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Institut für Medizinische Biometrie und Statistik (IMBI)
Forschungsdatenmanagement
Knowledge Discovery and Synthesis
Medical Data Science
Methods in Clinical Epidemiology
Expert Knowledge and Data Interactions
Sektion Versorgungsforschung und Rehabilitationsforschung (SEVERA)
Studien und Beratung
Admin & IT
SFB 1597 Small Data
Lehrbücher
Methodik Klinischer Studien, 3. Auflage
Multivariable Model-building
Competing Risks and Multistate Models with R
Regression Models as a Tool in Medical Research
Meta-Analysis with R
Competing Risks and Multistate Models with R
The book
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R code
R code for chapter 3
R code for chapter 4
R code for chapter 5
R code for chapter 6
R code for chapter 7
R code for chapter 9
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R code for chapter 11
Appendix
Appendix
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Competing Risks and Multistate Models with R
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Appendix
Appendix
## number of boostrap replications nboot <- 10000 res <- lapply(seq_len(nboot), function(i) { index <- sample(unique(sir.cont$id), replace=TRUE) dboot <- sir.cont[unlist(sapply(index, function(x) which(x==sir.cont[["id"]] ))), ] coef01 <- coef(coxph(Surv(time, to == 1) ~ age + sex, dboot, subset = from == 0)) coef10 <- coef(coxph(Surv(time, to == 0) ~ age + sex, dboot, subset = from == 1)) matrix(c(coef01, coef10), ncol = 4) }) res <- do.call(rbind, res) se.boot <- matrix(sqrt(apply(res, 2, var)), ncol = 4, dimnames = list("", c("age01", "sex01", "age10", "sex10")))
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