Trial emulation and survival analysis for disease incidence registers: a case study on the causal effect of pre-emptive kidney transplantation

TL;DR

This paper demonstrates how to emulate a target trial using registry data to assess the causal impact of pre-emptive kidney transplantation on survival, addressing complex biases and data challenges in observational studies.

Contribution

It introduces a comprehensive approach for trial emulation and survival analysis in disease registries, highlighting methods to handle immortal time bias and evolving patient characteristics.

Findings

Immortal time bias significantly affects survival estimates.

Adjusting for time-varying confounders improves causal inference.

Feasible methods are discussed for complex registry data analysis.

Abstract

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evolving over time, informative censoring is introduced in unadjusted Kaplan-Meier curves and also their IPW version is no longer valid.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.