932Using marginal structural models to account for selection bias in the analysis of legacy effect

Zhu, L; Hayen, A; Bell, K

932Using marginal structural models to account for selection bias in the analysis of legacy effect

Zhu, L Hayen, A

Bell, K

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Publisher:: Oxford University Press (OUP)
Publication Type:: Journal Article
Citation:: International Journal of Epidemiology, 2021, 50, (Supplement_1)
Issue Date:: 2021-09-01

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Field	Value	Language
dc.contributor.author	Zhu, L
dc.contributor.author	Hayen, A https://orcid.org/0000-0003-4046-8030
dc.contributor.author	Bell, K
dc.date.accessioned	2022-01-31T23:12:25Z
dc.date.available	2022-01-31T23:12:25Z
dc.date.issued	2021-09-01
dc.identifier.citation	International Journal of Epidemiology, 2021, 50, (Supplement_1)
dc.identifier.issn	0300-5771
dc.identifier.issn	1464-3685
dc.identifier.uri	http://hdl.handle.net/10453/154008
dc.description.abstract	<jats:title>Abstract</jats:title> <jats:sec> <jats:title>Background</jats:title> <jats:p>Analyses of legacy effects are mainly based on post-trial follow-up study after initial randomized controlled trials (RCT). However, the differential event rates between arms may cause a violation of randomization balance and induce selection bias in the post-trial analysis. We conducted a simulation to illustrate the bias and explore if marginal structural model (MSM) can address it.</jats:p> </jats:sec> <jats:sec> <jats:title>Methods</jats:title> <jats:p>Our simulation combined an RCT and an extended follow-up study. The scenarios investigated include different settings of direct treatment effect, legacy effect and underlying event rate. To fit the MSM, we used the inverse probability weighting method. The performance of MSM was compared to the standard model with and without adjustment of baseline covariate.</jats:p> </jats:sec> <jats:sec> <jats:title>Results</jats:title> <jats:p>Post-trial analysis without making adjustment resulted in biased estimates, and the bias increased with the underlying event rate and treatment effect. Both MSM and standard baseline covariate adjustment equally corrected for the bias if no patients took treatment after the trial. We are currently undertaking analysis for scenarios where some people continue treatment post-trial, and there is treatment confounder feedback; these results will be presented at the Congress.</jats:p> </jats:sec> <jats:sec> <jats:title>Conclusions</jats:title> <jats:p>Estimation of legacy effects using post-trial data without adjusting for differential survival between randomised treatment arms results in biased estimates. Although both standard covariate adjustment and MSM correct the bias if no patients take treatment in the post-trial period, MSM is expected to be the best method in the more realistic scenario where some patients continue to take treatment, and there is treatment confounder feedback.</jats:p> </jats:sec> <jats:sec> <jats:title>Key messages</jats:title> <jats:p>Post-trial analysis without making adjustment results in biased estimation of legacy effect. Marginal structural models may be used to address the selection bias.</jats:p> </jats:sec>
dc.language	en
dc.publisher	Oxford University Press (OUP)
dc.relation.ispartof	International Journal of Epidemiology
dc.relation.isbasedon	10.1093/ije/dyab168.760
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0104 Statistics, 1117 Public Health and Health Services
dc.subject.classification	Epidemiology
dc.title	932Using marginal structural models to account for selection bias in the analysis of legacy effect
dc.type	Journal Article
utslib.citation.volume	50
utslib.for	0104 Statistics
utslib.for	1117 Public Health and Health Services
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	*
dc.date.updated	2022-01-31T23:12:24Z
pubs.issue	Supplement_1
pubs.publication-status	Published
pubs.volume	50
utslib.citation.issue	Supplement_1

Abstract:

Abstract Background Analyses of legacy effects are mainly based on post-trial follow-up study after initial randomized controlled trials (RCT). However, the differential event rates between arms may cause a violation of randomization balance and induce selection bias in the post-trial analysis. We conducted a simulation to illustrate the bias and explore if marginal structural model (MSM) can address it. Methods Our simulation combined an RCT and an extended follow-up study. The scenarios investigated include different settings of direct treatment effect, legacy effect and underlying event rate. To fit the MSM, we used the inverse probability weighting method. The performance of MSM was compared to the standard model with and without adjustment of baseline covariate. Results Post-trial analysis without making adjustment resulted in biased estimates, and the bias increased with the underlying event rate and treatment effect. Both MSM and standard baseline covariate adjustment equally corrected for the bias if no patients took treatment after the trial. We are currently undertaking analysis for scenarios where some people continue treatment post-trial, and there is treatment confounder feedback; these results will be presented at the Congress. Conclusions Estimation of legacy effects using post-trial data without adjusting for differential survival between randomised treatment arms results in biased estimates. Although both standard covariate adjustment and MSM correct the bias if no patients take treatment in the post-trial period, MSM is expected to be the best method in the more realistic scenario where some patients continue to take treatment, and there is treatment confounder feedback. Key messages Post-trial analysis without making adjustment results in biased estimation of legacy effect. Marginal structural models may be used to address the selection bias.

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