library(haven)
library(tidyverse)
library(dplyr)
library(survival)
library(survminer)
library(ggsurvfit)Cox HW
Homework #3: Cox Model
For this homework, I am using the DHS file for Bangladesh 2014 data.
Hypothesis
I’ve been very interested in differences that stem from multi-generational households in relation to a variety of topics. In this case, I was seeking to understand if multigenerational are more likely to have a second birth. Additionally, I was interested how second births varied by rural versus urban areas.
Hypothesis 1: Multigenerational households are more likely to impact the hazard ratio as well as rural households.
Data Prep
This reads in the dta file from DHS. This is for Bangladesh 2014 Data
Bringing in the Individual File (BG 2014) and joining the files
bgin <- read_dta("C:/Users/codar/OneDrive - University of Texas at San Antonio/R 2022/BDIR72FL.DTA")
bgin$hh <- substr(bgin$caseid, 1,12)
bang1<- left_join(bgin, bghh22, by=c("hh" ="hhid")) #you used bdat, not bdat2
# names(mdat)
#| error: falsebang2 <- bang1 %>%
filter(bidx_01==1&b0_01==0)%>%
transmute(CASEID=caseid,
int.cmc=v008,
fbir.cmc=b3_01,
sbir.cmc=b3_02,
marr.cmc=v509,
rural=v025,
educ=v106,
age = v012,
agec=cut(v012, breaks = seq(15,50,5), include.lowest=T),
partneredu=v701,
partnerage=v730,
weight=v005/1000000,
psu=v021,
strata=v022, #by adding them into the transmute, I was able to get them to show up in the mdat2 data frame...why?
mgenhh = mgenhh,
hh = hh) %>%
select(CASEID, int.cmc, fbir.cmc, sbir.cmc, marr.cmc, rural, educ, age, agec, partneredu, partnerage, weight, psu, strata, mgenhh, hh, rural)%>%
mutate(agefb = (age - (int.cmc - fbir.cmc)/12))
#Calculating the birth intervals, observed and censored and the event indicator (did women have a second birth?)
bang3<-bang2%>%
mutate(secbi = ifelse(is.na(sbir.cmc)==T,
int.cmc - fbir.cmc,
fbir.cmc - sbir.cmc),
b2event = ifelse(is.na(sbir.cmc)==T,0,1))
# library(ggsurvfit)
# fit<-survfit2(Surv(secbi, b2event)~1, bang3)
#
# fit %>%
# ggsurvfit()+
# labs(title = "Survival Function for Second Birth Interval,\nBangledesh DHS 2014",
# y = "S(t)",
# x= "Months")Defining Covariates
The covariates I will use are rural, multgen, and education.
#| warning: false
#| error: false
#Recoding mgenhh from char to numeric
lookup <- c("notmgen" = 0, "mgenhh" = 1)
bang3$new_mghh <- lookup[bang3$mgenhh]
bang3$educ.high<-ifelse(bang3$educ %in% c(2,3), 1, 0)
#recoding rural to 0/1 binary
bang3$rural2 <- ifelse(bang3$rural==2, 1, 0)
library(survey) Loading required package: gridLoading required package: Matrix
Attaching package: 'Matrix'The following objects are masked from 'package:tidyr':
expand, pack, unpack
Attaching package: 'survey'The following object is masked from 'package:graphics':
dotchart#survey design
options(survey.lonely.psu = "adjust")
des<-svydesign(ids=~psu, strata=~strata,
data=bang3, weight=~weight)Fit the Model & Run Tests
(bs to spline if needed)
The exp(coef) column contains eβ1 aka the hazard ratio:
HR=1HR=1: No effect
HR>1HR>1: Increase in hazard
HR<1HR<1: Decrease in hazard
So, for these results, going from rural to urban the hazard of having a second birth are higher. Homes that are multigenerational have a lower chance of having a second birth, which is surprising. Lower chances of having a second birth are lower for higher educated women.
library(gtsummary)
# library(splines)
cox.b <-svycoxph(Surv(secbi, b2event)~rural2+new_mghh+educ.high, design=des)
summary(cox.b)Stratified 1 - level Cluster Sampling design (with replacement)
With (600) clusters.
