7037
## incomeW4c_$30,000<$50,000 0.8986 1.1128 0.5799
## incomeW4d_$50,000<$75,000 0.7231 1.3829 0.4594
## incomeW4e_$75,000<$100,000 0.7391 1.3530 0.4761
## incomeW4f_$100,000<$150,000 0.5910 1.6921 0.3576
## incomeW4g_$150,000+ 0.6118 1.6346 0.3600
## pregnancyb_yes 0.8615 1.1608 0.7060
## birthsb_yes 0.7299 1.3701 0.5786
## birthsskip 0.9938 1.0062 0.8003
## foster_homeb_yes 0.7290 1.3718 0.4635
## marriedW4ab_married 1.4014 0.7136 1.0794
## marriedW4bb_seperated/divorced 0.9668 1.0344 0.6615
## marriedW4bc_skip 1.0572 0.9459 0.8103
## insurance_statusW4b_yes_insurance 0.8072 1.2388 0.6886
## unmet_medcareW4b_yes 1.0469 0.9552 0.8984
## incarceratedb_yes 0.9874 1.0127 0.8095
## incarceratedskip 0.7550 1.3245 0.6354
## general_healthb_poor/bad 0.9485 1.0543 0.7559
## social_isolationW4b_sometimes/often 1.0506 0.9518 0.9194
## things.going.mywayW4b_sometimes 0.8404 1.1899 0.6848
## things.going.mywayW4c_often 0.7721 1.2951 0.6242
## depressionW4b_yes 1.0591 0.9442 0.8797
## suicideW4thinkb_yes 1.0135 0.9866 0.8176
## physical_limitationb_limited 1.2051 0.8298 0.9442
## cancerb_yes 1.4455 0.6918 0.8464
## high_bpb_yes 1.0218 0.9787 0.8164
## high_cholb_yes 1.0370 0.9643 0.8501
## diabetesb_yes 0.9154 1.0924 0.6161
## heart_diseaseb_yes 0.9587 1.0431 0.5775
## asthmb_yes 1.2233 0.8175 1.0422
## migraneb_yes 1.1891 0.8409 0.9810
## HIVb_yes 1.8580 0.5382 0.4186
## upper .95
## sexb_female 1.0294
## racethnicb-nhblack 1.3245
## racethnicc-hispanic 1.2303
## racethnicd-asian 1.0401
## racethnice-native_american 2.6583
## racethnicf-other 2.7514
## sexorientb_bisexual 2.3669
## sexorientc_LGB 0.9953
## educb_highschool_grad 1.1791
## educc_college_bach 0.9030
## educd_college+ 0.4033
## incomeW4b_$15,000<$30,000 1.7145
## incomeW4c_$30,000<$50,000 1.3926
## incomeW4d_$50,000<$75,000 1.1383
## incomeW4e_$75,000<$100,000 1.1475
## incomeW4f_$100,000<$150,000 0.9767
## incomeW4g_$150,000+ 1.0395
## pregnancyb_yes 1.0513
## birthsb_yes 0.9206
## birthsskip 1.2342
## foster_homeb_yes 1.1464
## marriedW4ab_married 1.8194
## marriedW4bb_seperated/divorced 1.4129
## marriedW4bc_skip 1.3793
## insurance_statusW4b_yes_insurance 0.9462
## unmet_medcareW4b_yes 1.2199
## incarceratedb_yes 1.2044
## incarceratedskip 0.8970
## general_healthb_poor/bad 1.1902
## social_isolationW4b_sometimes/often 1.2006
## things.going.mywayW4b_sometimes 1.0314
## things.going.mywayW4c_often 0.9551
## depressionW4b_yes 1.2751
## suicideW4thinkb_yes 1.2564
## physical_limitationb_limited 1.5380
## cancerb_yes 2.4688
## high_bpb_yes 1.2788
## high_cholb_yes 1.2649
## diabetesb_yes 1.3602
## heart_diseaseb_yes 1.5916
## asthmb_yes 1.4359
## migraneb_yes 1.4415
## HIVb_yes 8.2463
##
## Concordance= 0.689 (se = 0.009 )
## Likelihood ratio test= NA on 43 df, p=NA
## Wald test = 573.6 on 43 df, p=<2e-16
## Score (logrank) test = NA on 43 df, p=NA#model with demographic variables, plus social demographic variables, plus family circumstances, plus structural bias variables, plus health variables, plus discrimination/victimization
fit1f<-svycoxph(Surv(time=agew3,time2=agew4,event=job_transition)~sex+racethnic+sexorient+educ+incomeW4+pregnancy+births+foster_home+marriedW4a+marriedW4b+insurance_statusW4+unmet_medcareW4+incarcerated+general_health+social_isolationW4+things.going.mywayW4+depressionW4+suicideW4think+physical_limitation+cancer+high_bp+high_chol+diabetes+heart_disease+asthm+migrane+HIV+threatened_gun_knifeW4+forcedsex_physicalW4+beaten_upW4+sex_abuse_bycaretakerW4,design=des2)
summary(fit1f)## Stratified 1 - level Cluster Sampling design (with replacement)
