Methods to address selection bias in post-trial studies of legacy effects were evaluated.

Zhu, L; Bell, KJL; Hayen, A

Methods to address selection bias in post-trial studies of legacy effects were evaluated.

Zhu, L Bell, KJL Hayen, A

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: J Clin Epidemiol, 2023, 160, pp. 110-116
Issue Date:: 2023-08

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Field	Value	Language
dc.contributor.author	Zhu, L
dc.contributor.author	Bell, KJL
dc.contributor.author	Hayen, A https://orcid.org/0000-0003-4046-8030
dc.date.accessioned	2023-09-12T05:25:32Z
dc.date.available	2023-05-31
dc.date.available	2023-09-12T05:25:32Z
dc.date.issued	2023-08
dc.identifier.citation	J Clin Epidemiol, 2023, 160, pp. 110-116
dc.identifier.issn	0895-4356
dc.identifier.issn	1878-5921
dc.identifier.uri	http://hdl.handle.net/10453/172061
dc.description.abstract	OBJECTIVES: Post-trial follow-up studies have become increasingly important to investigate the long-term effectiveness of interventions after randomized controlled trials (RCTs). Legacy effects refer to intervention effects that are only observed after the trial has ended and are not the direct effects observed during the trial period. However, limited attention has been given to the potential selection bias in post-trial studies. METHODS: Using directed acyclic graphs, we illustrated potential sources of selection bias in post-trial studies of cardiovascular disease preventative interventions. We constructed scenarios where selection bias was present and undertook simulations to assess the ability of different modeling approaches to correct for this bias: no adjustment, adjustment for trial baseline covariates, adjustment for post-trial covariates and inverse probability weighting (IPW) methods. Using empirical data from the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial and post-trial study, we demonstrated application of different modeling approaches. RESULTS: In the presence of selection bias, modeling without adjustment always resulted in biased estimates. Modeling with adjustment and IPW methods were able to correct the selection bias. The ACCORD study also demonstrated that while the direct effects were potentially beneficial, all models attempting to address selection bias revealed larger potential legacy effects when compared to unadjusted estimates. CONCLUSION: Post-trial follow-up studies have the potential to provide valuable information for clinical practice by detecting legacy effects. However, it is important to consider and address selection bias that may arise from the post-trial study. This study highlights the importance of using an appropriate analysis method and identifying the potential bias sources to ensure that the findings are reliable and generalizable to the target population.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	Elsevier
dc.relation.ispartof	J Clin Epidemiol
dc.relation.isbasedon	10.1016/j.jclinepi.2023.05.028
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	01 Mathematical Sciences, 11 Medical and Health Sciences
dc.subject.classification	Epidemiology
dc.subject.classification	4202 Epidemiology
dc.subject.mesh	Humans
dc.subject.mesh	Selection Bias
dc.subject.mesh	Bias
dc.subject.mesh	Follow-Up Studies
dc.subject.mesh	Diabetes Mellitus
dc.subject.mesh	Research Design
dc.subject.mesh	Humans
dc.subject.mesh	Diabetes Mellitus
dc.subject.mesh	Follow-Up Studies
dc.subject.mesh	Selection Bias
dc.subject.mesh	Research Design
dc.subject.mesh	Bias
dc.title	Methods to address selection bias in post-trial studies of legacy effects were evaluated.
dc.type	Journal Article
utslib.citation.volume	160
utslib.location.activity	United States
utslib.for	01 Mathematical Sciences
utslib.for	11 Medical and Health Sciences
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	closed_access	*
dc.date.updated	2023-09-12T05:25:31Z
pubs.publication-status	Published
pubs.volume	160

Abstract:

OBJECTIVES: Post-trial follow-up studies have become increasingly important to investigate the long-term effectiveness of interventions after randomized controlled trials (RCTs). Legacy effects refer to intervention effects that are only observed after the trial has ended and are not the direct effects observed during the trial period. However, limited attention has been given to the potential selection bias in post-trial studies. METHODS: Using directed acyclic graphs, we illustrated potential sources of selection bias in post-trial studies of cardiovascular disease preventative interventions. We constructed scenarios where selection bias was present and undertook simulations to assess the ability of different modeling approaches to correct for this bias: no adjustment, adjustment for trial baseline covariates, adjustment for post-trial covariates and inverse probability weighting (IPW) methods. Using empirical data from the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial and post-trial study, we demonstrated application of different modeling approaches. RESULTS: In the presence of selection bias, modeling without adjustment always resulted in biased estimates. Modeling with adjustment and IPW methods were able to correct the selection bias. The ACCORD study also demonstrated that while the direct effects were potentially beneficial, all models attempting to address selection bias revealed larger potential legacy effects when compared to unadjusted estimates. CONCLUSION: Post-trial follow-up studies have the potential to provide valuable information for clinical practice by detecting legacy effects. However, it is important to consider and address selection bias that may arise from the post-trial study. This study highlights the importance of using an appropriate analysis method and identifying the potential bias sources to ensure that the findings are reliable and generalizable to the target population.

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http://hdl.handle.net/10453/172061