LAUSR

OBJECTIVES "Legacy effects" describe the phenomena where treatment effects are apparent during the post-trial period that are not attributable to the direct effects observed within the trial. We investigate different approaches to analysis of trial and extended follow up data for the evaluation of legacy effects. STUDY DESIGN AND SETTING We conducted a simulation to compare three approaches, which differed in terms of the time period and selection of trial participants included in the analysis. RESULTS The most common approach used for estimating legacy effects in the literature, which combines initial trial and post-trial follow-up data, gave the most biased estimates. Approaches using post-RCT data had better performance in most scenarios. When the size of the legacy effect was set to differ according to whether or not drugs were taken post-trial, the stratified approach using post-trial data but only from participants taking the drug post-trial performed were less biased but often had lower power to detect a legacy effect. CONCLUSION When estimating legacy effects, approaches to analysis that are restricted to post-trial follow-up data are preferred. If data are available on participant drug use post-trial, then both stratified and un-stratified approaches to analysis of the post-trial data should be investigated.