svydesign(ids = ~psu, strata = ~strata, data = bang3, weight = ~weight)Call:
svycoxph(formula = Surv(secbi, b2event) ~ rural2 + new_mghh +
educ.high, design = des)
n= 15934, number of events= 11959
coef exp(coef) se(coef) robust se z Pr(>|z|)
rural2 0.20248 1.22444 0.02110 0.03065 6.607 3.91e-11 ***
new_mghh -0.07547 0.92731 0.01850 0.02250 -3.354 0.000798 ***
educ.high -0.22116 0.80159 0.01936 0.02790 -7.927 2.24e-15 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp(coef) exp(-coef) lower .95 upper .95
rural2 1.2244 0.8167 1.1531 1.3002
new_mghh 0.9273 1.0784 0.8873 0.9691
educ.high 0.8016 1.2475 0.7589 0.8466
Concordance= 0.554 (se = 0.004 )
Likelihood ratio test= NA on 3 df, p=NA
Wald test = 137.4 on 3 df, p=<2e-16
Score (logrank) test = NA on 3 df, p=NA
(Note: the likelihood ratio and score tests assume independence of
observations within a cluster, the Wald and robust score tests do not).Plotting the Survival Function & Cumulative Hazard Function
ggsurvplot(survfit(Surv(secbi, b2event)~new_mghh, data=bang3))
library(muhaz)
fit.haz.km<-kphaz.fit(bang3$secbi,
bang3$b2event ,
method = "product-limit")
kphaz.plot(fit.haz.km,
main="Plot of the hazard of having a second birth")
Testing for heteroskedacity or examining non-proportional hazards with respect to time.
Theoretically, this should help me determine interaction between covariates using the schoenfeld residuals. However, I received the below code error.
schoenresid<-resid(cox.b, type="schoenfeld")
# fit.sr<-lm(schoenresid~des$variables$new_mghh[des$variables$educ.high]) #error message here about variable lenths being different
#I would then get a table from the below code
# fit.sr%>%
# broom::tidy()%>%
# filter(term != "(Intercept)")%>%
# select(response, estimate, statistic, p.value)
#
# #Then I would plot using the below code:
# plot(fit.test, df=2)Martingale Residuals
Martingale residuals are useful for assessing the functional form of a covariate. If a line fit to these residuals is a straight line, then the covariate has been modeled effectively. This is the case for my model.
#|error: false
#|warning: false
#extract Martingale residuals
res.mar<-resid(cox.b, type="martingale")
#plot vs maternal education
scatter.smooth(des$variables$new_mghh, res.mar,degree = 2,
span = 1, ylab="Martingale Residual",
col=1, cex=.5, lpars=list(col = "red", lwd = 3))Warning in simpleLoess(y, x, w, span, degree = degree, parametric = FALSE, :
pseudoinverse used at -0.005Warning in simpleLoess(y, x, w, span, degree = degree, parametric = FALSE, :
neighborhood radius 1.005Warning in simpleLoess(y, x, w, span, degree = degree, parametric = FALSE, :
reciprocal condition number 8.8746e-27Warning in simpleLoess(y, x, w, span, degree = degree, parametric = FALSE, :
There are other near singularities as well. 1.01Warning in simpleLoess(y, x, w, span, degree = degree, parametric = FALSE, :
pseudoinverse used at -0.005Warning in simpleLoess(y, x, w, span, degree = degree, parametric = FALSE, :
neighborhood radius 1.005Warning in simpleLoess(y, x, w, span, degree = degree, parametric = FALSE, :
reciprocal condition number 4.7211e-17Warning in simpleLoess(y, x, w, span, degree = degree, parametric = FALSE, :
There are other near singularities as well. 1.01Warning in simpleLoess(y, x, w, span, degree = degree, parametric = FALSE, :
pseudoinverse used at -0.005Warning in simpleLoess(y, x, w, span, degree = degree, parametric = FALSE, :
neighborhood radius 1.005Warning in simpleLoess(y, x, w, span, degree = degree, parametric = FALSE, :
reciprocal condition number 4.0827e-17Warning in simpleLoess(y, x, w, span, degree = degree, parametric = FALSE, :
There are other near singularities as well. 1.01Warning in simpleLoess(y, x, w, span, degree = degree, parametric = FALSE, :
pseudoinverse used at -0.005Warning in simpleLoess(y, x, w, span, degree = degree, parametric = FALSE, :
neighborhood radius 1.005Warning in simpleLoess(y, x, w, span, degree = degree, parametric = FALSE, :
reciprocal condition number 0Warning in simpleLoess(y, x, w, span, degree = degree, parametric = FALSE, :
There are other near singularities as well. 1.01Warning in simpleLoess(y, x, w, span, degree = degree, parametric = FALSE, :
pseudoinverse used at -0.005Warning in simpleLoess(y, x, w, span, degree = degree, parametric = FALSE, :
neighborhood radius 1.005Warning in simpleLoess(y, x, w, span, degree = degree, parametric = FALSE, :
reciprocal condition number 8.8746e-27Warning in simpleLoess(y, x, w, span, degree = degree, parametric = FALSE, :
There are other near singularities as well. 1.01title(main="Martingale residuals for Multigenerational Households")
Schoenfeld Test
fit.test <-cox.zph(cox.b)
fit.test chisq df p
rural2 0.000145 1 0.99
new_mghh 0.000325 1 0.99
educ.high 0.006696 1 0.93
GLOBAL 0.007260 3 1.00Plot the ST
plot(fit.test, df=2)
We can also do a graphical diagnostic using the function ggcoxzph(),, which produces, for each covariate, graphs of the scaled Schoenfeld residuals against the transformed time.
