ExtendedABSurvTDC - Survival Analysis using Indicators under Time Dependent
Covariates
Survival analysis is employed to model time-to-event data.
This package examines the relationship between survival and one
or more predictors, termed as covariates, which can include
both treatment variables (e.g., season of birth, represented by
indicator functions) and continuous variables. To this end, the
Cox-proportional hazard (Cox-PH) model, introduced by Cox in
1972, is a widely applicable and commonly used method for
survival analysis. This package enables the estimation of the
effect of randomization for the treatment variable to account
for potential confounders, providing adjustment when estimating
the association with exposure. It accommodates both fixed and
time-dependent covariates and computes survival probabilities
for lactation periods in dairy animals. The package is built
upon the algorithm developed by Klein and Moeschberger (2003)
<DOI:10.1007/b97377>.