knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "man/figures/README-", out.width = "100%" )
You can install the development version of aftiv from github with:
devtools::install_github("jaredhuling/aftiv")
This is a basic example which shows you how to fit a semiparametric AFT model with instrumental variable estimation:
library(aftiv)
## simulate data set.seed(1) true.beta <- c(1,-0.5,-0.5,0) dat <- simIVMultivarSurvivalData(500,1,1,-1,1,true.beta,num.confounded = 1, cens.distribution = "lognormal", confounding.function = "exp") ## delta is event indicator, log.t is log of the observed time ## X are the covariates, the first of which is the exposure of interest, the ## rest are covariates to adjust for df <- data.frame(dat$survival[c("delta", "log.t")], dat$X) ## Z is the instrument, related to the first variable in X Z <- dat$Z system.time(aftf <- aftfit(Surv(log.t, delta) ~ ., data = df, instrument = Z, confounded.x.names = "X1", # name of the exposure of interest method = c("AFT", # naive, unadjusted (biased) estimator "AFT-2SLS", # 2-stage approach that relies on IV model "AFT-IV", # incorrect approach "AFT-IPCW"), # proposed approach of Huling, et al boot.method = "ls", B = 200L, ## number of bootstrap iterations bootstrap = TRUE)) ## use bootstrap for Conf Intervals
Investigate results:
summary(aftf)
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