ggcoxzph(fit.test)
Martingale Residuals
#|output:false
res.mar <-resid(cox.b, type = "martingale")
res.mar 1 2 3 4 5
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11 12 13 14 15
-0.0093104485 0.5235409471 -0.9551259990 0.8786200572 -1.9742238662
16 17 18 19 20
-0.4111981231 0.0603808980 0.6006629898 -0.9619782061 0.0000000000
21 22 23 24 25
-1.6057999620 0.9157883789 0.8867092087 -0.0069206681 -0.9132496058
26 27 28 29 30
0.0000000000 -0.2296437783 0.5182929114 0.4825807068 0.3763696074
31 32 33 34 35
0.7375029124 -1.1850946353 0.4005832121 -1.2479055586 -0.0049246779
36 37 38 39 40
-0.7442015461 -2.2675689482 -0.1338631548 0.0439261110 -0.4181686986
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1471 1472 1473 1474 1475
-0.1907141464 0.9351202914 -0.1313942697 0.0000000000 -0.0416134685
1476 1477 1478 1479 1480
-0.1109752540 0.4478274789 -2.3130643670 0.2389359426 -0.1766021953
1481 1482 1483 1484 1485
-0.1264724663 -0.1839857483 -0.5802972606 -1.0453466120 -0.0654663902
1486 1487 1488 1489 1490
-1.1893103002 0.0634847494 -0.0022146872 0.9205374039 0.5104557496
1491 1492 1493 1494 1495
-1.4905134143 0.7315700931 0.6351866369 0.0198580353 -0.8196705212
1496 1497 1498 1499 1500
0.0000000000 -0.8027825448 0.2564893165 0.8150475392 -0.1197759071
1501 1502 1503 1504 1505
-0.0156071912 0.0881563283 0.2326122020 -2.7258062581 0.9478039503
1506 1507 1508 1509 1510
0.6730935186 0.7717794571 -0.8373859587 -0.1582197004 0.4130970814
1511 1512 1513 1514 1515
-0.1766021953 -0.3487027925 -0.6304219760 0.2300138160 0.4291514156
1516 1517 1518 1519 1520
0.5442979085 0.7446966913 0.3480112144 0.3105355756 -0.4980456320
1521 1522 1523 1524 1525
-0.0416134685 -0.6154755668 0.7241208106 -0.3088862855 -0.7439827385
1526 1527 1528 1529 1530
-1.3861959277 -0.1261405399 -0.3580115267 -2.2150732688 0.0000000000
1531 1532 1533 1534 1535
0.9437957161 0.6738610909 0.8170604935 0.7679620494 0.4624570997
1536 1537 1538 1539 1540
-0.2984852026 0.5092126950 0.3899379904 0.3026988878 0.3446044526
1541 1542 1543 1544 1545
0.5917341132 0.8990815859 0.7679620494 0.4334586582 0.0930846686
1546 1547 1548 1549 1550
0.3899379904 0.2897441720 -1.3087014929 -0.0205562055 0.7544523001
1551 1552 1553 1554 1555
-0.3327801304 -0.0190148978 -1.1323414545 0.6211103695 -0.6355779499
1556 1557 1558 1559 1560
0.9736713186 0.3848689376 0.3069586765 0.4291514156 -0.9945512681
1561 1562 1563 1564 1565
0.1004789236 0.4040086149 0.2143271373 0.6347133582 -0.2545895038
1566 1567 1568 1569 1570
-0.3327801304 -0.4628634848 0.1590089144 -0.2522545380 -0.8196705212
1571 1572 1573 1574 1575
0.7352041212 -0.3271947849 0.9826681960 -0.2825252398 0.5255112904
1576 1577 1578 1579 1580
-1.4228681914 -1.8943803562 0.3250328606 -0.7544579215 0.4295832014
1581 1582 1583 1584 1585
-0.2522545380 0.5762802434 0.3294049223 -0.2201805558 0.8165481761
1586 1587 1588 1589 1590
-2.7633463859 -2.7633463859 0.3250328606 -0.6662695827 0.6558354201
1591 1592 1593 1594 1595
-0.4381232416 0.7856183834 -0.5361551125 -0.4168841531 0.2623746347
1596 1597 1598 1599 1600
