R Analysis Example Replication C10
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1 R Analysis Example Replication C10 # ASDA2 Chapter 10 Survival Analysis library(survey) # Read in C10 data set, this data is set up for survival analysis in one record per person format ncsrc10 <- read.table(file = "P:/ASDA 2/Data sets/ncsr/c10_ncsr.csv", sep = ",", header = T, as.is=t) names(ncsrc10) #create factor versions with labels ncsrc10$racec <- factor(ncsrc10$racecat, levels = 1: 4, labels =c("other", "Hispanic", "Black", "White")) ncsrc10$mar3catc <- factor(ncsrc10$mar3cat, levels = 1: 3, labels =c("married", "Previously Married", "Never Married")) ncsrc10$ed4catc <- factor(ncsrc10$ed4cat, levels = 1: 4, labels =c("0-11", "12", "13-15","16+")) ncsrc10$sexc <- factor(ncsrc10$sex, levels = 1:2, labels=c("male","female")) ncsrc10$ag4catc <- factor(ncsrc10$ag4cat, levels = 1:4, labels=c("18-29", "30-44", "45-59", "60+")) ncsrc10$mdec <- factor(ncsrc10$mde, level = 1:2, labels=c("no","yes")) # survey design for one record per person ncsrsvyc10 <- svydesign(strata=~sestrat, id=~seclustr, weights=~ncsrwtsh, data=ncsrc10, nest=t) names (ncsrsvyc10) # Example 10.3 KM curve NCSR data, note use of survfit since we do not need SE's for this analysis (km <- survfit(surv(ageonsetmde,mde)~strata(racecat), data=ncsrc10, weight=ncsrwtsh)) plot(km,lwd=5,lty=c(1,2,3,4),col=c("blue","green","red", "purple"), ylab=c("survival"), xlab=c("time to Event in Years: Blue:Other Green:Hispanic Red:AfAm Purple:White")) # svykm instead for comparison and example # Note that when using "se=t" it causes R program to stall and die, omit here as PC runs out of memory, see documentation for details on this issue (kmsvy <- svykm(surv(ageonsetmde,mde)~strata(racecat),design=ncsrsvyc10)) plot(kmsvy,lwd=2,pars=list(lty=c(1,2,3,4)),ylab=c("survival"),xlab=c("time to Event in Years: Solid=Other, Dashed=Hispanic, Dotted=Black, Dash-Dot=White")) # Example 10.4 Cox model summary(ex104_coxph<-svycoxph(surv(ageonsetmde,mde)~intwage + sexm + mar3catc + ed4catc + racec,design=ncsrsvyc10)) # No test of proportional hazards for race in R #discrete time logistic using ncsr data in person year format #read in personyear data, previously set up with multiple records per person ncsrpy <- read.table(file = "P:/ASDA 2/Data sets/ncsr/c10_expanded1.csv", sep = ",", header = T, as.is=t) names(ncsrpy) ncsrsvypyp1 <- svydesign(strata=~sestrat, id=~seclustr, weights=~ncsrwtsh, data=ncsrpy, nest=t) # Example 10.5 discrete time logistic # Subset of records <= age of onset of mde/censor, needed for model to follow subncsrpy <- subset(ncsrsvypyp1, pyr <= ageonsetmde) summary(ex105_logit <- svyglm(mdetv ~ pyr + intwage + sexm + factor(ed4cat) + factor(racecat) + factor(mar3cat), family=quasibinomial, design=subncsrpy)) # get exponents of betas exp(ex105_logit$coef) # With cloglog link summary(ex105_cloglog<-svyglm(mdetv ~ pyr + intwage + sexm + factor(ed4cat) + factor(racecat) + factor(mar3cat), family=quasibinomial(link=cloglog), design=subncsrpy)) # With exponentiated coefficients exp(ex105_logit$coef) 1
2 Output R Analysis Example Replication C10 > # KM curve NCSR data, note use of survfit since we do not need SE's for this analysis > (km <- survfit(surv(ageonsetmde,mde)~strata(racecat), data=ncsrc10, weight=ncsrwtsh)) Call: survfit(formula = Surv(ageonsetmde, mde) ~ strata(racecat), data = ncsrc10, weights = NCSRWTSH) records n.max n.start events median 0.95LCL 0.95UCL strata(racecat)=racecat= NA NA NA strata(racecat)=racecat= NA NA NA strata(racecat)=racecat= NA NA NA strata(racecat)=racecat= NA NA NA > plot(km,lwd=5,lty=c(1,2,3,4),col=c("blue","green","red", "purple"), ylab=c("survival"), xlab=c("time to Event in Years: Blue:Other Green:Hispanic Red:AfAm Purple:White")) 2
3 #use of svykm instead for comparison and example (kmsvy <- svykm(surv(ageonsetmde,mde)~strata(racecat), design=ncsrsvyc10)) plot(kmsvy,lwd=2,pars=list(lty=c(1,2,3,4)),ylab=c("survival"),xlab=c("time to Event in Years: Solid=Other, Dashed=Hispanic, Dotted=Black, Dash-Dot=White")) 3
