Heller title: “Phar 7380 week11_4 doses_cor1” output: html_document:
default word_document: default Nov 8, 2024
— #libraries
library(ggplot2)
library(dplyr)
library(knitr)#theme
my_theme<-function(x){theme_bw()+
theme(text = element_text(size=20))+
theme(axis.line.y = element_line(size = 2.0))+
theme(axis.line.x = element_line(size = 2.0))+
theme(axis.ticks = element_line(size = 1.5,colour="black"))+
theme(axis.ticks.length= unit(0.45, "cm"))+
theme(axis.title.y =element_text(vjust=1.2))+
theme(axis.title.x =element_text(vjust=-0.2))+
theme(axis.text=element_text(colour="black"))+
theme(panel.background = element_rect(fill ="white"))}#0.5 mg dose
sim0.5<-read.table("C:\\Heller\\PHAR7380\\week11\\SDINDOUT1.res",skip=1,header=T)
#sim0.5<-read.table("/work/ac0837/PHAR7380/week11/indirect1.dir1/NM_run1/SDINDOUT1.res",skip=1,header=T)
sim0.5sum<-sim0.5%>%filter(CMT==4)%>%group_by(TIME)%>%summarise(cmed=median(IPRED),cu975=quantile(IPRED,0.975),cu025=quantile(IPRED,0.025))#plots for 0.5 mg dose
ggplot(data=sim0.5%>%filter(CMT==4),aes(TIME/(7*24),IPRED*1000,group=ID))+
geom_line(size=0.5)+
scale_x_continuous(limits = c(0,6))+
theme_bw()+
my_theme()+
labs(x="Time after dose (week)",y="CD4 count/mL")## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## i Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.
## i Please use the `linewidth` argument instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
#median and 95% CI
ggplot(data=sim0.5sum,aes(TIME/(24*7),cmed*1000))+
geom_line(size=0.5)+
geom_line(data=sim0.5sum,aes(TIME/(24*7),cu975*1000),size=0.5,linetype="dashed")+
geom_line(data=sim0.5sum,aes(TIME/(24*7),cu025*1000),size=0.5,linetype="dashed")+
scale_x_continuous(limits = c(0,6))+
theme_bw()+
my_theme()+
labs(x="Time after dose (week)",y="CD4 count/mL")
#CD4 suppression at any time post-dose is defined as ≥ 10% of subjects
with CD4 counts < 80% of pre-dose values. You need to interpret the
table for the criteria of CD4 suppression. Percent column is the percent
of subjects with <80% counts of pre-dose values.
sim0.5%>%
select(ID,TIME,CMT,IPRED,BASE)%>%
group_by(TIME)%>%
filter(CMT==4)%>%
mutate(suppression=(IPRED/BASE)*100)%>%
filter(suppression<80)%>%
summarise(percent=n_distinct(ID))%>%kable| TIME | percent |
|---|---|
| 0.5 | 40 |
| 1.0 | 98 |
| 1.5 | 100 |
| 2.0 | 100 |
| 3.0 | 100 |
| 4.0 | 100 |
| 6.0 | 100 |
| 8.0 | 100 |
| 10.0 | 100 |
| 12.0 | 100 |
| 18.0 | 100 |
| 24.0 | 100 |
| 36.0 | 100 |
| 48.0 | 100 |
| 60.0 | 100 |
| 72.0 | 100 |
| 96.0 | 100 |
| 120.0 | 100 |
| 144.0 | 100 |
| 168.0 | 100 |
| 336.0 | 96 |
| 504.0 | 65 |
| 672.0 | 34 |
| 840.0 | 13 |
| 1008.0 | 7 |
#0.1 mg dose results
sim0.1<-read.table("C:\\Heller\\PHAR7380\\week11\\SDINDOUT2.res",skip=1,header=T)
#sim0.1<-read.table("/work/ac0837/PHAR7380/week11/indirect2.dir1/NM_run1/SDINDOUT2.res",skip=1,header=T)
sim0.1sum<-sim0.1%>%filter(CMT==4)%>%group_by(TIME)%>%summarise(cmed=median(IPRED),cu975=quantile(IPRED,0.975),cu025=quantile(IPRED,0.025))#plots for 0.1 mg
ggplot(data=sim0.1%>%filter(CMT==4),aes(TIME/(7*24),IPRED*1000,group=ID))+
geom_line(size=0.5)+
scale_x_continuous(limits = c(0,6))+
theme_bw()+
my_theme()+
labs(x="Time after dose (week)",y="CD4 count/mL")
#median and 95% CI
ggplot(data=sim0.1sum,aes(TIME/(24*7),cmed*1000))+
geom_line(size=0.5)+
geom_line(data=sim0.1sum,aes(TIME/(24*7),cu975*1000),size=0.5,linetype="dashed")+
geom_line(data=sim0.1sum,aes(TIME/(24*7),cu025*1000),size=0.5,linetype="dashed")+
scale_x_continuous(limits = c(0,6))+
theme_bw()+
my_theme()+
labs(x="Time after dose (week)",y="CD4 count/mL")
#CD4 suppression at any time post-dose is defined as ≥ 10% of subjects with CD4 counts < 80% of pre-dose values. You need to interpret the table for the criteria of CD4 suppression. Percent column is the percent of subjects with <80% counts of pre-dose values.