## With (130) clusters.
## svydesign(ids = ~psuscid, strata = ~region, weights = ~GSWGT4,
## data = addhealth, nest = T)## Call:
## svycoxph(formula = Surv(time = agew3, time2 = agew4, event = job_transition) ~
## sex + racethnic + sexorient + educ + incomeW4 + pregnancy +
## births + foster_home + marriedW4a + marriedW4b + insurance_statusW4 +
## unmet_medcareW4 + incarcerated + general_health + social_isolationW4 +
## things.going.mywayW4 + depressionW4 + suicideW4think +
## physical_limitation + cancer + high_bp + high_chol +
## diabetes + heart_disease + asthm + migrane + HIV + threatened_gun_knifeW4 +
## forcedsex_physicalW4 + beaten_upW4 + sex_abuse_bycaretakerW4,
## design = des2)
##
## n= 6065, number of events= 1693
##
## coef exp(coef) se(coef) z
## sexb_female -0.139113 0.870129 0.071060 -1.958
## racethnicb-nhblack 0.072408 1.075094 0.110433 0.656
## racethnicc-hispanic -0.101146 0.903801 0.159193 -0.635
## racethnicd-asian -0.223741 0.799522 0.135018 -1.657
## racethnice-native_american 0.245037 1.277668 0.377602 0.649
## racethnicf-other 0.173067 1.188945 0.429645 0.403
## sexorientb_bisexual 0.444968 1.560441 0.201915 2.204
## sexorientc_LGB -0.500335 0.606327 0.247421 -2.022
## educb_highschool_grad -0.042432 0.958456 0.108187 -0.392
## educc_college_bach -0.393251 0.674860 0.152439 -2.580
## educd_college+ -1.304080 0.271422 0.206033 -6.329
## incomeW4b_$15,000<$30,000 0.085906 1.089703 0.228396 0.376
## incomeW4c_$30,000<$50,000 -0.110586 0.895309 0.223946 -0.494
## incomeW4d_$50,000<$75,000 -0.330238 0.718753 0.231699 -1.425
## incomeW4e_$75,000<$100,000 -0.307812 0.735054 0.224923 -1.369
## incomeW4f_$100,000<$150,000 -0.532172 0.587328 0.258108 -2.062
## incomeW4g_$150,000+ -0.501386 0.605690 0.270928 -1.851
## pregnancyb_yes -0.161721 0.850678 0.102227 -1.582
## birthsb_yes -0.318517 0.727226 0.120314 -2.647
## birthsskip -0.011160 0.988902 0.111518 -0.100
## foster_homeb_yes -0.384589 0.680730 0.253762 -1.516
## marriedW4ab_married 0.344118 1.410745 0.132529 2.597
## marriedW4bb_seperated/divorced -0.036646 0.964017 0.191736 -0.191
## marriedW4bc_skip 0.053402 1.054854 0.135591 0.394
## insurance_statusW4b_yes_insurance -0.218108 0.804039 0.081517 -2.676
## unmet_medcareW4b_yes 0.043725 1.044696 0.077720 0.563
## incarceratedb_yes -0.013665 0.986428 0.101778 -0.134
## incarceratedskip -0.275591 0.759123 0.086872 -3.172
## general_healthb_poor/bad -0.053113 0.948273 0.116461 -0.456
## social_isolationW4b_sometimes/often 0.044614 1.045624 0.068454 0.652
## things.going.mywayW4b_sometimes -0.173651 0.840590 0.106738 -1.627
## things.going.mywayW4c_often -0.256672 0.773622 0.108726 -2.361
## depressionW4b_yes 0.046187 1.047270 0.097205 0.475
## suicideW4thinkb_yes 0.006608 1.006630 0.108634 0.061
## physical_limitationb_limited 0.172787 1.188613 0.126604 1.365
## cancerb_yes 0.362054 1.436276 0.272090 1.331
## high_bpb_yes 0.019421 1.019611 0.117472 0.165
## high_cholb_yes 0.038627 1.039383 0.101182 0.382
## diabetesb_yes -0.086581 0.917061 0.204061 -0.424
## heart_diseaseb_yes -0.061872 0.940004 0.257714 -0.240
## asthmb_yes 0.197250 1.218048 0.081355 2.425
## migraneb_yes 0.168502 1.183530 0.098743 1.706
## HIVb_yes 0.577934 1.782353 0.731429 0.790
## threatened_gun_knifeW4b_yes -0.085341 0.918199 0.162412 -0.525
## forcedsex_physicalW4b_yes 0.120726 1.128316 0.107670 1.121
## beaten_upW4b_yes 0.086408 1.090251 0.162726 0.531
## sex_abuse_bycaretakerW4b_>10_times 0.043489 1.044448 0.156387 0.278
## sex_abuse_bycaretakerW4c_10+_times 0.083480 1.087064 0.240808 0.347
## Pr(>|z|)
## sexb_female 0.05027 .
## racethnicb-nhblack 0.51203
## racethnicc-hispanic 0.52519
## racethnicd-asian 0.09749 .
## racethnice-native_american 0.51638
## racethnicf-other 0.68709
## sexorientb_bisexual 0.02754 *
## sexorientc_LGB 0.04316 *
## educb_highschool_grad 0.69491
## educc_college_bach 0.00989 **
## educd_college+ 2.46e-10 ***
## incomeW4b_$15,000<$30,000 0.70682
## incomeW4c_$30,000<$50,000 0.62144
## incomeW4d_$50,000<$75,000 0.15407
## incomeW4e_$75,000<$100,000 0.17115
## incomeW4f_$100,000<$150,000 0.03922 *
## incomeW4g_$150,000+ 0.06422 .
## pregnancyb_yes 0.11365
## birthsb_yes 0.00811 **
## birthsskip 0.92029
## foster_homeb_yes 0.12963
## marriedW4ab_married 0.00942 **
## marriedW4bb_seperated/divorced 0.84842
## marriedW4bc_skip 0.69369
## insurance_statusW4b_yes_insurance 0.00746 **
## unmet_medcareW4b_yes 0.57371
## incarceratedb_yes 0.89320
## incarceratedskip 0.00151 **
## general_healthb_poor/bad 0.64835
## social_isolationW4b_sometimes/often 0.51457
## things.going.mywayW4b_sometimes 0.10376
## things.going.mywayW4c_often 0.01824 *
## depressionW4b_yes 0.63468
## suicideW4thinkb_yes 0.95149
## physical_limitationb_limited 0.17232
## cancerb_yes 0.18331
## high_bpb_yes 0.86869
## high_cholb_yes 0.70264
## diabetesb_yes 0.67135
## heart_diseaseb_yes 0.81027
## asthmb_yes 0.01533 *
## migraneb_yes 0.08792 .
## HIVb_yes 0.42944
## threatened_gun_knifeW4b_yes 0.59926
## forcedsex_physicalW4b_yes 0.26218
## beaten_upW4b_yes 0.59542
## sex_abuse_bycaretakerW4b_>10_times 0.78095
## sex_abuse_bycaretakerW4c_10+_times 0.72884
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95
## sexb_female 0.8701 1.1493 0.7570
## racethnicb-nhblack 1.0751 0.9302 0.8659
## racethnicc-hispanic 0.9038 1.1064 0.6616
## racethnicd-asian 0.7995 1.2507 0.6136
## racethnice-native_american 1.2777 0.7827 0.6095
## racethnicf-other 1.1889 0.8411 0.5122
## sexorientb_bisexual 1.5604 0.6408 1.0505
## sexorientc_LGB 0.6063 1.6493 0.3733
## educb_highschool_grad 0.9585 1.0433 0.7753
## educc_college_bach 0.6749 1.4818 0.5006
## educd_college+ 0.2714 3.6843 0.1812
## incomeW4b_$15,000<$30,000 1.0897 0.9177 0.6965
## incomeW4c_$30,000<$50,000 0.8953 1.1169 0.5772
## incomeW4d_$50,000<$75,000 0.7188 1.3913 0.4564
## incomeW4e_$75,000<$100,000 0.7351 1.3604 0.4730
## incomeW4f_$100,000<$150,000 0.5873 1.7026 0.3541
## incomeW4g_$150,000+ 0.6057 1.6510 0.3562
## pregnancyb_yes 0.8507 1.1755 0.6962
## birthsb_yes 0.7272 1.3751 0.5745
## birthsskip 0.9889 1.0112 0.7947
## foster_homeb_yes 0.6807 1.4690 0.4140
## marriedW4ab_married 1.4107 0.7088 1.0880
## marriedW4bb_seperated/divorced 0.9640 1.0373 0.6620
## marriedW4bc_skip 1.0549 0.9480 0.8087
## insurance_statusW4b_yes_insurance 0.8040 1.2437 0.6853
## unmet_medcareW4b_yes 1.0447 0.9572 0.8971
## incarceratedb_yes 0.9864 1.0138 0.8080
## incarceratedskip 0.7591 1.3173 0.6403
## general_healthb_poor/bad 0.9483 1.0545 0.7547
## social_isolationW4b_sometimes/often 1.0456 0.9564 0.9143
## things.going.mywayW4b_sometimes 0.8406 1.1896 0.6819
## things.going.mywayW4c_often 0.7736 1.2926 0.6251
## depressionW4b_yes 1.0473 0.9549 0.8656
## suicideW4thinkb_yes 1.0066 0.9934 0.8136
## physical_limitationb_limited 1.1886 0.8413 0.9274
## cancerb_yes 1.4363 0.6962 0.8426
## high_bpb_yes 1.0196 0.9808 0.8099
## high_cholb_yes 1.0394 0.9621 0.8524
## diabetesb_yes 0.9171 1.0904 0.6148
## heart_diseaseb_yes 0.9400 1.0638 0.5672
## asthmb_yes 1.2180 0.8210 1.0385
## migraneb_yes 1.1835 0.8449 0.9753
## HIVb_yes 1.7824 0.5611 0.4250
## threatened_gun_knifeW4b_yes 0.9182 1.0891 0.6679
## forcedsex_physicalW4b_yes 1.1283 0.8863 0.9137
## beaten_upW4b_yes 1.0903 0.9172 0.7925
## sex_abuse_bycaretakerW4b_>10_times 1.0444 0.9574 0.7687
## sex_abuse_bycaretakerW4c_10+_times 1.0871 0.9199 0.6781
## upper .95
## sexb_female 1.0002
## racethnicb-nhblack 1.3349
## racethnicc-hispanic 1.2347
## racethnicd-asian 1.0417
## racethnice-native_american 2.6781
## racethnicf-other 2.7598
## sexorientb_bisexual 2.3180
## sexorientc_LGB 0.9847
## educb_highschool_grad 1.1848
## educc_college_bach 0.9098
## educd_college+ 0.4065
## incomeW4b_$15,000<$30,000 1.7050
## incomeW4c_$30,000<$50,000 1.3887
## incomeW4d_$50,000<$75,000 1.1319
## incomeW4e_$75,000<$100,000 1.1423
## incomeW4f_$100,000<$150,000 0.9741
## incomeW4g_$150,000+ 1.0301
## pregnancyb_yes 1.0394
## birthsb_yes 0.9206
## birthsskip 1.2305
## foster_homeb_yes 1.1194
## marriedW4ab_married 1.8292
## marriedW4bb_seperated/divorced 1.4038
## marriedW4bc_skip 1.3760
## insurance_statusW4b_yes_insurance 0.9433
## unmet_medcareW4b_yes 1.2166
## incarceratedb_yes 1.2042
## incarceratedskip 0.9000
## general_healthb_poor/bad 1.1914
## social_isolationW4b_sometimes/often 1.1958
## things.going.mywayW4b_sometimes 1.0362
## things.going.mywayW4c_often 0.9574
## depressionW4b_yes 1.2671
## suicideW4thinkb_yes 1.2455
## physical_limitationb_limited 1.5234
## cancerb_yes 2.4482
## high_bpb_yes 1.2836
## high_cholb_yes 1.2674
## diabetesb_yes 1.3680
## heart_diseaseb_yes 1.5577
## asthmb_yes 1.4286
## migraneb_yes 1.4362
## HIVb_yes 7.4746
## threatened_gun_knifeW4b_yes 1.2624
## forcedsex_physicalW4b_yes 1.3934
## beaten_upW4b_yes 1.4998
## sex_abuse_bycaretakerW4b_>10_times 1.4191
## sex_abuse_bycaretakerW4c_10+_times 1.7427
##
## Concordance= 0.689 (se = 0.009 )
## Likelihood ratio test= NA on 48 df, p=NA
## Wald test = 663 on 48 df, p=<2e-16
## Score (logrank) test = NA on 48 df, p=NA#model with demographic variables, plus social demographic variables, plus family circumstances, plus structural bias variables, plus health variables, plus discrimination/victimization, plus subatance abuse
fit1g<-svycoxph(Surv(time=agew3,time2=agew4,event=job_transition)~sex+racethnic+sexorient+educ+incomeW4+pregnancy+births+foster_home+marriedW4a+marriedW4b+insurance_statusW4+unmet_medcareW4+incarcerated+general_health+social_isolationW4+things.going.mywayW4+depressionW4+suicideW4think+physical_limitation+cancer+high_bp+high_chol+diabetes+heart_disease+asthm+migrane+HIV+threatened_gun_knifeW4+forcedsex_physicalW4+beaten_upW4+sex_abuse_bycaretakerW4+alcohol_day_permonthW4+try.cocaineW4+try.methW4+try.herionW4,design=des2)
summary(fit1g)## Stratified 1 - level Cluster Sampling design (with replacement)
