  Numerous tutorials and research papers focus on methods in either survival
analysis or causal inference, leaving common complications in medical studies
unaddressed. In practice one must handle problems jointly, without the luxury
of ignoring essential features of the data structure. In this paper, we follow
incident cases of end-stage renal disease and examine the effect on all-cause
mortality of starting treatment with transplant, so-called pre-emptive kidney
transplantation, versus dialysis. The question is relatively simple: which
treatment start is expected to bring the best survival for a target population?
To address the question, we emulate a target trial drawing on the Swedish Renal
Registry to estimate a causal effect on survival curves. Aware of important
challenges, we see how previous studies have selected patients into treatment
groups based on events occurring post treatment initiation. Our study reveals
the dramatic impact of resulting immortal time bias and other typical features
of long term incident disease registries, including: missing or mismeasured
covariates during (the early) phases of the register, varying risk profile of
patients entering treatment groups over calendar time and changes in risk as
care improves over the years. With characteristics of cases and versions of
treatment evolving over time, informative censoring is introduced in unadjusted
Kaplan-Meier curves and also their IPW version is no longer valid. Here we
discuss feasible ways of handling these features and answer different research
questions relying on the no unmeasured baseline confounders assumption.
