Causal inference under transportability assumptions for conditional relative effect measures
arxiv(2024)
Abstract
When extending inferences from a randomized trial to a new target population,
an assumption of transportability of difference effect measures (e.g.,
conditional average treatment effects) – or even stronger assumptions of
transportability in expectation or distribution of potential outcomes – is
invoked to identify the marginal causal mean difference in the target
population. However, many clinical investigators believe that relative effect
measures conditional on covariates, such as conditional risk ratios and mean
ratios, are more likely to be “transportable” across populations compared
with difference effect measures. Here, we examine the identification and
estimation of the marginal counterfactual mean difference and ratio under a
transportability assumption for conditional relative effect measures. We obtain
identification results for two scenarios that often arise in practice when
individuals in the target population (1) only have access to the control
treatment, or (2) have access to the control and other treatments but not
necessarily the experimental treatment evaluated in the trial. We then propose
multiply robust and nonparametric efficient estimators that allow for the use
of data-adaptive methods (e.g., machine learning techniques) to model the
nuisance parameters. We examine the performance of the methods in simulation
studies and illustrate their use with data from two trials of paliperidone for
patients with schizophrenia. We conclude that the proposed methods are
attractive when background knowledge suggests that the transportability
assumption for conditional relative effect measures is more plausible than
alternative assumptions.
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