Transportability without positivity: a synthesis of statistical and simulation modeling

TL;DR

This paper introduces a novel synthesis of statistical and simulation modeling to address positivity violations in transportability studies, enabling more accurate effect estimation when study samples do not fully represent the target population.

Contribution

It proposes a combined modeling approach with g-computation and inverse probability weighting to overcome positivity violations, demonstrated through experiments and an applied example.

Findings

Synthesis approach accurately addressed the research question.

Restriction methods failed to provide accurate estimates.

Model synthesis is effective with external information.

Abstract

When estimating an effect of an action with a randomized or observational study, that study is often not a random sample of the desired target population. Instead, estimates from that study can be transported to the target population. However, transportability methods generally rely on a positivity assumption, such that all relevant covariate patterns in the target population are also observed in the study sample. Strict eligibility criteria, particularly in the context of randomized trials, may lead to violations of this assumption. Two common approaches to address positivity violations are restricting the target population and restricting the relevant covariate set. As neither of these restrictions are ideal, we instead propose a synthesis of statistical and simulation models to address positivity violations. We propose corresponding g-computation and inverse probability weighting estimators. The restriction and synthesis approaches to addressing positivity violations are contrasted with a simulation experiment and an illustrative example in the context of sexually transmitted infection testing uptake. In both cases, the proposed synthesis approach accurately addressed the original research question when paired with a thoughtfully selected simulation model. Neither of the restriction approaches were able to accurately address the motivating question. As public health decisions must often be made with imperfect target population information, model synthesis is a viable approach given a combination of empirical data and external information based on the best available knowledge.