## With (130) clusters.
## svydesign(ids = ~psuscid, strata = ~region, weights = ~GSWGT4,
## data = addhealth, nest = T)## Call:
## svycoxph(formula = Surv(time = agew3, time2 = agew4, event = job_transition) ~
## sex + racethnic + sexorient + educ + incomeW4 + pregnancy +
## births + foster_home + marriedW4a + marriedW4b + insurance_statusW4 +
## unmet_medcareW4 + incarcerated + general_health + social_isolationW4 +
## things.going.mywayW4 + depressionW4 + suicideW4think +
## physical_limitation + cancer + high_bp + high_chol +
## diabetes + heart_disease + asthm + migrane + HIV + threatened_gun_knifeW4 +
## forcedsex_physicalW4 + beaten_upW4 + sex_abuse_bycaretakerW4 +
## alcohol_day_permonthW4 + try.cocaineW4 + try.methW4 +
## try.herionW4, design = des2)
##
## n= 6065, number of events= 1693
##
## coef exp(coef) se(coef) z
## sexb_female -0.130391 0.877752 0.070606 -1.847
## racethnicb-nhblack 0.147821 1.159305 0.117438 1.259
## racethnicc-hispanic -0.086811 0.916851 0.156839 -0.554
## racethnicd-asian -0.203657 0.815742 0.136945 -1.487
## racethnice-native_american 0.311910 1.366031 0.381457 0.818
## racethnicf-other 0.241930 1.273706 0.417471 0.580
## sexorientb_bisexual 0.424368 1.528624 0.203621 2.084
## sexorientc_LGB -0.513684 0.598287 0.252170 -2.037
## educb_highschool_grad -0.057934 0.943712 0.106841 -0.542
## educc_college_bach -0.393456 0.674721 0.150865 -2.608
## educd_college+ -1.301084 0.272237 0.202378 -6.429
## incomeW4b_$15,000<$30,000 0.071578 1.074202 0.225494 0.317
## incomeW4c_$30,000<$50,000 -0.142900 0.866840 0.218534 -0.654
## incomeW4d_$50,000<$75,000 -0.362505 0.695931 0.227371 -1.594
## incomeW4e_$75,000<$100,000 -0.338210 0.713046 0.218286 -1.549
## incomeW4f_$100,000<$150,000 -0.576720 0.561738 0.254121 -2.269
## incomeW4g_$150,000+ -0.544870 0.579917 0.265716 -2.051
## pregnancyb_yes -0.166648 0.846498 0.101719 -1.638
## birthsb_yes -0.305357 0.736861 0.119671 -2.552
## birthsskip -0.010238 0.989815 0.112149 -0.091
## foster_homeb_yes -0.392916 0.675086 0.263127 -1.493
## marriedW4ab_married 0.337182 1.400994 0.129448 2.605
## marriedW4bb_seperated/divorced -0.037515 0.963180 0.193694 -0.194
## marriedW4bc_skip 0.034226 1.034818 0.136067 0.252
## insurance_statusW4b_yes_insurance -0.198228 0.820182 0.082619 -2.399
## unmet_medcareW4b_yes 0.024900 1.025212 0.078013 0.319
## incarceratedb_yes -0.043464 0.957467 0.103064 -0.422
## incarceratedskip -0.226337 0.797449 0.087219 -2.595
## general_healthb_poor/bad -0.049484 0.951721 0.116621 -0.424
## social_isolationW4b_sometimes/often 0.031285 1.031780 0.068893 0.454
## things.going.mywayW4b_sometimes -0.181113 0.834341 0.108337 -1.672
## things.going.mywayW4c_often -0.270520 0.762983 0.109969 -2.460
## depressionW4b_yes 0.036897 1.037587 0.095555 0.386
## suicideW4thinkb_yes -0.020160 0.980042 0.108581 -0.186
## physical_limitationb_limited 0.165053 1.179456 0.125272 1.318
## cancerb_yes 0.389752 1.476615 0.270877 1.439
## high_bpb_yes 0.009657 1.009704 0.115283 0.084
## high_cholb_yes 0.032901 1.033449 0.100670 0.327
## diabetesb_yes -0.063213 0.938743 0.200351 -0.316
## heart_diseaseb_yes -0.108977 0.896751 0.265839 -0.410
## asthmb_yes 0.194792 1.215059 0.080260 2.427
## migraneb_yes 0.156354 1.169240 0.099938 1.565
## HIVb_yes 0.659842 1.934487 0.711842 0.927
## threatened_gun_knifeW4b_yes -0.072144 0.930397 0.159115 -0.453
## forcedsex_physicalW4b_yes 0.115355 1.122272 0.107814 1.070
## beaten_upW4b_yes 0.070952 1.073529 0.158447 0.448
## sex_abuse_bycaretakerW4b_>10_times 0.052262 1.053652 0.153237 0.341
## sex_abuse_bycaretakerW4c_10+_times 0.082059 1.085520 0.232424 0.353
## alcohol_day_permonthW4b_1or2days/week -0.011308 0.988756 0.087053 -0.130
## alcohol_day_permonthW4c_3to5days/week -0.100543 0.904346 0.098232 -1.024
## alcohol_day_permonthW4d_daily 0.087205 1.091120 0.143236 0.609
## try.cocaineW4b_yes 0.019990 1.020191 0.118370 0.169
## try.methW4b_yes 0.063723 1.065797 0.112073 0.569
## try.herionW4b_yes 0.243196 1.275319 0.087458 2.781
## Pr(>|z|)
## sexb_female 0.06479 .
## racethnicb-nhblack 0.20814
## racethnicc-hispanic 0.57992
## racethnicd-asian 0.13698
## racethnice-native_american 0.41354
## racethnicf-other 0.56224
## sexorientb_bisexual 0.03715 *
## sexorientc_LGB 0.04164 *
## educb_highschool_grad 0.58765
## educc_college_bach 0.00911 **
## educd_college+ 1.28e-10 ***
## incomeW4b_$15,000<$30,000 0.75092
## incomeW4c_$30,000<$50,000 0.51317
## incomeW4d_$50,000<$75,000 0.11086
## incomeW4e_$75,000<$100,000 0.12129
## incomeW4f_$100,000<$150,000 0.02324 *
## incomeW4g_$150,000+ 0.04031 *
## pregnancyb_yes 0.10135
## birthsb_yes 0.01072 *
## birthsskip 0.92727
## foster_homeb_yes 0.13537
## marriedW4ab_married 0.00919 **
## marriedW4bb_seperated/divorced 0.84643
## marriedW4bc_skip 0.80140
## insurance_statusW4b_yes_insurance 0.01643 *
## unmet_medcareW4b_yes 0.74959
## incarceratedb_yes 0.67323
## incarceratedskip 0.00946 **
## general_healthb_poor/bad 0.67134
## social_isolationW4b_sometimes/often 0.64975
## things.going.mywayW4b_sometimes 0.09457 .
## things.going.mywayW4c_often 0.01390 *
## depressionW4b_yes 0.69939
## suicideW4thinkb_yes 0.85271
## physical_limitationb_limited 0.18765
## cancerb_yes 0.15019
## high_bpb_yes 0.93324
## high_cholb_yes 0.74380
## diabetesb_yes 0.75237
## heart_diseaseb_yes 0.68185
## asthmb_yes 0.01522 *
## migraneb_yes 0.11770
## HIVb_yes 0.35395
## threatened_gun_knifeW4b_yes 0.65026
## forcedsex_physicalW4b_yes 0.28465
## beaten_upW4b_yes 0.65430
## sex_abuse_bycaretakerW4b_>10_times 0.73306
## sex_abuse_bycaretakerW4c_10+_times 0.72404
## alcohol_day_permonthW4b_1or2days/week 0.89665
## alcohol_day_permonthW4c_3to5days/week 0.30606
## alcohol_day_permonthW4d_daily 0.54264
## try.cocaineW4b_yes 0.86589
## try.methW4b_yes 0.56964
## try.herionW4b_yes 0.00542 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95
## sexb_female 0.8778 1.1393 0.7643
## racethnicb-nhblack 1.1593 0.8626 0.9209
## racethnicc-hispanic 0.9169 1.0907 0.6742
## racethnicd-asian 0.8157 1.2259 0.6237
## racethnice-native_american 1.3660 0.7320 0.6468
## racethnicf-other 1.2737 0.7851 0.5620
## sexorientb_bisexual 1.5286 0.6542 1.0256
## sexorientc_LGB 0.5983 1.6714 0.3650
## educb_highschool_grad 0.9437 1.0596 0.7654
## educc_college_bach 0.6747 1.4821 0.5020
## educd_college+ 0.2722 3.6733 0.1831
## incomeW4b_$15,000<$30,000 1.0742 0.9309 0.6905
## incomeW4c_$30,000<$50,000 0.8668 1.1536 0.5648
## incomeW4d_$50,000<$75,000 0.6959 1.4369 0.4457
## incomeW4e_$75,000<$100,000 0.7130 1.4024 0.4648
## incomeW4f_$100,000<$150,000 0.5617 1.7802 0.3414
## incomeW4g_$150,000+ 0.5799 1.7244 0.3445
## pregnancyb_yes 0.8465 1.1813 0.6935
## birthsb_yes 0.7369 1.3571 0.5828
## birthsskip 0.9898 1.0103 0.7945
## foster_homeb_yes 0.6751 1.4813 0.4031
## marriedW4ab_married 1.4010 0.7138 1.0871
## marriedW4bb_seperated/divorced 0.9632 1.0382 0.6589
## marriedW4bc_skip 1.0348 0.9664 0.7926
## insurance_statusW4b_yes_insurance 0.8202 1.2192 0.6976
## unmet_medcareW4b_yes 1.0252 0.9754 0.8799
## incarceratedb_yes 0.9575 1.0444 0.7823
## incarceratedskip 0.7974 1.2540 0.6721
## general_healthb_poor/bad 0.9517 1.0507 0.7573
## social_isolationW4b_sometimes/often 1.0318 0.9692 0.9015
## things.going.mywayW4b_sometimes 0.8343 1.1986 0.6747
## things.going.mywayW4c_often 0.7630 1.3106 0.6150
## depressionW4b_yes 1.0376 0.9638 0.8604
## suicideW4thinkb_yes 0.9800 1.0204 0.7922
## physical_limitationb_limited 1.1795 0.8478 0.9227
## cancerb_yes 1.4766 0.6772 0.8684
## high_bpb_yes 1.0097 0.9904 0.8055
## high_cholb_yes 1.0334 0.9676 0.8484
## diabetesb_yes 0.9387 1.0653 0.6339
## heart_diseaseb_yes 0.8968 1.1151 0.5326
## asthmb_yes 1.2151 0.8230 1.0382
## migraneb_yes 1.1692 0.8553 0.9612
## HIVb_yes 1.9345 0.5169 0.4793
## threatened_gun_knifeW4b_yes 0.9304 1.0748 0.6811
## forcedsex_physicalW4b_yes 1.1223 0.8910 0.9085
## beaten_upW4b_yes 1.0735 0.9315 0.7869
## sex_abuse_bycaretakerW4b_>10_times 1.0537 0.9491 0.7803
## sex_abuse_bycaretakerW4c_10+_times 1.0855 0.9212 0.6883
## alcohol_day_permonthW4b_1or2days/week 0.9888 1.0114 0.8337
## alcohol_day_permonthW4c_3to5days/week 0.9043 1.1058 0.7460
## alcohol_day_permonthW4d_daily 1.0911 0.9165 0.8240
## try.cocaineW4b_yes 1.0202 0.9802 0.8090
## try.methW4b_yes 1.0658 0.9383 0.8556
## try.herionW4b_yes 1.2753 0.7841 1.0744
## upper .95
## sexb_female 1.0080
## racethnicb-nhblack 1.4594
## racethnicc-hispanic 1.2468
## racethnicd-asian 1.0669
## racethnice-native_american 2.8851
## racethnicf-other 2.8868
## sexorientb_bisexual 2.2784
## sexorientc_LGB 0.9807
## educb_highschool_grad 1.1635
## educc_college_bach 0.9069
## educd_college+ 0.4048
## incomeW4b_$15,000<$30,000 1.6712
## incomeW4c_$30,000<$50,000 1.3303
## incomeW4d_$50,000<$75,000 1.0867
## incomeW4e_$75,000<$100,000 1.0938
## incomeW4f_$100,000<$150,000 0.9244
## incomeW4g_$150,000+ 0.9762
## pregnancyb_yes 1.0333
## birthsb_yes 0.9316
## birthsskip 1.2331
## foster_homeb_yes 1.1307
## marriedW4ab_married 1.8056
## marriedW4bb_seperated/divorced 1.4079
## marriedW4bc_skip 1.3511
## insurance_statusW4b_yes_insurance 0.9644
## unmet_medcareW4b_yes 1.1946
## incarceratedb_yes 1.1718
## incarceratedskip 0.9461
## general_healthb_poor/bad 1.1961
## social_isolationW4b_sometimes/often 1.1809
## things.going.mywayW4b_sometimes 1.0317
## things.going.mywayW4c_often 0.9465
## depressionW4b_yes 1.2513
## suicideW4thinkb_yes 1.2125
## physical_limitationb_limited 1.5077
## cancerb_yes 2.5110
## high_bpb_yes 1.2657
## high_cholb_yes 1.2589
## diabetesb_yes 1.3902
## heart_diseaseb_yes 1.5099
## asthmb_yes 1.4220
## migraneb_yes 1.4222
## HIVb_yes 7.8071
## threatened_gun_knifeW4b_yes 1.2709
## forcedsex_physicalW4b_yes 1.3863
## beaten_upW4b_yes 1.4645
## sex_abuse_bycaretakerW4b_>10_times 1.4228
## sex_abuse_bycaretakerW4c_10+_times 1.7119
## alcohol_day_permonthW4b_1or2days/week 1.1727
## alcohol_day_permonthW4c_3to5days/week 1.0964
## alcohol_day_permonthW4d_daily 1.4448
## try.cocaineW4b_yes 1.2866
## try.methW4b_yes 1.3276
## try.herionW4b_yes 1.5138
##
## Concordance= 0.692 (se = 0.009 )
## Likelihood ratio test= NA on 54 df, p=NA
## Wald test = 757.5 on 54 df, p=<2e-16
## Score (logrank) test = NA on 54 df, p=NAschoenresid<-resid(fit1g,type="schoenfeld")
fit.sr<-lm(schoenresid~des2$variables$agew4[des2$variables$job_transition==1])
summary(fit.sr)## Response sexb_female :
