Selection on treatment in the target population of generalizabillity and transportability analyses

TL;DR

This paper highlights that conditioning on treatment in the target population can bias generalizability and transportability analyses, and naive standardization methods may not yield valid causal estimates.

Contribution

It demonstrates that conditioning on treatment alters the estimand and introduces bias, challenging common assumptions in transportability methods.

Findings

Conditioning on treatment changes the estimand.

Naive standardization methods can lead to bias.

Simulation illustrates the identification problems.

Abstract

Investigators are increasingly using novel methods for extending (generalizing or transporting) causal inferences from a trial to a target population. In many generalizability and transportability analyses, the trial and the observational data from the target population are separately sampled, following a non-nested trial design. In practical implementations of this design, non-randomized individuals from the target population are often identified by conditioning on the use of a particular treatment, while individuals who used other candidate treatments for the same indication or individuals who did not use any treatment are excluded. In this paper, we argue that conditioning on treatment in the target population changes the estimand of generalizability and transportability analyses and potentially introduces serious bias in the estimation of causal estimands in the target population or the subset of the target population using a specific treatment. Furthermore, we argue that the naive application of marginalization-based or weighting-based standardization methods does not produce estimates of any reasonable causal estimand. We use causal graphs and counterfactual arguments to characterize the identification problems induced by conditioning on treatment in the target population and illustrate the problems using simulated data. We conclude by considering the implications of our findings for applied work.