sim0.1%>%
select(ID,TIME,CMT,IPRED,BASE)%>%
group_by(TIME)%>%
filter(CMT==4)%>%
mutate(suppression=(IPRED/BASE)*100)%>%
filter(suppression<80)%>%
summarise(percent=n_distinct(ID))%>%kable| TIME | percent |
|---|---|
| 1.0 | 24 |
| 1.5 | 55 |
| 2.0 | 79 |
| 3.0 | 94 |
| 4.0 | 97 |
| 6.0 | 98 |
| 8.0 | 98 |
| 10.0 | 98 |
| 12.0 | 98 |
| 18.0 | 98 |
| 24.0 | 98 |
| 36.0 | 98 |
| 48.0 | 98 |
| 60.0 | 97 |
| 72.0 | 97 |
| 96.0 | 95 |
| 120.0 | 91 |
| 144.0 | 88 |
| 168.0 | 80 |
| 336.0 | 29 |
| 504.0 | 3 |
#0.05 mg dose results
sim0.05<-read.table("C:\\Heller\\PHAR7380\\week11\\SDINDOUT3.res",skip=1,header=T)
#sim0.1<-read.table("/work/ac0837/PHAR7380/week11/indirect2.dir1/NM_run1/SDINDOUT2.res",skip=1,header=T)
sim0.05sum<-sim0.05%>%filter(CMT==4)%>%group_by(TIME)%>%summarise(cmed=median(IPRED),cu975=quantile(IPRED,0.975),cu025=quantile(IPRED,0.025))#plots for 0.05 mg
ggplot(data=sim0.05%>%filter(CMT==4),aes(TIME/(7*24),IPRED*1000,group=ID))+
geom_line(size=0.5)+
scale_x_continuous(limits = c(0,6))+
theme_bw()+
my_theme()+
labs(x="Time after dose (week)",y="CD4 count/mL")
#median and 95% CI
ggplot(data=sim0.05sum,aes(TIME/(24*7),cmed*1000))+
geom_line(size=0.5)+
geom_line(data=sim0.05sum,aes(TIME/(24*7),cu975*1000),size=0.5,linetype="dashed")+
geom_line(data=sim0.05sum,aes(TIME/(24*7),cu025*1000),size=0.5,linetype="dashed")+
scale_x_continuous(limits = c(0,6))+
theme_bw()+
my_theme()+
labs(x="Time after dose (week)",y="CD4 count/mL")
#CD4 suppression at any time post-dose is defined as ≥ 10% of subjects
with CD4 counts < 80% of pre-dose values. You need to interpret the
table for the criteria of CD4 suppression. Percent column is the percent
of subjects with <80% counts of pre-dose values.