4 > # Example 10.4 Cox model > summary(ex104_coxph<-svycoxph(surv(ageonsetmde,mde)~intwage + sexm + mar3catc + ed4catc + racec,design=ncsrsvyc10)) Stratified 1 - level Cluster Sampling design (with replacement) With (84) clusters. svydesign(strata = ~SESTRAT, id = ~SECLUSTR, weights = ~NCSRWTSH, data = ncsrc10, nest = T) Call: svycoxph(formula = Surv(ageonsetmde, mde) ~ intwage + sexm + mar3catc + ed4catc + racec, design = ncsrsvyc10) n= 9282, number of events= 1829 coef exp(coef) intwage sexm mar3catcpreviously Married mar3catcnever Married ed4catc ed4catc ed4catc racechispanic racecblack racecwhite se(coef) z Pr(> z ) intwage < 2e-16 sexm e-13 mar3catcpreviously Married < 2e-16 mar3catcnever Married ed4catc ed4catc ed4catc racechispanic racecblack racecwhite intwage *** sexm *** mar3catcpreviously Married *** mar3catcnever Married ed4catc12 ed4catc13-15 ed4catc16+ racechispanic. racecblack ** racecwhite --- Signif. codes: 0 *** ** 0.01 * exp(coef) exp(-coef) intwage sexm
5 mar3catcpreviously Married mar3catcnever Married ed4catc ed4catc ed4catc racechispanic racecblack racecwhite lower.95 upper.95 intwage sexm mar3catcpreviously Married mar3catcnever Married ed4catc ed4catc ed4catc racechispanic racecblack racecwhite Concordance= (se = ) Rsquare= NA (max possible= NA ) Likelihood ratio test= NA on 10 df, p=na Wald test = on 10 df, p=0 Score (logrank) test = NA on 10 df, p=na > # No test of proportional hazards for race in R 5
6 > #discrete time logistic using NCSR data in person year format > #read in personyear data, previously set up with multiple records per person > ncsrpy <- read.table(file = "P:/ASDA 2/Data sets/ncsr/c10_expanded1.csv", sep = ",", header = T, as.is=t) > names(ncsrpy) [1] "CASEID" "DSM_SO" "MDE_OND" "SO_OND" "AGE" "REGION" "MAR3CAT" [8] "ED4CAT" "OBESE6CA" "NCSRWTSH" "NCSRWTLG" "SEX" "WKSTAT3C" "SESTRAT" [15] "SECLUSTR" "ag4cat" "racecat" "mde" "ald" "sexf" "sexm" [22] "ageonsetmde" "intwage" "ncsrwtsh100" "pyr" "mdetv" > ncsrsvypyp1 <- svydesign(strata=~sestrat, id=~seclustr, weights=~ncsrwtsh, data=ncsrpy, nest=t) > # Example 10.5 discrete time logistic > # Subset of records <= age of onset of mde/censor, needed for model to follow > subncsrpy <- subset(ncsrsvypyp1, pyr <= ageonsetmde) > summary(ex105_logit <- svyglm(mdetv ~ pyr + intwage + sexm + factor(ed4cat) + factor(racecat) + factor(mar3cat), family=quasibinomial, design=subncsrpy)) Call: svyglm(formula = mdetv ~ pyr + intwage + sexm + factor(ed4cat) + factor(racecat) + factor(mar3cat), family = quasibinomial, design = subncsrpy) Survey design: subset(ncsrsvypyp1, pyr <= ageonsetmde) Coefficients: Estimate Std. Error t value Pr(> t ) (Intercept) < 2e-16 *** pyr < 2e-16 *** intwage < 2e-16 *** sexm e-08 *** factor(ed4cat) factor(ed4cat) factor(ed4cat) factor(racecat) factor(racecat) ** factor(racecat) factor(mar3cat) e-09 *** factor(mar3cat) Signif. codes: 0 *** ** 0.01 * (Dispersion parameter for quasibinomial family taken to be ) Number of Fisher Scoring iterations: 9 > # get exponents of betas > exp(ex105_logit$coef) (Intercept) pyr intwage sexm factor(ed4cat)2 factor(ed4cat) factor(ed4cat)4 factor(racecat)2 factor(racecat)3 factor(racecat)4 factor(mar3cat)2 factor(mar3cat)
7 > # With cloglog link > summary(ex105_cloglog<-svyglm(mdetv ~ pyr + intwage + sexm + factor(ed4cat) + factor(racecat) + factor(mar3cat), family=quasibinomial(link=cloglog), design=subncsrpy)) Call: svyglm(formula = mdetv ~ pyr + intwage + sexm + factor(ed4cat) + factor(racecat) + factor(mar3cat), family = quasibinomial(link = cloglog), design = subncsrpy) Survey design: subset(ncsrsvypyp1, pyr <= ageonsetmde) Coefficients: Estimate Std. Error t value Pr(> t ) (Intercept) < 2e-16 *** pyr < 2e-16 *** intwage < 2e-16 *** sexm e-08 *** factor(ed4cat) factor(ed4cat) factor(ed4cat) factor(racecat) factor(racecat) ** factor(racecat) factor(mar3cat) e-09 *** factor(mar3cat) Signif. codes: 0 *** ** 0.01 * (Dispersion parameter for quasibinomial family taken to be ) Number of Fisher Scoring iterations: 9 > # With exponentiated coefficients > exp(ex105_logit$coef) (Intercept) pyr intwage sexm factor(ed4cat)2 factor(ed4cat) factor(ed4cat)4 factor(racecat)2 factor(racecat)3 factor(racecat)4 factor(mar3cat)2 factor(mar3cat)
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