0.5921778947 0.1612323746 0.7446966913 -0.2826498792 0.7105282064
1601 1602 1603 1604 1605
0.2859845959 0.3954046893 0.2859845959 0.1982646913 0.6850394706
1606 1607 1608 1609 1610
-1.7566981389 0.1459318558 0.9787236062 0.7544523001 -0.6377380804
1611 1612 1613 1614 1615
0.6927385762 0.1163635427 0.9478039503 0.0687484309 0.0000000000
1616 1617 1618 1619 1620
0.2191735621 0.0000000000 -0.1991573644 0.6603501063 0.5015314311
1621 1622 1623 1624 1625
-0.0857854798 0.0789825156 0.4910812082 -0.8031753866 0.7679620494
1626 1627 1628 1629 1630
-0.2698089303 0.8012019179 0.0198580353 0.3670904912 -0.0811078620
1631 1632 1633 1634 1635
-0.0559014497 0.2883941037 -0.8527743711 -2.0066357495 -0.5802972606
1636 1637 1638 1639 1640
-0.3742281249 0.9830620720 0.8309290350 0.5756752676 0.9398364382
1641 1642 1643 1644 1645
-0.0643936814 0.7716055974 0.6850394706 0.2749469571 0.7679620494
1646 1647 1648 1649 1650
0.9437957161 0.8170604935 -0.9277929265 0.9783782422 0.6558354201
1651 1652 1653 1654 1655
0.6486520950 0.4510942722 0.0000000000 0.0299665847 0.2749469571
1656 1657 1658 1659 1660
-0.7279556131 0.8170604935 0.5086624360 -1.0789102176 -0.9392466500
1661 1662 1663 1664 1665
0.7246837823 -0.1313942697 0.3421159878 0.8825607799 -2.7159178948
1666 1667 1668 1669 1670
0.0000000000 0.4615843721 0.3069586765 -0.0341645958 -0.1794123772
1671 1672 1673 1674 1675
0.4040086149 -0.6304219760 0.3155818275 0.0000000000 0.2932288103
1676 1677 1678 1679 1680
0.5153619112 0.3899379904 -0.2743346183 -0.2075507372 -1.2659664863
1681 1682 1683 1684 1685
-0.4006975344 0.5442979085 0.4910812082 0.0000000000 -0.7641848010
1686 1687 1688 1689 1690
-0.3209832000 0.4624570997 0.0173992363 -0.5447712595 0.5920553364
1691 1692 1693 1694 1695
0.7716055974 -0.1839857483 0.4773717338 0.3276515187 0.5295439714
1696 1697 1698 1699 1700
0.1773735272 -0.9623122314 -1.7688311247 0.8741023429 -0.1489869760
1701 1702 1703 1704 1705
-0.5250082615 -0.4048819218 0.7716055974 -0.7941717556 0.2535172327
1706 1707 1708 1709 1710
0.3250328606 0.2897441720 -0.0569740749 0.9736713186 -0.9222567110
1711 1712 1713 1714 1715
-0.0381071810 0.9351202914 0.2897441720 -0.2374661454 -1.5736815206
1716 1717 1718 1719 1720
0.2480383204 0.9143084298 -2.1952821257 0.0634847494 -1.2135711584
1721 1722 1723 1724 1725
0.4193786403 -0.7328865809 0.3446044526 0.9527064449 0.8675167761
1726 1727 1728 1729 1730
0.6733138311 -0.0697381306 0.8165481761 0.5448883328 -0.1766021953
1731 1732 1733 1734 1735
-0.0056521161 0.6486520950 -2.2150732688 -1.7010343963 0.9205374039
1736 1737 1738 1739 1740
-0.3446409821 0.6391611551 0.8281536507 0.8176757722 -0.0833947675
1741 1742 1743 1744 1745
0.1139422325 0.2883941037 -0.4994753476 0.9008688874 -0.0912204038
1746 1747 1748 1749 1750
-1.1197233428 0.6733138311 0.3954046893 -0.4299462351 0.2045530993
1751 1752 1753 1754 1755
0.6108754466 0.5602092268 0.3740966464 -0.0234945630 -0.7086804213
1756 1757 1758 1759 1760
0.8741023429 -0.1489869760 0.4040086149 -1.0859152091 0.9658586757
1761 1762 1763 1764 1765
-0.8760500551 -0.4006975344 0.8165481761 0.3276515187 0.9620899174
1766 1767 1768 1769 1770
