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


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-12

AUTHORS

Lauren Lapointe-Shaw, Zachary Bouck, Nicholas A. Howell, Theis Lange, Ani Orchanian-Cheff, Peter C. Austin, Noah M. Ivers, Donald A. Redelmeier, Chaim M. Bell

ABSTRACT

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. More... »

PAGES

118

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s12874-018-0578-7

DOI

http://dx.doi.org/10.1186/s12874-018-0578-7

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1107916651

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30373524


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