##
## Call:
## lm(formula = sexb_female ~ des2$variables$agew4[des2$variables$job_transition ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.4844 -0.4543 -0.4155 0.5401 0.7477
##
## Coefficients:
## Estimate
## (Intercept) -0.021290
## des2$variables$agew4[des2$variables$job_transition == 1] 0.002798
## Std. Error
## (Intercept) 0.216140
## des2$variables$agew4[des2$variables$job_transition == 1] 0.007550
## t value Pr(>|t|)
## (Intercept) -0.098 0.922
## des2$variables$agew4[des2$variables$job_transition == 1] 0.371 0.711
##
## Residual standard error: 0.4954 on 1691 degrees of freedom
## Multiple R-squared: 8.119e-05, Adjusted R-squared: -0.0005101
## F-statistic: 0.1373 on 1 and 1691 DF, p-value: 0.711
##
##
## Response racethnicb-nhblack :
##
## Call:
## lm(formula = `racethnicb-nhblack` ~ des2$variables$agew4[des2$variables$job_transition ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.4762 -0.2057 -0.1965 -0.1863 0.8226
##
## Coefficients:
## Estimate
## (Intercept) 0.153099
## des2$variables$agew4[des2$variables$job_transition == 1] -0.003091
## Std. Error
## (Intercept) 0.175208
## des2$variables$agew4[des2$variables$job_transition == 1] 0.006120
## t value Pr(>|t|)
## (Intercept) 0.874 0.382
## des2$variables$agew4[des2$variables$job_transition == 1] -0.505 0.614
##
## Residual standard error: 0.4016 on 1691 degrees of freedom
## Multiple R-squared: 0.0001508, Adjusted R-squared: -0.0004405
## F-statistic: 0.255 on 1 and 1691 DF, p-value: 0.6136
##
##
## Response racethnicc-hispanic :
##
## Call:
## lm(formula = `racethnicc-hispanic` ~ des2$variables$agew4[des2$variables$job_transition ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.18047 -0.06824 -0.06322 -0.05746 0.95070
##
## Coefficients:
## Estimate
## (Intercept) -0.088793
## des2$variables$agew4[des2$variables$job_transition == 1] 0.003480
## Std. Error
## (Intercept) 0.110706
## des2$variables$agew4[des2$variables$job_transition == 1] 0.003867
## t value Pr(>|t|)
## (Intercept) -0.802 0.423
## des2$variables$agew4[des2$variables$job_transition == 1] 0.900 0.368
##
## Residual standard error: 0.2537 on 1691 degrees of freedom
## Multiple R-squared: 0.0004787, Adjusted R-squared: -0.0001123
## F-statistic: 0.8099 on 1 and 1691 DF, p-value: 0.3683
##
##
## Response racethnicd-asian :
##
## Call:
## lm(formula = `racethnicd-asian` ~ des2$variables$agew4[des2$variables$job_transition ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.07026 -0.05098 -0.04655 -0.04200 0.96532
##
## Coefficients:
## Estimate
## (Intercept) -0.083139
## des2$variables$agew4[des2$variables$job_transition == 1] 0.003661
## Std. Error
## (Intercept) 0.092600
## des2$variables$agew4[des2$variables$job_transition == 1] 0.003235
## t value Pr(>|t|)
## (Intercept) -0.898 0.369
## des2$variables$agew4[des2$variables$job_transition == 1] 1.132 0.258
##
## Residual standard error: 0.2122 on 1691 degrees of freedom
## Multiple R-squared: 0.0007569, Adjusted R-squared: 0.000166
## F-statistic: 1.281 on 1 and 1691 DF, p-value: 0.2579
##
##
## Response racethnice-native_american :
##
## Call:
## lm(formula = `racethnice-native_american` ~ des2$variables$agew4[des2$variables$job_transition ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.01384 -0.00915 -0.00640 -0.00503 0.99644
##
## Coefficients:
## Estimate
## (Intercept) -0.013610
## des2$variables$agew4[des2$variables$job_transition == 1] 0.000426
## Std. Error
## (Intercept) 0.036614
## des2$variables$agew4[des2$variables$job_transition == 1] 0.001279
## t value Pr(>|t|)
## (Intercept) -0.372 0.710
## des2$variables$agew4[des2$variables$job_transition == 1] 0.333 0.739
##
## Residual standard error: 0.08392 on 1691 degrees of freedom
## Multiple R-squared: 6.561e-05, Adjusted R-squared: -0.0005257
## F-statistic: 0.111 on 1 and 1691 DF, p-value: 0.7391
##
##
## Response racethnicf-other :
##
## Call:
## lm(formula = `racethnicf-other` ~ des2$variables$agew4[des2$variables$job_transition ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.01515 -0.01019 -0.00839 -0.00640 0.99751
##
## Coefficients:
## Estimate
## (Intercept) 0.031785
## des2$variables$agew4[des2$variables$job_transition == 1] -0.001127
## Std. Error
## (Intercept) 0.039526
## des2$variables$agew4[des2$variables$job_transition == 1] 0.001381
## t value Pr(>|t|)
## (Intercept) 0.804 0.421
## des2$variables$agew4[des2$variables$job_transition == 1] -0.816 0.414
##
## Residual standard error: 0.0906 on 1691 degrees of freedom
## Multiple R-squared: 0.000394, Adjusted R-squared: -0.0001971
## F-statistic: 0.6665 on 1 and 1691 DF, p-value: 0.4144
##
##
## Response sexorientb_bisexual :
##
## Call:
## lm(formula = sexorientb_bisexual ~ des2$variables$agew4[des2$variables$job_transition ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.05242 -0.03023 -0.02433 -0.01818 0.98953
##
## Coefficients:
## Estimate
## (Intercept) -0.134171
## des2$variables$agew4[des2$variables$job_transition == 1] 0.004618
## Std. Error
## (Intercept) 0.066983
## des2$variables$agew4[des2$variables$job_transition == 1] 0.002340
## t value Pr(>|t|)
## (Intercept) -2.003 0.0453
## des2$variables$agew4[des2$variables$job_transition == 1] 1.974 0.0486
##
## (Intercept) *
## des2$variables$agew4[des2$variables$job_transition == 1] *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1535 on 1691 degrees of freedom
## Multiple R-squared: 0.002298, Adjusted R-squared: 0.001708
## F-statistic: 3.895 on 1 and 1691 DF, p-value: 0.0486
##
##
## Response sexorientc_LGB :
##
## Call:
## lm(formula = sexorientc_LGB ~ des2$variables$agew4[des2$variables$job_transition ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.03262 -0.02022 -0.01722 -0.01432 0.99084
##
## Coefficients:
## Estimate
## (Intercept) -0.047758
## des2$variables$agew4[des2$variables$job_transition == 1] 0.001715
## Std. Error
## (Intercept) 0.057552
## des2$variables$agew4[des2$variables$job_transition == 1] 0.002010
## t value Pr(>|t|)
## (Intercept) -0.830 0.407
## des2$variables$agew4[des2$variables$job_transition == 1] 0.853 0.394
##
## Residual standard error: 0.1319 on 1691 degrees of freedom
## Multiple R-squared: 0.0004304, Adjusted R-squared: -0.0001607
## F-statistic: 0.7281 on 1 and 1691 DF, p-value: 0.3936
##
##
## Response educb_highschool_grad :
##
## Call:
## lm(formula = educb_highschool_grad ~ des2$variables$agew4[des2$variables$job_transition ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.7331 -0.6614 0.3017 0.3372 0.3437
##
## Coefficients:
## Estimate
## (Intercept) -0.019811
## des2$variables$agew4[des2$variables$job_transition == 1] 0.000174
## Std. Error
## (Intercept) 0.204553
## des2$variables$agew4[des2$variables$job_transition == 1] 0.007145
## t value Pr(>|t|)
## (Intercept) -0.097 0.923
## des2$variables$agew4[des2$variables$job_transition == 1] 0.024 0.981
##
## Residual standard error: 0.4688 on 1691 degrees of freedom
## Multiple R-squared: 3.506e-07, Adjusted R-squared: -0.000591
## F-statistic: 0.0005929 on 1 and 1691 DF, p-value: 0.9806
##
##
## Response educc_college_bach :
##
## Call:
## lm(formula = educc_college_bach ~ des2$variables$agew4[des2$variables$job_transition ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.2570 -0.2306 -0.2143 -0.1545 0.9276
##
## Coefficients:
## Estimate
## (Intercept) 0.173543
## des2$variables$agew4[des2$variables$job_transition == 1] -0.005432
## Std. Error
## (Intercept) 0.178437
## des2$variables$agew4[des2$variables$job_transition == 1] 0.006233
## t value Pr(>|t|)
## (Intercept) 0.973 0.331
## des2$variables$agew4[des2$variables$job_transition == 1] -0.871 0.384
##
## Residual standard error: 0.409 on 1691 degrees of freedom
## Multiple R-squared: 0.0004489, Adjusted R-squared: -0.0001422
## F-statistic: 0.7594 on 1 and 1691 DF, p-value: 0.3836
##
##
## Response educd_college+ :
##
## Call:
## lm(formula = `educd_college+` ~ des2$variables$agew4[des2$variables$job_transition ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.05649 -0.04124 -0.03676 -0.03137 0.98203
##
## Coefficients:
## Estimate
## (Intercept) -0.098586
## des2$variables$agew4[des2$variables$job_transition == 1] 0.003812
## Std. Error
## (Intercept) 0.082602
## des2$variables$agew4[des2$variables$job_transition == 1] 0.002885
## t value Pr(>|t|)
## (Intercept) -1.194 0.233
## des2$variables$agew4[des2$variables$job_transition == 1] 1.321 0.187
##
## Residual standard error: 0.1893 on 1691 degrees of freedom
## Multiple R-squared: 0.001031, Adjusted R-squared: 0.0004402
## F-statistic: 1.745 on 1 and 1691 DF, p-value: 0.1867
##
##
## Response incomeW4b_$15,000<$30,000 :
##
## Call:
## lm(formula = `incomeW4b_$15,000<$30,000` ~ des2$variables$agew4[des2$variables$job_transition ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.4610 -0.1947 -0.1818 -0.1687 0.8449
##
## Coefficients:
## Estimate
## (Intercept) 0.185016
## des2$variables$agew4[des2$variables$job_transition == 1] -0.006806
## Std. Error
## (Intercept) 0.170760
## des2$variables$agew4[des2$variables$job_transition == 1] 0.005965
## t value Pr(>|t|)
## (Intercept) 1.083 0.279
## des2$variables$agew4[des2$variables$job_transition == 1] -1.141 0.254
##
## Residual standard error: 0.3914 on 1691 degrees of freedom
## Multiple R-squared: 0.0007694, Adjusted R-squared: 0.0001785
## F-statistic: 1.302 on 1 and 1691 DF, p-value: 0.254
##
##
## Response incomeW4c_$30,000<$50,000 :
##
## Call:
## lm(formula = `incomeW4c_$30,000<$50,000` ~ des2$variables$agew4[des2$variables$job_transition ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.3100 -0.2806 -0.2669 0.7086 0.7510
##
## Coefficients:
## Estimate
## (Intercept) -0.154616
## des2$variables$agew4[des2$variables$job_transition == 1] 0.004961
## Std. Error
## (Intercept) 0.194649
## des2$variables$agew4[des2$variables$job_transition == 1] 0.006799
## t value Pr(>|t|)
## (Intercept) -0.794 0.427
## des2$variables$agew4[des2$variables$job_transition == 1] 0.730 0.466
##
## Residual standard error: 0.4461 on 1691 degrees of freedom
## Multiple R-squared: 0.0003147, Adjusted R-squared: -0.0002765
## F-statistic: 0.5323 on 1 and 1691 DF, p-value: 0.4657
##
##
## Response incomeW4d_$50,000<$75,000 :
##
## Call:
## lm(formula = `incomeW4d_$50,000<$75,000` ~ des2$variables$agew4[des2$variables$job_transition ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.2869 -0.2570 -0.2468 0.7131 0.7793
##
## Coefficients:
## Estimate
## (Intercept) 0.084433
## des2$variables$agew4[des2$variables$job_transition == 1] -0.002669
## Std. Error
## (Intercept) 0.189308
## des2$variables$agew4[des2$variables$job_transition == 1] 0.006613
## t value Pr(>|t|)
## (Intercept) 0.446 0.656
## des2$variables$agew4[des2$variables$job_transition == 1] -0.404 0.686
##
## Residual standard error: 0.4339 on 1691 degrees of freedom
## Multiple R-squared: 9.636e-05, Adjusted R-squared: -0.0004949
## F-statistic: 0.163 on 1 and 1691 DF, p-value: 0.6865
##
##
## Response incomeW4e_$75,000<$100,000 :
##
## Call:
## lm(formula = `incomeW4e_$75,000<$100,000` ~ des2$variables$agew4[des2$variables$job_transition ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.1685 -0.1482 -0.1426 -0.1364 0.9238
##
## Coefficients:
## Estimate
## (Intercept) -0.083497
## des2$variables$agew4[des2$variables$job_transition == 1] 0.002948
## Std. Error
## (Intercept) 0.153211
## des2$variables$agew4[des2$variables$job_transition == 1] 0.005352
## t value Pr(>|t|)
## (Intercept) -0.545 0.586
## des2$variables$agew4[des2$variables$job_transition == 1] 0.551 0.582
##
## Residual standard error: 0.3512 on 1691 degrees of freedom
## Multiple R-squared: 0.0001794, Adjusted R-squared: -0.0004119
## F-statistic: 0.3034 on 1 and 1691 DF, p-value: 0.5818
##
##
## Response incomeW4f_$100,000<$150,000 :
##
## Call:
## lm(formula = `incomeW4f_$100,000<$150,000` ~ des2$variables$agew4[des2$variables$job_transition ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.09575 -0.08413 -0.08192 -0.07940 0.92498
##
## Coefficients:
## Estimate
## (Intercept) -0.028970
## des2$variables$agew4[des2$variables$job_transition == 1] 0.001261
## Std. Error
## (Intercept) 0.119833
## des2$variables$agew4[des2$variables$job_transition == 1] 0.004186
## t value Pr(>|t|)
## (Intercept) -0.242 0.809
## des2$variables$agew4[des2$variables$job_transition == 1] 0.301 0.763
##
## Residual standard error: 0.2747 on 1691 degrees of freedom
## Multiple R-squared: 5.365e-05, Adjusted R-squared: -0.0005377
## F-statistic: 0.09073 on 1 and 1691 DF, p-value: 0.7633
##
##
## Response incomeW4g_$150,000+ :
##
## Call:
## lm(formula = `incomeW4g_$150,000+` ~ des2$variables$agew4[des2$variables$job_transition ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.04731 -0.04319 -0.04097 -0.03867 0.97194
##
## Coefficients:
## Estimate
## (Intercept) 0.038407
## des2$variables$agew4[des2$variables$job_transition == 1] -0.001149
## Std. Error
## (Intercept) 0.086317
## des2$variables$agew4[des2$variables$job_transition == 1] 0.003015
## t value Pr(>|t|)
## (Intercept) 0.445 0.656
## des2$variables$agew4[des2$variables$job_transition == 1] -0.381 0.703
##
## Residual standard error: 0.1978 on 1691 degrees of freedom
## Multiple R-squared: 8.586e-05, Adjusted R-squared: -0.0005055
## F-statistic: 0.1452 on 1 and 1691 DF, p-value: 0.7032
##
##
## Response pregnancyb_yes :
##
## Call:
## lm(formula = pregnancyb_yes ~ des2$variables$agew4[des2$variables$job_transition ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.4904 -0.1978 -0.1812 -0.1579 0.8659
