Mediation analysis with a time-to-event outcome: a review of use and reporting in healthcare research - Lapointe-Shaw, Lauren; Bouck, Zachary; Howell, Nicholas A.; Lange, Theis; Orchanian-Cheff, Ani; Austin, Peter C.; Ivers, Noah M.; Redelmeier, Donald A.; Bell, Chaim M. (BMC medical research methodology, 2018)
Background Mediation 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. Methods A 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. Results We 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. Conclusion There 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.