Estimated legacy effects from simulated post-trial data were less biased than from combined trial/post-trial data

Zhu, L; Bell, KJL; Hayen, A

Estimated legacy effects from simulated post-trial data were less biased than from combined trial/post-trial data

Zhu, L Bell, KJL Hayen, A

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Publication Type:: Journal Article
Citation:: Journal of Clinical Epidemiology, 2019, 114 pp. 30 - 37
Issue Date:: 2019-10-01

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Field	Value	Language
dc.contributor.author	Zhu, L	en_US
dc.contributor.author	Bell, KJL	en_US
dc.contributor.author	Hayen, A https://orcid.org/0000-0003-4046-8030	en_US
dc.date.available	2020-05-25T19:18:02Z
dc.date.issued	2019-10-01	en_US
dc.identifier.citation	Journal of Clinical Epidemiology, 2019, 114 pp. 30 - 37	en_US
dc.identifier.issn	0895-4356	en_US
dc.identifier.uri	http://hdl.handle.net/10453/135234
dc.description.abstract	© 2019 Elsevier Inc. 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–randomized controlled trial 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 after trial, the stratified approach using post-trial data but only from participants taking the drug after trial was 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 after trial, then both stratified and unstratified approaches to analysis of the post-trial data should be investigated.	en_US
dc.relation.ispartof	Journal of Clinical Epidemiology	en_US
dc.relation.isbasedon	10.1016/j.jclinepi.2019.05.010	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	Epidemiology	en_US
dc.title	Estimated legacy effects from simulated post-trial data were less biased than from combined trial/post-trial data	en_US
dc.type	Journal Article
utslib.citation.volume	114	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	1103 Clinical Sciences	en_US
utslib.for	01 Mathematical Sciences	en_US
utslib.for	11 Medical and Health Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Health
pubs.organisational-group	/University of Technology Sydney/Faculty of Health/Public Health
utslib.copyright.status	open_access	*
pubs.publication-status	Published	en_US
pubs.volume	114	en_US

Abstract:

© 2019 Elsevier Inc. 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–randomized controlled trial 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 after trial, the stratified approach using post-trial data but only from participants taking the drug after trial was 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 after trial, then both stratified and unstratified approaches to analysis of the post-trial data should be investigated.

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http://hdl.handle.net/10453/135234