##
## Coefficients:
## Estimate
## (Intercept) 0.312069
## des2$variables$agew4[des2$variables$job_transition == 1] -0.010151
## Std. Error
## (Intercept) 0.170032
## des2$variables$agew4[des2$variables$job_transition == 1] 0.005939
## t value Pr(>|t|)
## (Intercept) 1.835 0.0666
## des2$variables$agew4[des2$variables$job_transition == 1] -1.709 0.0876
##
## (Intercept) .
## des2$variables$agew4[des2$variables$job_transition == 1] .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3897 on 1691 degrees of freedom
## Multiple R-squared: 0.001724, Adjusted R-squared: 0.001134
## F-statistic: 2.921 on 1 and 1691 DF, p-value: 0.08761
##
##
## Response birthsb_yes :
##
## Call:
## lm(formula = birthsb_yes ~ des2$variables$agew4[des2$variables$job_transition ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.9025 -0.4579 -0.3786 0.5401 0.6281
##
## Coefficients:
## Estimate
## (Intercept) 0.033190
## des2$variables$agew4[des2$variables$job_transition == 1] -0.001014
## Std. Error
## (Intercept) 0.215549
## des2$variables$agew4[des2$variables$job_transition == 1] 0.007529
## t value Pr(>|t|)
## (Intercept) 0.154 0.878
## des2$variables$agew4[des2$variables$job_transition == 1] -0.135 0.893
##
## Residual standard error: 0.494 on 1691 degrees of freedom
## Multiple R-squared: 1.072e-05, Adjusted R-squared: -0.0005806
## F-statistic: 0.01813 on 1 and 1691 DF, p-value: 0.8929
##
##
## Response birthsskip :
##
## Call:
## lm(formula = birthsskip ~ des2$variables$agew4[des2$variables$job_transition ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.5038 -0.4277 -0.3645 0.5584 0.9708
##
## Coefficients:
## Estimate
## (Intercept) -0.145092
## des2$variables$agew4[des2$variables$job_transition == 1] 0.004647
## Std. Error
## (Intercept) 0.213145
## des2$variables$agew4[des2$variables$job_transition == 1] 0.007445
## t value Pr(>|t|)
## (Intercept) -0.681 0.496
## des2$variables$agew4[des2$variables$job_transition == 1] 0.624 0.533
##
## Residual standard error: 0.4885 on 1691 degrees of freedom
## Multiple R-squared: 0.0002303, Adjusted R-squared: -0.0003609
## F-statistic: 0.3895 on 1 and 1691 DF, p-value: 0.5327
##
##
## Response foster_homeb_yes :
##
## Call:
## lm(formula = foster_homeb_yes ~ des2$variables$agew4[des2$variables$job_transition ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.03993 -0.02050 -0.01744 -0.01558 0.98575
##
## Coefficients:
## Estimate
## (Intercept) 0.033216
## des2$variables$agew4[des2$variables$job_transition == 1] -0.001026
## Std. Error
## (Intercept) 0.059322
## des2$variables$agew4[des2$variables$job_transition == 1] 0.002072
## t value Pr(>|t|)
## (Intercept) 0.560 0.576
## des2$variables$agew4[des2$variables$job_transition == 1] -0.495 0.621
##
## Residual standard error: 0.136 on 1691 degrees of freedom
## Multiple R-squared: 0.0001449, Adjusted R-squared: -0.0004464
## F-statistic: 0.2451 on 1 and 1691 DF, p-value: 0.6206
##
##
## Response marriedW4ab_married :
##
## Call:
## lm(formula = marriedW4ab_married ~ des2$variables$agew4[des2$variables$job_transition ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.6757 -0.5596 0.3503 0.4394 0.5007
##
## Coefficients:
## Estimate
## (Intercept) -0.0044585
## des2$variables$agew4[des2$variables$job_transition == 1] 0.0002624
## Std. Error
## (Intercept) 0.2131476
## des2$variables$agew4[des2$variables$job_transition == 1] 0.0074454
## t value Pr(>|t|)
## (Intercept) -0.021 0.983
## des2$variables$agew4[des2$variables$job_transition == 1] 0.035 0.972
##
## Residual standard error: 0.4885 on 1691 degrees of freedom
## Multiple R-squared: 7.344e-07, Adjusted R-squared: -0.0005906
## F-statistic: 0.001242 on 1 and 1691 DF, p-value: 0.9719
##
##
## Response marriedW4bb_seperated/divorced :
##
## Call:
## lm(formula = `marriedW4bb_seperated/divorced` ~ des2$variables$agew4[des2$variables$job_transition ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.07255 -0.04663 -0.03112 -0.02644 0.97679
##
## Coefficients:
## Estimate
## (Intercept) 0.025468
## des2$variables$agew4[des2$variables$job_transition == 1] -0.001003
## Std. Error
## (Intercept) 0.081153
## des2$variables$agew4[des2$variables$job_transition == 1] 0.002835
## t value Pr(>|t|)
## (Intercept) 0.314 0.754
## des2$variables$agew4[des2$variables$job_transition == 1] -0.354 0.723
##
## Residual standard error: 0.186 on 1691 degrees of freedom
## Multiple R-squared: 7.407e-05, Adjusted R-squared: -0.0005173
## F-statistic: 0.1253 on 1 and 1691 DF, p-value: 0.7234
##
##
## Response marriedW4bc_skip :
##
## Call:
## lm(formula = marriedW4bc_skip ~ des2$variables$agew4[des2$variables$job_transition ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.7038 -0.6138 0.3244 0.3775 0.4536
##
## Coefficients:
## Estimate
## (Intercept) -0.095666
## des2$variables$agew4[des2$variables$job_transition == 1] 0.003286
## Std. Error
## (Intercept) 0.208274
## des2$variables$agew4[des2$variables$job_transition == 1] 0.007275
## t value Pr(>|t|)
## (Intercept) -0.459 0.646
## des2$variables$agew4[des2$variables$job_transition == 1] 0.452 0.652
##
## Residual standard error: 0.4774 on 1691 degrees of freedom
## Multiple R-squared: 0.0001206, Adjusted R-squared: -0.0004707
## F-statistic: 0.204 on 1 and 1691 DF, p-value: 0.6516
##
##
## Response insurance_statusW4b_yes_insurance :
##
## Call:
## lm(formula = insurance_statusW4b_yes_insurance ~ des2$variables$agew4[des2$variables$job_transition ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.7259 -0.7158 0.2774 0.2815 0.8251
##
## Coefficients:
## Estimate
## (Intercept) -0.0039093
## des2$variables$agew4[des2$variables$job_transition == 1] 0.0006787
## Std. Error
## (Intercept) 0.1969323
## des2$variables$agew4[des2$variables$job_transition == 1] 0.0068790
## t value Pr(>|t|)
## (Intercept) -0.020 0.984
## des2$variables$agew4[des2$variables$job_transition == 1] 0.099 0.921
##
## Residual standard error: 0.4514 on 1691 degrees of freedom
## Multiple R-squared: 5.756e-06, Adjusted R-squared: -0.0005856
## F-statistic: 0.009733 on 1 and 1691 DF, p-value: 0.9214
##
##
## Response unmet_medcareW4b_yes :
##
## Call:
## lm(formula = unmet_medcareW4b_yes ~ des2$variables$agew4[des2$variables$job_transition ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.6577 -0.3202 -0.2998 0.6671 0.7357
##
## Coefficients:
## Estimate
## (Intercept) 0.266889
## des2$variables$agew4[des2$variables$job_transition == 1] -0.009323
## Std. Error
## (Intercept) 0.203226
## des2$variables$agew4[des2$variables$job_transition == 1] 0.007099
## t value Pr(>|t|)
## (Intercept) 1.313 0.189
## des2$variables$agew4[des2$variables$job_transition == 1] -1.313 0.189
##
## Residual standard error: 0.4658 on 1691 degrees of freedom
## Multiple R-squared: 0.001019, Adjusted R-squared: 0.0004281
## F-statistic: 1.725 on 1 and 1691 DF, p-value: 0.1893
##
##
## Response incarceratedb_yes :
##
## Call:
## lm(formula = incarceratedb_yes ~ des2$variables$agew4[des2$variables$job_transition ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.2831 -0.2270 -0.2134 -0.2054 0.9342
##
## Coefficients:
## Estimate
## (Intercept) 0.0035860
## des2$variables$agew4[des2$variables$job_transition == 1] -0.0006775
## Std. Error
## (Intercept) 0.1823071
## des2$variables$agew4[des2$variables$job_transition == 1] 0.0063681
## t value Pr(>|t|)
## (Intercept) 0.020 0.984
## des2$variables$agew4[des2$variables$job_transition == 1] -0.106 0.915
##
## Residual standard error: 0.4178 on 1691 degrees of freedom
## Multiple R-squared: 6.693e-06, Adjusted R-squared: -0.0005847
## F-statistic: 0.01132 on 1 and 1691 DF, p-value: 0.9153
##
##
## Response incarceratedskip :
##
## Call:
## lm(formula = incarceratedskip ~ des2$variables$agew4[des2$variables$job_transition ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.6297 -0.6059 0.3775 0.3915 0.5075
##
## Coefficients:
## Estimate
## (Intercept) -0.007258
## des2$variables$agew4[des2$variables$job_transition == 1] 0.001273
## Std. Error
## (Intercept) 0.213037
## des2$variables$agew4[des2$variables$job_transition == 1] 0.007442
## t value Pr(>|t|)
## (Intercept) -0.034 0.973
## des2$variables$agew4[des2$variables$job_transition == 1] 0.171 0.864
##
## Residual standard error: 0.4883 on 1691 degrees of freedom
## Multiple R-squared: 1.731e-05, Adjusted R-squared: -0.000574
## F-statistic: 0.02927 on 1 and 1691 DF, p-value: 0.8642
##
##
## Response general_healthb_poor/bad :
##
## Call:
## lm(formula = `general_healthb_poor/bad` ~ des2$variables$agew4[des2$variables$job_transition ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.4506 -0.1105 -0.1029 -0.0932 0.9160
##
## Coefficients:
## Estimate
## (Intercept) 0.170907
## des2$variables$agew4[des2$variables$job_transition == 1] -0.005770
## Std. Error
## (Intercept) 0.135610
## des2$variables$agew4[des2$variables$job_transition == 1] 0.004737
## t value Pr(>|t|)
## (Intercept) 1.260 0.208
## des2$variables$agew4[des2$variables$job_transition == 1] -1.218 0.223
##
## Residual standard error: 0.3108 on 1691 degrees of freedom
## Multiple R-squared: 0.0008768, Adjusted R-squared: 0.0002859
## F-statistic: 1.484 on 1 and 1691 DF, p-value: 0.2233
##
##
## Response social_isolationW4b_sometimes/often :
##
## Call:
## lm(formula = `social_isolationW4b_sometimes/often` ~ des2$variables$agew4[des2$variables$job_transition ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.4299 -0.3386 -0.3236 0.6554 0.7141
##
## Coefficients:
## Estimate
## (Intercept) 0.219235
## des2$variables$agew4[des2$variables$job_transition == 1] -0.007508
## Std. Error
## (Intercept) 0.205907
## des2$variables$agew4[des2$variables$job_transition == 1] 0.007192
## t value Pr(>|t|)
## (Intercept) 1.065 0.287
## des2$variables$agew4[des2$variables$job_transition == 1] -1.044 0.297
##
## Residual standard error: 0.4719 on 1691 degrees of freedom
## Multiple R-squared: 0.0006439, Adjusted R-squared: 5.293e-05
## F-statistic: 1.09 on 1 and 1691 DF, p-value: 0.2967
##
##
## Response things.going.mywayW4b_sometimes :
##
## Call:
## lm(formula = things.going.mywayW4b_sometimes ~ des2$variables$agew4[des2$variables$job_transition ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.4571 -0.4018 -0.3873 0.5982 0.6276
##
## Coefficients:
## Estimate
## (Intercept) 0.142384
## des2$variables$agew4[des2$variables$job_transition == 1] -0.004474
## Std. Error
## (Intercept) 0.213668
## des2$variables$agew4[des2$variables$job_transition == 1] 0.007464
## t value Pr(>|t|)
## (Intercept) 0.666 0.505
## des2$variables$agew4[des2$variables$job_transition == 1] -0.599 0.549
##
## Residual standard error: 0.4897 on 1691 degrees of freedom
## Multiple R-squared: 0.0002124, Adjusted R-squared: -0.0003788
## F-statistic: 0.3593 on 1 and 1691 DF, p-value: 0.549
##
##
## Response things.going.mywayW4c_often :
##
## Call:
## lm(formula = things.going.mywayW4c_often ~ des2$variables$agew4[des2$variables$job_transition ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.5038 -0.4857 -0.4834 0.5143 0.5167
##
## Coefficients:
## Estimate
## (Intercept) -0.005030
## des2$variables$agew4[des2$variables$job_transition == 1] -0.000140
## Std. Error
## (Intercept) 0.218177
## des2$variables$agew4[des2$variables$job_transition == 1] 0.007621
## t value Pr(>|t|)
## (Intercept) -0.023 0.982
## des2$variables$agew4[des2$variables$job_transition == 1] -0.018 0.985
##
## Residual standard error: 0.5001 on 1691 degrees of freedom
## Multiple R-squared: 1.996e-07, Adjusted R-squared: -0.0005912
## F-statistic: 0.0003376 on 1 and 1691 DF, p-value: 0.9853
##
##
## Response depressionW4b_yes :
##
## Call:
## lm(formula = depressionW4b_yes ~ des2$variables$agew4[des2$variables$job_transition ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.2510 -0.1777 -0.1626 -0.1414 0.8769
##
## Coefficients:
## Estimate
## (Intercept) 0.257135
## des2$variables$agew4[des2$variables$job_transition == 1] -0.008984
## Std. Error
## (Intercept) 0.163151
## des2$variables$agew4[des2$variables$job_transition == 1] 0.005699
## t value Pr(>|t|)
## (Intercept) 1.576 0.115
## des2$variables$agew4[des2$variables$job_transition == 1] -1.576 0.115
##
## Residual standard error: 0.3739 on 1691 degrees of freedom
## Multiple R-squared: 0.001467, Adjusted R-squared: 0.0008769
## F-statistic: 2.485 on 1 and 1691 DF, p-value: 0.1151
##
##
## Response suicideW4thinkb_yes :
##
## Call:
## lm(formula = suicideW4thinkb_yes ~ des2$variables$agew4[des2$variables$job_transition ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.17476 -0.08825 -0.08395 -0.07677 0.93136
##
## Coefficients:
## Estimate
## (Intercept) 0.106595
## des2$variables$agew4[des2$variables$job_transition == 1] -0.003828
## Std. Error
## (Intercept) 0.121337
## des2$variables$agew4[des2$variables$job_transition == 1] 0.004238
## t value Pr(>|t|)
## (Intercept) 0.879 0.380
## des2$variables$agew4[des2$variables$job_transition == 1] -0.903 0.367
##
## Residual standard error: 0.2781 on 1691 degrees of freedom
## Multiple R-squared: 0.0004822, Adjusted