-0.1292606804 0.7538895324 0.4130970814 0.9683404301 -0.4450940515
1771 1772 1773 1774 1775
0.2143271373 0.9398364382 -0.0731214440 0.2371707629 0.2883941037
1776 1777 1778 1779 1780
0.8057787819 -0.5319881070 -0.3851134653 -2.1525964407 0.1163635427
1781 1782 1783 1784 1785
-1.0280714539 -1.2698502855 -1.8090116773 0.0000000000 -0.0381071810
1786 1787 1788 1789 1790
0.8990815859 0.4510942722 -2.7559630237 -0.7711121451 -0.2701010081
1791 1792 1793 1794 1795
-0.2959712131 -0.5736899934 0.1527392182 -0.1109752540 -2.1434006275
1796 1797 1798 1799 1800
-1.5170416036 -0.2698089303 0.4295832014 0.5768249302 -0.8169538810
1801 1802 1803 1804 1805
0.6558354201 0.0954824231 0.6108754466 0.4507555517 0.0000000000
1806 1807 1808 1809 1810
0.7905540220 -0.5428277229 -0.1489869760 0.5448883328 -0.0115078076
1811 1812 1813 1814 1815
-0.4915371619 -0.5178993589 -0.7059130616 -0.0979210797 -0.5844753669
1816 1817 1818 1819 1820
0.9817343289 -0.0262423020 0.0000000000 -0.0020537002 0.6065893709
1821 1822 1823 1824 1825
0.5273279192 0.1315606319 0.5424129793 -0.0253362726 0.7848290340
1826 1827 1828 1829 1830
0.9951602699 -0.1093261635 -0.3851134653 0.8057787819 0.8170604935
1831 1832 1833 1834 1835
0.8910975107 0.0000000000 0.9826681960 0.2480383204 -1.0984498241
1836 1837 1838 1839 1840
-0.1489869760 0.8027200930 -1.1272900334 0.2897441720 0.5443780725
1841 1842 1843 1844 1845
0.6211103695 -0.5250082615 0.2859845959 -0.1806163886 0.6391611551
1846 1847 1848 1849 1850
-0.1686823242 0.4720809844 -0.0007939204 0.8930981227 -1.0859152091
1851 1852 1853 1854 1855
-0.0056521161 -0.0491525087 0.1139422325 0.9190613191 -0.0368427160
1856 1857 1858 1859 1860
-0.2744741428 0.5255112904 0.5273279192 0.9143084298 -0.1686823242
1861 1862 1863 1864 1865
0.9783782422 0.7577053054 0.2897441720 0.4473316034 -2.9799616739
1866 1867 1868 1869 1870
-1.8799002014 0.6880813057 -0.0168306195 -1.6050788589 0.6391611551
1871 1872 1873 1874 1875
-0.3519740487 0.3421159878 0.5424129793 -0.9932671560 -1.1001151728
1876 1877 1878 1879 1880
0.3283161778 0.4316029070 0.4473316034 -0.1904458218 0.6351866369
1881 1882 1883 1884 1885
0.7848290340 -0.0234945630 -0.1892421669 -2.1275668070 -0.3088862855
1886 1887 1888 1889 1890
-1.6176131279 -2.3411633469 0.5104557496 0.0000000000 0.4478274789
1891 1892 1893 1894 1895
-0.7845688290 0.3421159878 0.4879652467 -0.8249502536 0.4773717338
1896 1897 1898 1899 1900
0.2300138160 -0.0559014497 0.1590089144 0.9858226452 0.3763696074
1901 1902 1903 1904 1905
0.9478487717 0.3788001569 0.0439261110 0.1130948281 0.0000000000
1906 1907 1908 1909 1910
0.9936095206 -0.1075260717 0.9140200544 0.9677621510 -0.0111573685
1911 1912 1913 1914 1915
0.0000000000 0.6873968203 -0.7105382602 -0.5288654772 0.0098561304
1916 1917 1918 1919 1920
0.7174831906 -0.6672620294 -0.9396463612 -0.5333085665 -0.1548577467
1921 1922 1923 1924 1925
0.1788976234 0.0792692301 0.5955215383 -0.2572546122 0.9027029445
1926 1927 1928 1929 1930
0.8048799463 -1.1028968961 0.9360891515 -0.9396463612 -0.0074631706
1931 1932 1933 1934 1935
0.9405112988 -1.8580205328 -0.1014081645 -0.8859871566 0.0000000000