sim0.05%>%
select(ID,TIME,CMT,IPRED,BASE)%>%
group_by(TIME)%>%
filter(CMT==4)%>%
mutate(suppression=(IPRED/BASE)*100)%>%
filter(suppression<80)%>%
summarise(percent=n_distinct(ID))%>%kable| TIME | percent |
|---|---|
| 1.5 | 17 |
| 2.0 | 31 |
| 3.0 | 54 |
| 4.0 | 67 |
| 6.0 | 74 |
| 8.0 | 81 |
| 10.0 | 82 |
| 12.0 | 82 |
| 18.0 | 83 |
| 24.0 | 81 |
| 36.0 | 78 |
| 48.0 | 76 |
| 60.0 | 66 |
| 72.0 | 63 |
| 96.0 | 57 |
| 120.0 | 49 |
| 144.0 | 35 |
| 168.0 | 28 |
| 336.0 | 1 |
#0.01 mg dose results
sim0.01<-read.table("C:\\Heller\\PHAR7380\\week11\\SDINDOUT4.res",skip=1,header=T)
#sim0.01<-read.table("/work/ac0837/PHAR7380/week11/indirect1.dir1/NM_run1/SDINDOUT1.res",skip=1,header=T)
sim0.01sum<-sim0.01%>%filter(CMT==4)%>%group_by(TIME)%>%summarise(cmed=median(IPRED),cu975=quantile(IPRED,0.975),cu025=quantile(IPRED,0.025))#plots for 0.01 mg
ggplot(data=sim0.01%>%filter(CMT==4),aes(TIME/(7*24),IPRED*1000,group=ID))+
geom_line(size=0.5)+
scale_x_continuous(limits = c(0,6))+
theme_bw()+
my_theme()+
labs(x="Time after dose (week)",y="CD4 count/mL")
#median and 95% CI
ggplot(data=sim0.01sum,aes(TIME/(24*7),cmed*1000))+
geom_line(size=0.5)+
geom_line(data=sim0.01sum,aes(TIME/(24*7),cu975*1000),size=0.5,linetype="dashed")+
geom_line(data=sim0.01sum,aes(TIME/(24*7),cu025*1000),size=0.5,linetype="dashed")+
scale_x_continuous(limits = c(0,6))+
theme_bw()+
my_theme()+
labs(x="Time after dose (week)",y="CD4 count/mL")
#CD4 suppression at any time post-dose is defined as ≥ 10% of subjects
with CD4 counts < 80% of pre-dose values. You need to interpret the
table for the criteria of CD4 suppression. Percent column is the percent
of subjects with <80% counts of pre-dose values.
sim0.01%>%
select(ID,TIME,CMT,IPRED,BASE)%>%
group_by(TIME)%>%
filter(CMT==4)%>%
mutate(suppression=(IPRED/BASE)*100)%>%
filter(suppression<80)%>%
summarise(percent=n_distinct(ID))%>%kable| TIME | percent |
|---|
#0.03 mg dose results
sim0.03<-read.table("C:\\Heller\\PHAR7380\\week11\\SDINDOUT5.res",skip=1,header=T)
#sim0.03<-read.table("/work/ac0837/PHAR7380/week11/indirect1.dir1/NM_run1/SDINDOUT1.res",skip=1,header=T)
sim0.03sum<-sim0.03%>%filter(CMT==4)%>%group_by(TIME)%>%summarise(cmed=median(IPRED),cu975=quantile(IPRED,0.975),cu025=quantile(IPRED,0.025))#plots for 0.03 mg
ggplot(data=sim0.03%>%filter(CMT==4),aes(TIME/(7*24),IPRED*1000,group=ID))+
geom_line(size=0.5)+
scale_x_continuous(limits = c(0,6))+
theme_bw()+
my_theme()+
labs(x="Time after dose (week)",y="CD4 count/mL")
#median and 95% CI
ggplot(data=sim0.03sum,aes(TIME/(24*7),cmed*1000))+
geom_line(size=0.5)+
geom_line(data=sim0.01sum,aes(TIME/(24*7),cu975*1000),size=0.5,linetype="dashed")+
geom_line(data=sim0.01sum,aes(TIME/(24*7),cu025*1000),size=0.5,linetype="dashed")+
scale_x_continuous(limits = c(0,6))+
theme_bw()+
my_theme()+
labs(x="Time after dose (week)",y="CD4 count/mL")
#CD4 suppression at any time post-dose is defined as ≥ 10% of subjects with CD4 counts < 80% of pre-dose values. You need to interpret the table for the criteria of CD4 suppression. Percent column is the percent of subjects with <80% counts of pre-dose values.
sim0.03%>%
select(ID,TIME,CMT,IPRED,BASE)%>%
group_by(TIME)%>%
filter(CMT==4)%>%
mutate(suppression=(IPRED/BASE)*100)%>%
filter(suppression<80)%>%
summarise(percent=n_distinct(ID))%>%kable| TIME | percent |
|---|---|
| 2 | 10 |
| 3 | 20 |
| 4 | 27 |
| 6 | 40 |
| 8 | 46 |
| 10 | 48 |
| 12 | 47 |
| 18 | 47 |
| 24 | 46 |
| 36 | 40 |
| 48 | 34 |
| 60 | 31 |
| 72 | 28 |
| 96 | 20 |
| 120 | 11 |
| 144 | 8 |
| 168 | 5 |