R-squared: -0.0001089
## F-statistic: 0.8158 on 1 and 1691 DF, p-value: 0.3665
##
##
## Response physical_limitationb_limited :
##
## Call:
## lm(formula = physical_limitationb_limited ~ des2$variables$agew4[des2$variables$job_transition ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.08094 -0.07919 -0.07618 -0.07266 0.93033
##
## Coefficients:
## Estimate
## (Intercept) 0.0073401
## des2$variables$agew4[des2$variables$job_transition == 1] -0.0003502
## Std. Error
## (Intercept) 0.1144156
## des2$variables$agew4[des2$variables$job_transition == 1] 0.0039966
## t value Pr(>|t|)
## (Intercept) 0.064 0.949
## des2$variables$agew4[des2$variables$job_transition == 1] -0.088 0.930
##
## Residual standard error: 0.2622 on 1691 degrees of freedom
## Multiple R-squared: 4.54e-06, Adjusted R-squared: -0.0005868
## F-statistic: 0.007678 on 1 and 1691 DF, p-value: 0.9302
##
##
## Response cancerb_yes :
##
## Call:
## lm(formula = cancerb_yes ~ des2$variables$agew4[des2$variables$job_transition ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.02618 -0.01694 -0.01356 -0.01114 0.99101
##
## Coefficients:
## Estimate
## (Intercept) -0.062638
## des2$variables$agew4[des2$variables$job_transition == 1] 0.002153
## Std. Error
## (Intercept) 0.051588
## des2$variables$agew4[des2$variables$job_transition == 1] 0.001802
## t value Pr(>|t|)
## (Intercept) -1.214 0.225
## des2$variables$agew4[des2$variables$job_transition == 1] 1.195 0.232
##
## Residual standard error: 0.1182 on 1691 degrees of freedom
## Multiple R-squared: 0.0008432, Adjusted R-squared: 0.0002523
## F-statistic: 1.427 on 1 and 1691 DF, p-value: 0.2324
##
##
## Response high_bpb_yes :
##
## Call:
## lm(formula = high_bpb_yes ~ des2$variables$agew4[des2$variables$job_transition ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.14783 -0.11356 -0.10146 -0.09337 0.92470
##
## Coefficients:
## Estimate
## (Intercept) 0.183084
## des2$variables$agew4[des2$variables$job_transition == 1] -0.006541
## Std. Error
## (Intercept) 0.133685
## des2$variables$agew4[des2$variables$job_transition == 1] 0.004670
## t value Pr(>|t|)
## (Intercept) 1.370 0.171
## des2$variables$agew4[des2$variables$job_transition == 1] -1.401 0.161
##
## Residual standard error: 0.3064 on 1691 degrees of freedom
## Multiple R-squared: 0.001159, Adjusted R-squared: 0.0005683
## F-statistic: 1.962 on 1 and 1691 DF, p-value: 0.1615
##
##
## Response high_cholb_yes :
##
## Call:
## lm(formula = high_cholb_yes ~ des2$variables$agew4[des2$variables$job_transition ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.10461 -0.08973 -0.08344 -0.07404 0.98919
##
## Coefficients:
## Estimate
## (Intercept) 0.075857
## des2$variables$agew4[des2$variables$job_transition == 1] -0.002726
## Std. Error
## (Intercept) 0.120828
## des2$variables$agew4[des2$variables$job_transition == 1] 0.004221
## t value Pr(>|t|)
## (Intercept) 0.628 0.530
## des2$variables$agew4[des2$variables$job_transition == 1] -0.646 0.518
##
## Residual standard error: 0.2769 on 1691 degrees of freedom
## Multiple R-squared: 0.0002466, Adjusted R-squared: -0.0003446
## F-statistic: 0.4171 on 1 and 1691 DF, p-value: 0.5185
##
##
## Response diabetesb_yes :
##
## Call:
## lm(formula = diabetesb_yes ~ des2$variables$agew4[des2$variables$job_transition ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.05013 -0.02868 -0.02507 -0.02148 0.98228
##
## Coefficients:
## Estimate
## (Intercept) -0.071498
## des2$variables$agew4[des2$variables$job_transition == 1] 0.002449
## Std. Error
## (Intercept) 0.068606
## des2$variables$agew4[des2$variables$job_transition == 1] 0.002396
## t value Pr(>|t|)
## (Intercept) -1.042 0.297
## des2$variables$agew4[des2$variables$job_transition == 1] 1.022 0.307
##
## Residual standard error: 0.1572 on 1691 degrees of freedom
## Multiple R-squared: 0.0006172, Adjusted R-squared: 2.625e-05
## F-statistic: 1.044 on 1 and 1691 DF, p-value: 0.3069
##
##
## Response heart_diseaseb_yes :
##
## Call:
## lm(formula = heart_diseaseb_yes ~ des2$variables$agew4[des2$variables$job_transition ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.01916 -0.01114 -0.00919 -0.00732 0.99372
##
## Coefficients:
## Estimate
## (Intercept) 0.042765
## des2$variables$agew4[des2$variables$job_transition == 1] -0.001407
## Std. Error
## (Intercept) 0.042227
## des2$variables$agew4[des2$variables$job_transition == 1] 0.001475
## t value Pr(>|t|)
## (Intercept) 1.013 0.311
## des2$variables$agew4[des2$variables$job_transition == 1] -0.954 0.340
##
## Residual standard error: 0.09679 on 1691 degrees of freedom
## Multiple R-squared: 0.0005376, Adjusted R-squared: -5.344e-05
## F-statistic: 0.9096 on 1 and 1691 DF, p-value: 0.3404
##
##
## Response asthmb_yes :
##
## Call:
## lm(formula = asthmb_yes ~ des2$variables$agew4[des2$variables$job_transition ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.1972 -0.1806 -0.1705 -0.1514 0.8670
##
## Coefficients:
## Estimate
## (Intercept) 0.088754
## des2$variables$agew4[des2$variables$job_transition == 1] -0.003422
## Std. Error
## (Intercept) 0.164820
## des2$variables$agew4[des2$variables$job_transition == 1] 0.005757
## t value Pr(>|t|)
## (Intercept) 0.538 0.590
## des2$variables$agew4[des2$variables$job_transition == 1] -0.594 0.552
##
## Residual standard error: 0.3778 on 1691 degrees of freedom
## Multiple R-squared: 0.0002089, Adjusted R-squared: -0.0003824
## F-statistic: 0.3533 on 1 and 1691 DF, p-value: 0.5523
##
##
## Response migraneb_yes :
##
## Call:
## lm(formula = migraneb_yes ~ des2$variables$agew4[des2$variables$job_transition ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.2013 -0.1705 -0.1531 -0.1324 0.9258
##
## Coefficients:
## Estimate
## (Intercept) 0.251954
## des2$variables$agew4[des2$variables$job_transition == 1] -0.008988
## Std. Error
## (Intercept) 0.158376
## des2$variables$agew4[des2$variables$job_transition == 1] 0.005532
## t value Pr(>|t|)
## (Intercept) 1.591 0.112
## des2$variables$agew4[des2$variables$job_transition == 1] -1.625 0.104
##
## Residual standard error: 0.363 on 1691 degrees of freedom
## Multiple R-squared: 0.001559, Adjusted R-squared: 0.0009681
## F-statistic: 2.64 on 1 and 1691 DF, p-value: 0.1044
##
##
## Response HIVb_yes :
##
## Call:
## lm(formula = HIVb_yes ~ des2$variables$agew4[des2$variables$job_transition ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.00502 -0.00233 -0.00180 -0.00111 0.99820
##
## Coefficients:
## Estimate
## (Intercept) -0.0130062
## des2$variables$agew4[des2$variables$job_transition == 1] 0.0004804
## Std. Error
## (Intercept) 0.0183592
## des2$variables$agew4[des2$variables$job_transition == 1] 0.0006413
## t value Pr(>|t|)
## (Intercept) -0.708 0.479
## des2$variables$agew4[des2$variables$job_transition == 1] 0.749 0.454
##
## Residual standard error: 0.04208 on 1691 degrees of freedom
## Multiple R-squared: 0.0003318, Adjusted R-squared: -0.0002594
## F-statistic: 0.5612 on 1 and 1691 DF, p-value: 0.4539
##
##
## Response threatened_gun_knifeW4b_yes :
##
## Call:
## lm(formula = threatened_gun_knifeW4b_yes ~ des2$variables$agew4[des2$variables$job_transition ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.4129 -0.1456 -0.1358 -0.1270 0.8856
##
## Coefficients:
## Estimate
## (Intercept) 0.150111
## des2$variables$agew4[des2$variables$job_transition == 1] -0.005156
## Std. Error
## (Intercept) 0.152121
## des2$variables$agew4[des2$variables$job_transition == 1] 0.005314
## t value Pr(>|t|)
## (Intercept) 0.987 0.324
## des2$variables$agew4[des2$variables$job_transition == 1] -0.970 0.332
##
## Residual standard error: 0.3487 on 1691 degrees of freedom
## Multiple R-squared: 0.0005564, Adjusted R-squared: -3.466e-05
## F-statistic: 0.9414 on 1 and 1691 DF, p-value: 0.3321
##
##
## Response forcedsex_physicalW4b_yes :
##
## Call:
## lm(formula = forcedsex_physicalW4b_yes ~ des2$variables$agew4[des2$variables$job_transition ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.13832 -0.10893 -0.09729 -0.08158 0.93535
##
## Coefficients:
## Estimate
## (Intercept) 0.281796
## des2$variables$agew4[des2$variables$job_transition == 1] -0.009795
## Std. Error
## (Intercept) 0.129982
## des2$variables$agew4[des2$variables$job_transition == 1] 0.004540
## t value Pr(>|t|)
## (Intercept) 2.168 0.0303
## des2$variables$agew4[des2$variables$job_transition == 1] -2.157 0.0311
##
## (Intercept) *
## des2$variables$agew4[des2$variables$job_transition == 1] *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2979 on 1691 degrees of freedom
## Multiple R-squared: 0.002745, Adjusted R-squared: 0.002155
## F-statistic: 4.654 on 1 and 1691 DF, p-value: 0.03112
##
##
## Response beaten_upW4b_yes :
##
## Call:
## lm(formula = beaten_upW4b_yes ~ des2$variables$agew4[des2$variables$job_transition ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.3694 -0.1201 -0.1098 -0.1012 0.9127
##
## Coefficients:
## Estimate
## (Intercept) 0.154538
## des2$variables$agew4[des2$variables$job_transition == 1] -0.005305
## Std. Error
## (Intercept) 0.139204
## des2$variables$agew4[des2$variables$job_transition == 1] 0.004862
## t value Pr(>|t|)
## (Intercept) 1.110 0.267
## des2$variables$agew4[des2$variables$job_transition == 1] -1.091 0.275
##
## Residual standard error: 0.3191 on 1691 degrees of freedom
## Multiple R-squared: 0.0007035, Adjusted R-squared: 0.0001126
## F-statistic: 1.19 on 1 and 1691 DF, p-value: 0.2754
##
##
## Response sex_abuse_bycaretakerW4b_>10_times :
##
## Call:
## lm(formula = `sex_abuse_bycaretakerW4b_>10_times` ~ des2$variables$agew4[des2$variables$job_transition ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.06862 -0.04318 -0.03643 -0.02876 0.97804
##
## Coefficients:
## Estimate
## (Intercept) 0.133504
## des2$variables$agew4[des2$variables$job_transition == 1] -0.004806
## Std. Error
## (Intercept) 0.083201
## des2$variables$agew4[des2$variables$job_transition == 1] 0.002906
## t value Pr(>|t|)
## (Intercept) 1.605 0.1088
## des2$variables$agew4[des2$variables$job_transition == 1] -1.654 0.0984
##
## (Intercept)
## des2$variables$agew4[des2$variables$job_transition == 1] .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1907 on 1691 degrees of freedom
## Multiple R-squared: 0.001614, Adjusted R-squared: 0.001024
## F-statistic: 2.734 on 1 and 1691 DF, p-value: 0.09839
##
##
## Response sex_abuse_bycaretakerW4c_10+_times :
##
## Call:
## lm(formula = `sex_abuse_bycaretakerW4c_10+_times` ~ des2$variables$agew4[des2$variables$job_transition ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.02639 -0.01945 -0.01615 -0.01370 0.99124
##
## Coefficients:
## Estimate
## (Intercept) 0.060346
## des2$variables$agew4[des2$variables$job_transition == 1] -0.002049
## Std. Error
## (Intercept) 0.055644
## des2$variables$agew4[des2$variables$job_transition == 1] 0.001944
## t value Pr(>|t|)
## (Intercept) 1.085 0.278
## des2$variables$agew4[des2$variables$job_transition == 1] -1.054 0.292
##
## Residual standard error: 0.1275 on 1691 degrees of freedom
## Multiple R-squared: 0.0006567, Adjusted R-squared: 6.572e-05
## F-statistic: 1.111 on 1 and 1691 DF, p-value: 0.292
##
##
## Response alcohol_day_permonthW4b_1or2days/week :
##
## Call:
## lm(formula = `alcohol_day_permonthW4b_1or2days/week` ~ des2$variables$agew4[des2$variables$job_transition ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.2677 -0.2535 -0.2465 -0.1974 0.8040
##
## Coefficients:
## Estimate
## (Intercept) 0.036818
## des2$variables$agew4[des2$variables$job_transition == 1] -0.001402
## Std. Error
## (Intercept) 0.187726
## des2$variables$agew4[des2$variables$job_transition == 1] 0.006557
## t value Pr(>|t|)
## (Intercept) 0.196 0.845
## des2$variables$agew4[des2$variables$job_transition == 1] -0.214 0.831
##
## Residual standard error: 0.4303 on 1691 degrees of freedom
## Multiple R-squared: 2.704e-05, Adjusted R-squared: -0.0005643
## F-statistic: 0.04572 on 1 and 1691 DF, p-value: 0.8307
##
##
## Response alcohol_day_permonthW4c_3to5days/week :
##
## Call:
## lm(formula = `alcohol_day_permonthW4c_3to5days/week` ~ des2$variables$agew4[des2$variables$job_transition ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.1835 -0.1288 -0.1174 -0.1043 0.9194
##
## Coefficients:
## Estimate
## (Intercept) 0.188096
## des2$variables$agew4[des2$variables$job_transition == 1] -0.006707
## Std. Error
## (Intercept) 0.142036
## des2$variables$agew4[des2$variables$job_transition == 1] 0.004961
## t value Pr(>|t|)
## (Intercept) 1.324 0.186
## des2$variables$agew4[des2$variables$job_transition == 1] -1.352 0.177
##
## Residual standard error: 0.3255 on 1691 degrees of freedom
## Multiple R-squared: 0.00108, Adjusted R-squared: 0.0004888
## F-statistic: 1.827 on 1 and 1691 DF, p-value: 0.1766
##
##
## Response alcohol_day_permonthW4d_daily :
##
## Call:
## lm(formula = alcohol_day_permonthW4d_daily ~ des2$variables$agew4[des2$variables$job_transition ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.06954 -0.04505 -0.03929 -0.03294 0.99706