1936 1937 1938 1939 1940
0.4005832121 -0.2572546122 0.8206103686 0.8691052344 0.3983874100
1941 1942 1943 1944 1945
0.3274839962 0.6986528882 -0.6074377527 -1.0003097183 -0.2696269502
1946 1947 1948 1949 1950
0.9027029445 -1.0216614357 0.8562029070 0.9652350661 0.3768602793
1951 1952 1953 1954 1955
-2.5501031579 0.9205587921 0.7174831906 0.1122171985 0.7375029124
1956 1957 1958 1959 1960
0.6455596147 -1.8052360621 0.0183244862 -0.0140906017 0.5686368449
1961 1962 1963 1964 1965
0.6511251721 0.7929830149 0.7577664282 0.3518395535 -0.0191100445
1966 1967 1968 1969 1970
0.8495741465 -0.2438559284 0.4005832121 0.0000000000 0.0000000000
1971 1972 1973 1974 1975
0.6006629898 -0.5264426883 0.5686368449 -0.8178000047 0.6511251721
1976 1977 1978 1979 1980
0.4420208516 -0.3603207834 -0.0384338340 0.7375029124 0.9776348106
1981 1982 1983 1984 1985
0.3600738721 -0.6996069966 -0.0509531296 -0.0310193326 -1.1862216803
1986 1987 1988 1989 1990
0.7380195151 0.9405112988 0.6511251721 0.8949443875 0.9420919542
1991 1992 1993 1994 1995
0.8691052344 -0.0684478826 -0.0454558840 0.2385840715 0.8949443875
1996 1997 1998 1999 2000
0.4212421448 -0.1164964236 0.9936095206 -0.9558946935 0.6628922457
2001 2002 2003 2004 2005
0.9516396773 -0.0651347085 -0.2001234775 -3.4796965811 0.8336503138
2006 2007 2008 2009 2010
-0.3981360570 -0.3320510995 0.0260242546 0.5852414120 0.8691052344
2011 2012 2013 2014 2015
0.5697961267 0.3768602793 0.4212421448 0.4221566974 0.3788001569
2016 2017 2018 2019 2020
0.6542232277 0.0000000000 0.6873968203 -0.6863016466 0.3990611682
2021 2022 2023 2024 2025
0.8867092087 0.7895847278 -0.3045979821 -0.4383630677 -0.0140906017
2026 2027 2028 2029 2030
0.8495741465 -0.0895327053 0.9652350661 -0.3237500011 0.2192699224
2031 2032 2033 2034 2035
0.6296910482 -0.2031341255 0.9478487717 0.4811812512 0.1788976234
2036 2037 2038 2039 2040
0.6622029215 0.8206103686 -0.4434314328 0.9008955696 -0.0521867758
2041 2042 2043 2044 2045
0.4420208516 0.1346022020 0.3600738721 0.4825807068 0.0000000000
2046 2047 2048 2049 2050
0.7767552108 0.1788976234 0.6511251721 -0.0238401470 0.6993419626
2051 2052 2053 2054 2055
0.4811812512 -0.3320510995 0.9983545529 0.9735254890 0.6237773080
2056 2057 2058 2059 2060
0.5686368449 -0.0685477883 0.5581750552 0.4065905048 0.0681239289
2061 2062 2063 2064 2065
-0.1582717431 0.7380195151 0.7976304963 0.5999929039 0.5686368449
2066 2067 2068 2069 2070
0.6511251721 0.2250412817 0.0896169543 0.3010310619 0.4005832121
2071 2072 2073 2074 2075
0.1788976234 0.9008955696 -1.6653902547 0.3274839962 0.9448355699
2076 2077 2078 2079 2080
-1.1625031388 -0.8357104596 0.5235409471 0.8458460719 0.5686368449
2081 2082 2083 2084 2085
0.9792605502 0.9420919542 0.7929830149 -1.1389298192 -0.1402601988
2086 2087 2088 2089 2090
0.8048799463 0.6986528882 0.1788976234 0.6873968203 0.7205579993
2091 2092 2093 2094 2095
-0.0191100445 0.9936095206 0.8458460719 -0.5333085665 0.4804404956
2096 2097 2098 2099 2100
0.7976304963 0.8691052344 0.7767552108 0.3535956752 0.6986528882
2101 2102 2103 2104 2105
