Alternative models and randomization techniques for Bayesian response-adaptive randomization with binary outcomes.
Background: Bayesian response-adaptive designs, which data adaptively alter the allocation ratio in favor of the better performing treatment, are often criticized for engendering a non-trivial probability of a subject imbalance in favor of the inferior treatment, inflating type I error rate, and inc...
| Published in: | Clinical Trials Vol. 18; no. 4; pp. 417 - 427 |
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| Main Authors: | , , |
| Format: | equations & formulas research tables/charts Journal Article |
| Published: |
Sage Publications, Ltd.
Aug2021
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=151485250&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 151485250 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 17407745 AB6 jtl: Clinical Trials issn: 17407745 maglogo: N pubinfo: dt: Aug2021 vid: 18 iid: 4 pid: 33180 pub: Sage Publications, Ltd. place: <Blank> artinfo: ui: 151485250 150222597 151485250 151485250 10.1177/17407745211010139 151485250 ppf: 417 ppct: 10 formats: tig: atl: Alternative models and randomization techniques for Bayesian response-adaptive randomization with binary outcomes. aug: au: Proper, Jennifer Connett, John Murray, Thomas affil: Division of Biostatistics, University of Minnesota Twin Cities, Minneapolis, MN, USA sug: subj: Clinical Trials Methods Random Assignment Methods Probability Models, Statistical Human Study Design Computer Simulation Random Sample Methods Logistic Regression Sample Size Sampling Error ab: Background: Bayesian response-adaptive designs, which data adaptively alter the allocation ratio in favor of the better performing treatment, are often criticized for engendering a non-trivial probability of a subject imbalance in favor of the inferior treatment, inflating type I error rate, and increasing sample size requirements. The implementation of these designs using the Thompson sampling methods has generally assumed a simple beta-binomial probability model in the literature; however, the effect of these choices on the resulting design operating characteristics relative to other reasonable alternatives has not been fully examined. Motivated by the Advanced R2 Eperfusion STrategies for Refractory Cardiac Arrest trial, we posit that a logistic probability model coupled with an urn or permuted block randomization method will alleviate some of the practical limitations engendered by the conventional implementation of a two-arm Bayesian response-adaptive design with binary outcomes. In this article, we discuss up to what extent this solution works and when it does not. Methods: A computer simulation study was performed to evaluate the relative merits of a Bayesian response-adaptive design for the Advanced R2 Eperfusion STrategies for Refractory Cardiac Arrest trial using the Thompson sampling methods based on a logistic regression probability model coupled with either an urn or permuted block randomization method that limits deviations from the evolving target allocation ratio. The different implementations of the response-adaptive design were evaluated for type I error rate control across various null response rates and power, among other performance metrics. Results: The logistic regression probability model engenders smaller average sample sizes with similar power, better control over type I error rate, and more favorable treatment arm sample size distributions than the conventional beta-binomial probability model, and designs using the alternative randomization methods have a negligible chance of a sample size imbalance in the wrong direction. Conclusion: Pairing the logistic regression probability model with either of the alternative randomization methods results in a much improved response-adaptive design in regard to important operating characteristics, including type I error rate control and the risk of a sample size imbalance in favor of the inferior treatment. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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