##
## Coefficients:
## Estimate
## (Intercept) -0.144710
## des2$variables$agew4[des2$variables$job_transition == 1] 0.004758
## Std. Error
## (Intercept) 0.084419
## des2$variables$agew4[des2$variables$job_transition == 1] 0.002949
## t value Pr(>|t|)
## (Intercept) -1.714 0.0867
## des2$variables$agew4[des2$variables$job_transition == 1] 1.614 0.1068
##
## (Intercept) .
## des2$variables$agew4[des2$variables$job_transition == 1]
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1935 on 1691 degrees of freedom
## Multiple R-squared: 0.001537, Adjusted R-squared: 0.0009468
## F-statistic: 2.603 on 1 and 1691 DF, p-value: 0.1068
##
##
## Response try.cocaineW4b_yes :
##
## Call:
## lm(formula = try.cocaineW4b_yes ~ des2$variables$agew4[des2$variables$job_transition ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.3636 -0.2833 -0.2758 0.7072 0.7393
##
## Coefficients:
## Estimate
## (Intercept) -0.143151
## des2$variables$agew4[des2$variables$job_transition == 1] 0.003166
## Std. Error
## (Intercept) 0.196236
## des2$variables$agew4[des2$variables$job_transition == 1] 0.006855
## t value Pr(>|t|)
## (Intercept) -0.729 0.466
## des2$variables$agew4[des2$variables$job_transition == 1] 0.462 0.644
##
## Residual standard error: 0.4498 on 1691 degrees of freedom
## Multiple R-squared: 0.0001261, Adjusted R-squared: -0.0004652
## F-statistic: 0.2133 on 1 and 1691 DF, p-value: 0.6443
##
##
## Response try.methW4b_yes :
##
## Call:
## lm(formula = try.methW4b_yes ~ des2$variables$agew4[des2$variables$job_transition ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.1966 -0.1413 -0.1343 -0.1247 0.8909
##
## Coefficients:
## Estimate
## (Intercept) 0.131102
## des2$variables$agew4[des2$variables$job_transition == 1] -0.005212
## Std. Error
## (Intercept) 0.149936
## des2$variables$agew4[des2$variables$job_transition == 1] 0.005237
## t value Pr(>|t|)
## (Intercept) 0.874 0.382
## des2$variables$agew4[des2$variables$job_transition == 1] -0.995 0.320
##
## Residual standard error: 0.3437 on 1691 degrees of freedom
## Multiple R-squared: 0.0005852, Adjusted R-squared: -5.783e-06
## F-statistic: 0.9902 on 1 and 1691 DF, p-value: 0.3198
##
##
## Response try.herionW4b_yes :
##
## Call:
## lm(formula = try.herionW4b_yes ~ des2$variables$agew4[des2$variables$job_transition ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.3829 -0.3307 -0.3081 0.6553 0.7620
##
## Coefficients:
## Estimate
## (Intercept) -0.330558
## des2$variables$agew4[des2$variables$job_transition == 1] 0.009875
## Std. Error
## (Intercept) 0.203293
## des2$variables$agew4[des2$variables$job_transition == 1] 0.007101
## t value Pr(>|t|)
## (Intercept) -1.626 0.104
## des2$variables$agew4[des2$variables$job_transition == 1] 1.391 0.165
##
## Residual standard error: 0.4659 on 1691 degrees of freedom
## Multiple R-squared: 0.001142, Adjusted R-squared: 0.0005516
## F-statistic: 1.934 on 1 and 1691 DF, p-value: 0.1645#Test the fit of model. Grambsch and Therneau formal test using weighted residuals
fit.test<-cox.zph(fit1g)
fit.test## rho chisq p
## sexb_female -0.012030 7.06e-01 4.01e-01
## racethnicb-nhblack -0.033637 8.72e+00 3.15e-03
## racethnicc-hispanic 0.008263 4.04e-01 5.25e-01
## racethnicd-asian 0.068652 1.92e+01 1.15e-05
## racethnice-native_american -0.038947 8.37e+00 3.80e-03
## racethnicf-other -0.002403 3.56e-02 8.50e-01
## sexorientb_bisexual 0.000314 5.06e-04 9.82e-01
## sexorientc_LGB 0.015246 1.00e+00 3.17e-01
## educb_highschool_grad -0.013530 7.09e-01 4.00e-01
## educc_college_bach -0.011443 6.24e-01 4.29e-01
## educd_college+ 0.017618 1.50e+00 2.21e-01
## incomeW4b_$15,000<$30,000 -0.032176 4.80e+00 2.84e-02
## incomeW4c_$30,000<$50,000 -0.029481 3.42e+00 6.43e-02
## incomeW4d_$50,000<$75,000 -0.036664 5.36e+00 2.06e-02
## incomeW4e_$75,000<$100,000 -0.033227 4.49e+00 3.40e-02
## incomeW4f_$100,000<$150,000 -0.036711 6.40e+00 1.14e-02
## incomeW4g_$150,000+ -0.011491 5.57e-01 4.56e-01
## pregnancyb_yes -0.005132 1.65e-01 6.85e-01
## birthsb_yes -0.018204 1.95e+00 1.63e-01
## birthsskip 0.003065 5.21e-02 8.19e-01
## foster_homeb_yes 0.025257 4.02e+00 4.49e-02
## marriedW4ab_married 0.004865 1.09e-01 7.41e-01
## marriedW4bb_seperated/divorced -0.003764 7.10e-02 7.90e-01
## marriedW4bc_skip -0.042502 9.47e+00 2.09e-03
## insurance_statusW4b_yes_insurance 0.069811 2.94e+01 5.97e-08
## unmet_medcareW4b_yes 0.006492 2.34e-01 6.29e-01
## incarceratedb_yes 0.035226 5.54e+00 1.86e-02
## incarceratedskip -0.003817 8.15e-02 7.75e-01
## general_healthb_poor/bad -0.036937 6.92e+00 8.51e-03
## social_isolationW4b_sometimes/often 0.072194 2.56e+01 4.30e-07
## things.going.mywayW4b_sometimes -0.031517 5.58e+00 1.82e-02
## things.going.mywayW4c_often -0.009891 4.35e-01 5.09e-01
## depressionW4b_yes 0.026347 3.69e+00 5.48e-02
## suicideW4thinkb_yes -0.053326 1.07e+01 1.07e-03
## physical_limitationb_limited -0.021806 2.17e+00 1.41e-01
## cancerb_yes -0.044791 1.03e+01 1.34e-03
## high_bpb_yes 0.029877 4.98e+00 2.57e-02
## high_cholb_yes 0.013532 8.08e-01 3.69e-01
## diabetesb_yes 0.050937 1.23e+01 4.60e-04
## heart_diseaseb_yes 0.027586 2.28e+00 1.31e-01
## asthmb_yes 0.001638 1.27e-02 9.10e-01
## migraneb_yes -0.015680 1.35e+00 2.46e-01
## HIVb_yes -0.012109 5.35e-01 4.64e-01
## threatened_gun_knifeW4b_yes 0.002450 3.78e-02 8.46e-01
## forcedsex_physicalW4b_yes 0.020920 1.89e+00 1.69e-01
## beaten_upW4b_yes 0.004128 9.69e-02 7.56e-01
## sex_abuse_bycaretakerW4b_>10_times -0.016544 1.18e+00 2.77e-01
## sex_abuse_bycaretakerW4c_10+_times 0.021506 2.50e+00 1.14e-01
## alcohol_day_permonthW4b_1or2days/week -0.014147 1.23e+00 2.68e-01
## alcohol_day_permonthW4c_3to5days/week 0.022555 2.11e+00 1.47e-01
## alcohol_day_permonthW4d_daily 0.016541 9.97e-01 3.18e-01
## try.cocaineW4b_yes 0.021679 2.74e+00 9.76e-02
## try.methW4b_yes 0.004436 1.01e-01 7.51e-01
## try.herionW4b_yes -0.039261 7.41e+00 6.50e-03
## GLOBAL NA 1.39e+02 1.98e-09#plot of residuals
par(mfrow=c(3,3))
plot(fit.test,df=2)
par(mfrow=c(1,1))#martingale residual
res.mar<-resid(fit1g,type = "martingale")
scatter.smooth(des2$variables$sexorient,res.mar,degree = 2,span = 1)## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## FALSE, : pseudoinverse used at 0.99## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## FALSE, : neighborhood radius 2.01## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## FALSE, : reciprocal condition number 0## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## FALSE, : There are other near singularities as well. 4.0401## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## FALSE, : pseudoinverse used at 0.99## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## FALSE, : neighborhood radius 2.01## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## FALSE, : reciprocal condition number 0## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## FALSE, : There are other near singularities as well. 4.0401## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## FALSE, : pseudoinverse used at 0.99## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## FALSE, : neighborhood radius 2.01## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## FALSE, : reciprocal condition number 0## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## FALSE, : There are other near singularities as well. 4.0401## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## FALSE, : pseudoinverse used at 0.99## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## FALSE, : neighborhood radius 2.01## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## FALSE, : reciprocal condition number 0## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## FALSE, : There are other near singularities as well. 4.0401## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## FALSE, : pseudoinverse used at 0.99## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## FALSE, : neighborhood radius 2.01## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## FALSE, : reciprocal condition number 0## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## FALSE, : There are other near singularities as well. 4.0401
#stratify by orientation
fit1g.strat<-svycoxph(Surv(time=agew3,time2=agew4,event=job_transition)~strata(sexorient)+sex+racethnic+educ+incomeW4+pregnancy+births+foster_home+marriedW4a+marriedW4b+insurance_statusW4+unmet_medcareW4+incarcerated+general_health+social_isolationW4+things.going.mywayW4+depressionW4+suicideW4think+physical_limitation+cancer+high_bp+high_chol+diabetes+heart_disease+asthm+migrane+HIV+threatened_gun_knifeW4+forcedsex_physicalW4+beaten_upW4+sex_abuse_bycaretakerW4+alcohol_day_permonthW4+try.cocaineW4+try.methW4+try.herionW4,design=des2)
summary(fit1g)## Stratified 1 - level Cluster Sampling design (with replacement)