0.8206103686 0.5686368449 -0.0586278370 -0.6826577124 0.4065905048
2106 2107 2108 2109 2110
0.3600738721 -1.3357152878 0.8867092087 0.9566300782 0.8949443875
2111 2112 2113 2114 2115
0.4421190075 0.8340570722 -0.0238401470 0.0000000000 -0.0175782959
2116 2117 2118 2119 2120
-0.3045979821 0.9140200544 0.5785916659 0.3768602793 0.6458371525
2121 2122 2123 2124 2125
0.0000000000 -1.6734815036 0.6628922457 0.9205587921 0.9675000870
2126 2127 2128 2129 2130
0.3010310619 -0.7315450412 0.9792605502 -0.0140906017 0.5533085026
2131 2132 2133 2134 2135
0.3896559337 0.4421190075 0.0681239289 0.7753745388 -0.7101299871
2136 2137 2138 2139 2140
0.9952498114 -0.0191100445 0.2468098946 -1.2677403696 0.5235409471
2141 2142 2143 2144 2145
0.1286824014 -0.2162385128 -0.2037107732 -0.0111573685 0.0000000000
2146 2147 2148 2149 2150
0.7297774976 0.5999929039 0.0000000000 -0.4305367954 -0.1277291923
2151 2152 2153 2154 2155
-0.2290861673 -0.6185891605 0.6006629898 0.6542232277 0.9263334235
2156 2157 2158 2159 2160
0.5006469462 0.9868532061 0.4825807068 0.9202699907 0.0000000000
2161 2162 2163 2164 2165
0.7375029124 0.8691052344 -0.1486292943 -1.0967253704 0.0439261110
2166 2167 2168 2169 2170
0.6511251721 -0.2296437783 0.2494767556 0.9311813154 0.0000000000
2171 2172 2173 2174 2175
0.6458371525 0.0000000000 0.9420919542 0.9478487717 -0.6018569853
2176 2177 2178 2179 2180
0.7375029124 -0.1467052527 -0.2541330965 0.2813735183 0.8666556082
2181 2182 2183 2184 2185
0.6249233320 -0.5551604701 -0.6514035147 0.6984233202 0.5686368449
2186 2187 2188 2189 2190
0.9375526145 -0.5117901097 -0.4074687767 0.6757737528 0.7375029124
2191 2192 2193 2194 2195
-0.4817361411 -0.0007792318 0.6458371525 -2.3830050143 0.2752135562
2196 2197 2198 2199 2200
0.0962801294 -0.5102031866 0.2760216828 -1.3710322794 0.2016798823
2201 2202 2203 2204 2205
0.5686368449 0.4615032811 -1.8046636383 0.4221566974 -0.0027117481
2206 2207 2208 2209 2210
-1.1850946353 0.0183244862 -0.5313619547 0.0000000000 0.9787782734
2211 2212 2213 2214 2215
0.0000000000 -0.0111573685 -0.0310193326 0.6622029215 -0.5671209023
2216 2217 2218 2219 2220
-0.1864982657 0.6757737528 0.7375029124 0.2250412817 0.0183244862
2221 2222 2223 2224 2225
0.5785916659 -1.1028968961 -0.4713676124 -1.5017574146 0.8867092087
2226 2227 2228 2229 2230
0.6249233320 -0.3613636019 0.2336198873 -0.2714307640 0.5018194209
2231 2232 2233 2234 2235
-0.8085505571 -0.0287676456 0.8867092087 0.1130948281 -0.0418324437
2236 2237 2238 2239 2240
0.0681239289 0.5182929114 0.7767552108 -0.0384338340 0.5360729799
2241 2242 2243 2244 2245
-0.0418324437 0.5380324805 0.2336198873 0.5533085026 -0.1050390373
2246 2247 2248 2249 2250
-0.6411653385 -0.3853226107 0.2813735183 -0.1652296016 -0.6364882605
2251 2252 2253 2254 2255
0.9310792595 0.0348707976 0.0348707976 -0.2165431774 0.1788976234
2256 2257 2258 2259 2260
0.6993419626 -1.3153802010 -0.3782122351 -0.7975711275 -1.2240430995
2261 2262 2263 2264 2265
0.9858226452 0.6006629898 0.9979472732 0.5360729799 -0.1205193638
2266 2267 2268 2269 2270
-0.2565705016 0.3238987513 0.5004968812 -2.0568810872 -1.3877500514
