Transportability methods for failure-time outcomes under assumptions for relative effect measures

Abstract

In certain clinical areas, relative effect measures are believed to remain more constant across populations than absolute measures. This suggests that exchangeability assumptions on the relative scale may be more plausible than those on the absolute scale. Despite this, most methods for extending trial results to a target population rely on strong and often implausible distributional exchangeability assumptions between the trial and target population (distributional transportability). We propose identification results for failure-time outcomes under a more plausible assumption of exchangeability on the relative scale, focusing primarily on the risk ratio (risk ratio transportability). We applied these methods to estimate the effect of screening strategies on all-cause mortality, using data from the National Lung Screening Trial (NLST) to a nationally representative target population from the National Health Interview Survey (NHIS). All-cause mortality data was available from NHIS, along with no confounding of screening, allowing a falsification assessment of the distributional transportability analysis against the observed baseline risk.We found that this approach substantially underestimated mortality risk. In comparison, the risk ratio transportability approach produced reasonable estimates that matched the baseline risk.Thus, assumptions on relative scales may be more plausible in certain settings where distributional transportability fails.