## With (130) clusters.
## svydesign(ids = ~psuscid, strata = ~region, weights = ~GSWGT4,
## data = addhealth, nest = T)## Call:
## svycoxph(formula = Surv(time = agew3, time2 = agew4, event = job_transition) ~
## sex + racethnic + sexorient + educ + incomeW4 + pregnancy +
## births + foster_home + marriedW4a + marriedW4b + insurance_statusW4 +
## unmet_medcareW4 + incarcerated + general_health + social_isolationW4 +
## things.going.mywayW4 + depressionW4 + suicideW4think +
## physical_limitation + cancer + high_bp + high_chol +
## diabetes + heart_disease + asthm + migrane + HIV + threatened_gun_knifeW4 +
## forcedsex_physicalW4 + beaten_upW4 + sex_abuse_bycaretakerW4 +
## alcohol_day_permonthW4 + try.cocaineW4 + try.methW4 +
## try.herionW4, design = des2)
##
## n= 6065, number of events= 1693
##
## coef exp(coef) se(coef) z
## sexb_female -0.130391 0.877752 0.070606 -1.847
## racethnicb-nhblack 0.147821 1.159305 0.117438 1.259
## racethnicc-hispanic -0.086811 0.916851 0.156839 -0.554
## racethnicd-asian -0.203657 0.815742 0.136945 -1.487
## racethnice-native_american 0.311910 1.366031 0.381457 0.818
## racethnicf-other 0.241930 1.273706 0.417471 0.580
## sexorientb_bisexual 0.424368 1.528624 0.203621 2.084
## sexorientc_LGB -0.513684 0.598287 0.252170 -2.037
## educb_highschool_grad -0.057934 0.943712 0.106841 -0.542
## educc_college_bach -0.393456 0.674721 0.150865 -2.608
## educd_college+ -1.301084 0.272237 0.202378 -6.429
## incomeW4b_$15,000<$30,000 0.071578 1.074202 0.225494 0.317
## incomeW4c_$30,000<$50,000 -0.142900 0.866840 0.218534 -0.654
## incomeW4d_$50,000<$75,000 -0.362505 0.695931 0.227371 -1.594
## incomeW4e_$75,000<$100,000 -0.338210 0.713046 0.218286 -1.549
## incomeW4f_$100,000<$150,000 -0.576720 0.561738 0.254121 -2.269
## incomeW4g_$150,000+ -0.544870 0.579917 0.265716 -2.051
## pregnancyb_yes -0.166648 0.846498 0.101719 -1.638
## birthsb_yes -0.305357 0.736861 0.119671 -2.552
## birthsskip -0.010238 0.989815 0.112149 -0.091
## foster_homeb_yes -0.392916 0.675086 0.263127 -1.493
## marriedW4ab_married 0.337182 1.400994 0.129448 2.605
## marriedW4bb_seperated/divorced -0.037515 0.963180 0.193694 -0.194
## marriedW4bc_skip 0.034226 1.034818 0.136067 0.252
## insurance_statusW4b_yes_insurance -0.198228 0.820182 0.082619 -2.399
## unmet_medcareW4b_yes 0.024900 1.025212 0.078013 0.319
## incarceratedb_yes -0.043464 0.957467 0.103064 -0.422
## incarceratedskip -0.226337 0.797449 0.087219 -2.595
## general_healthb_poor/bad -0.049484 0.951721 0.116621 -0.424
## social_isolationW4b_sometimes/often 0.031285 1.031780 0.068893 0.454
## things.going.mywayW4b_sometimes -0.181113 0.834341 0.108337 -1.672
## things.going.mywayW4c_often -0.270520 0.762983 0.109969 -2.460
## depressionW4b_yes 0.036897 1.037587 0.095555 0.386
## suicideW4thinkb_yes -0.020160 0.980042 0.108581 -0.186
## physical_limitationb_limited 0.165053 1.179456 0.125272 1.318
## cancerb_yes 0.389752 1.476615 0.270877 1.439
## high_bpb_yes 0.009657 1.009704 0.115283 0.084
## high_cholb_yes 0.032901 1.033449 0.100670 0.327
## diabetesb_yes -0.063213 0.938743 0.200351 -0.316
## heart_diseaseb_yes -0.108977 0.896751 0.265839 -0.410
## asthmb_yes 0.194792 1.215059 0.080260 2.427
## migraneb_yes 0.156354 1.169240 0.099938 1.565
## HIVb_yes 0.659842 1.934487 0.711842 0.927
## threatened_gun_knifeW4b_yes -0.072144 0.930397 0.159115 -0.453
## forcedsex_physicalW4b_yes 0.115355 1.122272 0.107814 1.070
## beaten_upW4b_yes 0.070952 1.073529 0.158447 0.448
## sex_abuse_bycaretakerW4b_>10_times 0.052262 1.053652 0.153237 0.341
## sex_abuse_bycaretakerW4c_10+_times 0.082059 1.085520 0.232424 0.353
## alcohol_day_permonthW4b_1or2days/week -0.011308 0.988756 0.087053 -0.130
## alcohol_day_permonthW4c_3to5days/week -0.100543 0.904346 0.098232 -1.024
## alcohol_day_permonthW4d_daily 0.087205 1.091120 0.143236 0.609
## try.cocaineW4b_yes 0.019990 1.020191 0.118370 0.169
## try.methW4b_yes 0.063723 1.065797 0.112073 0.569
## try.herionW4b_yes 0.243196 1.275319 0.087458 2.781
## Pr(>|z|)
## sexb_female 0.06479 .
## racethnicb-nhblack 0.20814
## racethnicc-hispanic 0.57992
## racethnicd-asian 0.13698
## racethnice-native_american 0.41354
## racethnicf-other 0.56224
## sexorientb_bisexual 0.03715 *
## sexorientc_LGB 0.04164 *
## educb_highschool_grad 0.58765
## educc_college_bach 0.00911 **
## educd_college+ 1.28e-10 ***
## incomeW4b_$15,000<$30,000 0.75092
## incomeW4c_$30,000<$50,000 0.51317
## incomeW4d_$50,000<$75,000 0.11086
## incomeW4e_$75,000<$100,000 0.12129
## incomeW4f_$100,000<$150,000 0.02324 *
## incomeW4g_$150,000+ 0.04031 *
## pregnancyb_yes 0.10135
## birthsb_yes 0.01072 *
## birthsskip 0.92727
## foster_homeb_yes 0.13537
## marriedW4ab_married 0.00919 **
## marriedW4bb_seperated/divorced 0.84643
## marriedW4bc_skip 0.80140
## insurance_statusW4b_yes_insurance 0.01643 *
## unmet_medcareW4b_yes 0.74959
## incarceratedb_yes 0.67323
## incarceratedskip 0.00946 **
## general_healthb_poor/bad 0.67134
## social_isolationW4b_sometimes/often 0.64975
## things.going.mywayW4b_sometimes 0.09457 .
## things.going.mywayW4c_often 0.01390 *
## depressionW4b_yes 0.69939
## suicideW4thinkb_yes 0.85271
## physical_limitationb_limited 0.18765
## cancerb_yes 0.15019
## high_bpb_yes 0.93324
## high_cholb_yes 0.74380
## diabetesb_yes 0.75237
## heart_diseaseb_yes 0.68185
## asthmb_yes 0.01522 *
## migraneb_yes 0.11770
## HIVb_yes 0.35395
## threatened_gun_knifeW4b_yes 0.65026
## forcedsex_physicalW4b_yes 0.28465
## beaten_upW4b_yes 0.65430
## sex_abuse_bycaretakerW4b_>10_times 0.73306
## sex_abuse_bycaretakerW4c_10+_times 0.72404
## alcohol_day_permonthW4b_1or2days/week 0.89665
## alcohol_day_permonthW4c_3to5days/week 0.30606
## alcohol_day_permonthW4d_daily 0.54264
## try.cocaineW4b_yes 0.86589
## try.methW4b_yes 0.56964
## try.herionW4b_yes 0.00542 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95
## sexb_female 0.8778 1.1393 0.7643
## racethnicb-nhblack 1.1593 0.8626 0.9209
## racethnicc-hispanic 0.9169 1.0907 0.6742
## racethnicd-asian 0.8157 1.2259 0.6237
## racethnice-native_american 1.3660 0.7320 0.6468
## racethnicf-other 1.2737 0.7851 0.5620
## sexorientb_bisexual 1.5286 0.6542 1.0256
## sexorientc_LGB 0.5983 1.6714 0.3650
## educb_highschool_grad 0.9437 1.0596 0.7654
## educc_college_bach 0.6747 1.4821 0.5020
## educd_college+ 0.2722 3.6733 0.1831
## incomeW4b_$15,000<$30,000 1.0742 0.9309 0.6905
## incomeW4c_$30,000<$50,000 0.8668 1.1536 0.5648
## incomeW4d_$50,000<$75,000 0.6959 1.4369 0.4457
## incomeW4e_$75,000<$100,000 0.7130 1.4024 0.4648
## incomeW4f_$100,000<$150,000 0.5617 1.7802 0.3414
## incomeW4g_$150,000+ 0.5799 1.7244 0.3445
## pregnancyb_yes 0.8465 1.1813 0.6935
## birthsb_yes 0.7369 1.3571 0.5828
## birthsskip 0.9898 1.0103 0.7945
## foster_homeb_yes 0.6751 1.4813 0.4031
## marriedW4ab_married 1.4010 0.7138 1.0871
## marriedW4bb_seperated/divorced 0.9632 1.0382 0.6589
## marriedW4bc_skip 1.0348 0.9664 0.7926
## insurance_statusW4b_yes_insurance 0.8202 1.2192 0.6976
## unmet_medcareW4b_yes 1.0252 0.9754 0.8799
## incarceratedb_yes 0.9575 1.0444 0.7823
## incarceratedskip 0.7974 1.2540 0.6721
## general_healthb_poor/bad 0.9517 1.0507 0.7573
## social_isolationW4b_sometimes/often 1.0318 0.9692 0.9015
## things.going.mywayW4b_sometimes 0.8343 1.1986 0.6747
## things.going.mywayW4c_often 0.7630 1.3106 0.6150
## depressionW4b_yes 1.0376 0.9638 0.8604
## suicideW4thinkb_yes 0.9800 1.0204 0.7922
## physical_limitationb_limited 1.1795 0.8478 0.9227
## cancerb_yes 1.4766 0.6772 0.8684
## high_bpb_yes 1.0097 0.9904 0.8055
## high_cholb_yes 1.0334 0.9676 0.8484
## diabetesb_yes 0.9387 1.0653 0.6339
## heart_diseaseb_yes 0.8968 1.1151 0.5326
## asthmb_yes 1.2151 0.8230 1.0382
## migraneb_yes 1.1692 0.8553 0.9612
## HIVb_yes 1.9345 0.5169 0.4793
## threatened_gun_knifeW4b_yes 0.9304 1.0748 0.6811
## forcedsex_physicalW4b_yes 1.1223 0.8910 0.9085
## beaten_upW4b_yes 1.0735 0.9315 0.7869
## sex_abuse_bycaretakerW4b_>10_times 1.0537 0.9491 0.7803
## sex_abuse_bycaretakerW4c_10+_times 1.0855 0.9212 0.6883
## alcohol_day_permonthW4b_1or2days/week 0.9888 1.0114 0.8337
## alcohol_day_permonthW4c_3to5days/week 0.9043 1.1058 0.7460
## alcohol_day_permonthW4d_daily 1.0911 0.9165 0.8240
## try.cocaineW4b_yes 1.0202 0.9802 0.8090
## try.methW4b_yes 1.0658 0.9383 0.8556
## try.herionW4b_yes 1.2753 0.7841 1.0744
## upper .95
## sexb_female 1.0080
## racethnicb-nhblack 1.4594
## racethnicc-hispanic 1.2468
## racethnicd-asian 1.0669
## racethnice-native_american 2.8851
## racethnicf-other 2.8868
## sexorientb_bisexual 2.2784
## sexorientc_LGB 0.9807
## educb_highschool_grad 1.1635
## educc_college_bach 0.9069
## educd_college+ 0.4048
## incomeW4b_$15,000<$30,000 1.6712
## incomeW4c_$30,000<$50,000 1.3303
## incomeW4d_$50,000<$75,000 1.0867
## incomeW4e_$75,000<$100,000 1.0938
## incomeW4f_$100,000<$150,000 0.9244
## incomeW4g_$150,000+ 0.9762
## pregnancyb_yes 1.0333
## birthsb_yes 0.9316
## birthsskip 1.2331
## foster_homeb_yes 1.1307
## marriedW4ab_married 1.8056
## marriedW4bb_seperated/divorced 1.4079
## marriedW4bc_skip 1.3511
## insurance_statusW4b_yes_insurance 0.9644
## unmet_medcareW4b_yes 1.1946
## incarceratedb_yes 1.1718
## incarceratedskip 0.9461
## general_healthb_poor/bad 1.1961
## social_isolationW4b_sometimes/often 1.1809
## things.going.mywayW4b_sometimes 1.0317
## things.going.mywayW4c_often 0.9465
## depressionW4b_yes 1.2513
## suicideW4thinkb_yes 1.2125
## physical_limitationb_limited 1.5077
## cancerb_yes 2.5110
## high_bpb_yes 1.2657
## high_cholb_yes 1.2589
## diabetesb_yes 1.3902
## heart_diseaseb_yes 1.5099
## asthmb_yes 1.4220
## migraneb_yes 1.4222
## HIVb_yes 7.8071
## threatened_gun_knifeW4b_yes 1.2709
## forcedsex_physicalW4b_yes 1.3863
## beaten_upW4b_yes 1.4645
## sex_abuse_bycaretakerW4b_>10_times 1.4228
## sex_abuse_bycaretakerW4c_10+_times 1.7119
## alcohol_day_permonthW4b_1or2days/week 1.1727
## alcohol_day_permonthW4c_3to5days/week 1.0964
## alcohol_day_permonthW4d_daily 1.4448
## try.cocaineW4b_yes 1.2866
## try.methW4b_yes 1.3276
## try.herionW4b_yes 1.5138
##
## Concordance= 0.692 (se = 0.009 )
## Likelihood ratio test= NA on 54 df, p=NA
## Wald test = 757.5 on 54 df, p=<2e-16
## Score (logrank) test = NA on 54 df, p=NA