2271 2272 2273 2274 2275
0.8199422783 0.2494767556 -0.0677600167 -0.3613636019 0.4421190075
2276 2277 2278 2279 2280
-0.0270193051 0.3407432198 -0.0684478826 0.9027029445 -0.0140906017
2281 2282 2283 2284 2285
0.0896169543 -0.2001234775 -0.0069206681 0.4221566974 0.4818481867
2286 2287 2288 2289 2290
0.7494201413 0.0183244862 -0.6328929032 0.9027029445 0.9833754462
2291 2292 2293 2294 2295
-0.0509531296 -0.1241325579 -0.0140906017 0.1775645228 -0.9396463612
2296 2297 2298 2299 2300
0.5527290019 0.9936095206 0.9157883789 -1.8764414748 0.8854138801
2301 2302 2303 2304 2305
0.5018194209 -0.7105382602 0.6713241092 -0.0418324437 0.7767552108
2306 2307 2308 2309 2310
0.5955215383 -0.0310193326 0.9894616498 0.6873968203 -0.0238401470
2311 2312 2313 2314 2315
0.3040327622 0.2597102737 0.7767552108 0.7380195151 -0.2001234775
2316 2317 2318 2319 2320
0.1767506886 -0.4115160533 0.8562029070 0.5527290019 -0.6018569853
2321 2322 2323 2324 2325
-0.2572546122 -1.2588101855 0.7621880788 0.2192699224 0.1514135777
2326 2327 2328 2329 2330
-0.3443367508 -0.2696269502 0.8206103686 0.2530166375 0.0571993343
2331 2332 2333 2334 2335
0.6713241092 0.0000000000 0.3015597570 0.8377824508 0.7158838187
2336 2337 2338 2339 2340
0.9375526145 0.4190177947 -3.5458646019 -0.0074631706 0.3040327622
2341 2342 2343 2344 2345
-1.2078772193 0.8786200572 -0.0684478826 -1.7406160361 0.7494201413
2346 2347 2348 2349 2350
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2351 2352 2353 2354 2355
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15931 15932 15933 15934
-1.2074970637 0.4624570997 0.3446044526 0.4879652467 scatter.smooth(des$variables$agefb, res.mar,
degree = 2,
span = 1, ylab = "Martingale Residual",
col=1, cex=.25, lpars = list(col= "red", lwd =3))
title (main = "Martingale residuals for mother age")
Visualizing the Survival Estimate
library(sm)Package 'sm', version 2.2-5.7: type help(sm) for summary informationsm.options(
list(
xlab = "Multgen",
ylab = "Second Birth")
)
sm.survival(
x = bang3$new_mghh,
y = bang3$rural2,
status = bang3$secbi,
h = sd(bang3$new_mghh) / nrow(bang3)^(-1/4)
)
Aalen’s Additive Regression Model
According to some reading:
The additive regression is an alternative (or supplement) to the Cox model. It results in plots that are informative regarding the effect of covariates on survival.
Aalen’s additive regression model. So, while in your example, you include cluster, the library mentions that it is optional, so I didn’t include it because it seems it will find it on it’s own(?). See below:
fitz <- aareg(Surv(secbi, b2event) ~rural+new_mghh+educ.high, bang3, weights = weight)
summary(fitz) slope coef se(coef) z p
Intercept 0.02340 1.06e-04 5.72e-06 18.50 1.04e-76
rural 0.00558 2.56e-05 3.14e-06 8.16 3.46e-16
new_mghh -0.00140 -9.26e-06 3.17e-06 -2.92 3.47e-03
educ.high -0.00866 -2.78e-05 3.09e-06 -8.98 2.67e-19
Chisq=172.67 on 3 df, p=<2e-16; test weights=aalenPlot the model
# install.packages("ggfortify")
library(ggfortify)
autoplot(fitz)Warning: `gather_()` was deprecated in tidyr 1.2.0.
Please use `gather()` instead.