Mediation analysis with a time-to-event outcome: a review of use and reporting in healthcare research

BackgroundMediation analysis tests whether the relationship between two variables is explained by a third intermediate variable. We sought to describe the usage and reporting of mediation analysis with time-to-event outcomes in published healthcare research.MethodsA systematic search of Medline, Embase, and Web of Science was executed in December 2016 to identify applications of mediation analysis to healthcare research involving a clinically relevant time-to-event outcome. We summarized usage over time and reporting of important methodological characteristics.ResultsWe included 149 primary studies, published from 1997 to 2016. Most studies were published after 2011 (n=110, 74%), and the annual number of studies nearly doubled in the last year (from n=21 to n=40). A traditional approach (causal steps or change in coefficient) was most commonly taken (n=87, 58%), and the majority of studies (n=114, 77%) used a Cox Proportional Hazards regression for the outcome. Few studies (n=52, 35%) mentioned any of the assumptions or limitations fundamental to a causal interpretation of mediation analysis.ConclusionThere is increasing use of mediation analysis with time-to-event outcomes. Current usage is limited by reliance on traditional methods and the Cox Proportional Hazards model, as well as low rates of reporting of underlying assumptions. There is a need for formal criteria to aid authors, reviewers, and readers reporting or appraising such studies.