Literature DB >> 19930189

Design and analysis of crossover trials for absorbing binary endpoints.

Martha Nason1, Dean Follmann.   

Abstract

The crossover is a popular and efficient trial design used in the context of patient heterogeneity to assess the effect of treatments that act relatively quickly and whose benefit disappears with discontinuation. Each patient can serve as her own control as within-individual treatment and placebo responses are compared. Conventional wisdom is that these designs are not appropriate for absorbing binary endpoints, such as death or HIV infection. We explore the use of crossover designs in the context of these absorbing binary endpoints and show that they can be more efficient than the standard parallel group design when there is heterogeneity in individuals' risks. We also introduce a new two-period design where first period "survivors" are rerandomized for the second period. This design combines the crossover design with the parallel design and achieves some of the efficiency advantages of the crossover design while ensuring that the second period groups are comparable by randomization. We discuss the validity of the new designs and evaluate both a mixture model and a modified Mantel-Haenszel test for inference. The mixture model assumes no carryover or period effects while the Mantel-Haenszel approach conditions out period effects. Simulations are used to compare the different designs and an example is provided to explore practical issues in implementation.
© 2009, The International Biometric Society No claim to original US government works.

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Year:  2010        PMID: 19930189     DOI: 10.1111/j.1541-0420.2009.01358.x

Source DB:  PubMed          Journal:  Biometrics        ISSN: 0006-341X            Impact factor:   2.571


  11 in total

1.  Personalized treatment selection using data from crossover designs with carry-over effects.

Authors:  Chathura Siriwardhana; K B Kulasekera; Somnath Datta
Journal:  Stat Med       Date:  2019-10-21       Impact factor: 2.373

2.  Re-randomisation trials in multi-episode settings: Estimands and independence estimators.

Authors:  Brennan C Kahan; Ian R White; Richard Hooper; Sandra Eldridge
Journal:  Stat Methods Med Res       Date:  2022-04-14       Impact factor: 2.494

3.  Who really gets strep sore throat? Confounding and effect modification of a time-varying exposure on recurrent events.

Authors:  Dean Follmann; Chiung-Yu Huang; Erin Gabriel
Journal:  Stat Med       Date:  2016-06-16       Impact factor: 2.373

Review 4.  HIV prevention trial design in an era of effective pre-exposure prophylaxis.

Authors:  Amy Cutrell; Deborah Donnell; David T Dunn; David V Glidden; Anneke Grobler; Brett Hanscom; Britt S Stancil; R Daniel Meyer; Ronnie Wang; Robert L Cuffe
Journal:  HIV Clin Trials       Date:  2017-10-17

5.  Assessing vaccine durability in randomized trials following placebo crossover.

Authors:  Jonathan Fintzi; Dean Follmann
Journal:  Stat Med       Date:  2021-04-29       Impact factor: 2.497

6.  Testing for carryover effects after cessation of treatments: a design approach.

Authors:  S Gwynn Sturdevant; Thomas Lumley
Journal:  BMC Med Res Methodol       Date:  2016-08-02       Impact factor: 4.615

7.  Assessing Durability of Vaccine Effect Following Blinded Crossover in COVID-19 Vaccine Efficacy Trials.

Authors:  Dean Follmann; Jonathan Fintzi; Michael P Fay; Holly E Janes; Lindsey Baden; Hana El Sahly; Thomas R Fleming; Devan V Mehrotra; Lindsay N Carpp; Michal Juraska; David Benkeser; Deborah Donnell; Youyi Fong; Shu Han; Ian Hirsch; Ying Huang; Yunda Huang; Ollivier Hyrien; Alex Luedtke; Marco Carone; Martha Nason; An Vandebosch; Honghong Zhou; Iksung Cho; Erin Gabriel; James G Kublin; Myron S Cohen; Lawrence Corey; Peter B Gilbert; Kathleen M Neuzil
Journal:  medRxiv       Date:  2020-12-14

8.  Independence estimators for re-randomisation trials in multi-episode settings: a simulation study.

Authors:  Brennan C Kahan; Ian R White; Sandra Eldridge; Richard Hooper
Journal:  BMC Med Res Methodol       Date:  2021-10-30       Impact factor: 4.615

9.  A re-randomisation design for clinical trials.

Authors:  Brennan C Kahan; Andrew B Forbes; Caroline J Doré; Tim P Morris
Journal:  BMC Med Res Methodol       Date:  2015-11-05       Impact factor: 4.615

10.  Using re-randomization to increase the recruitment rate in clinical trials - an assessment of three clinical areas.

Authors:  Brennan C Kahan
Journal:  Trials       Date:  2016-12-13       Impact factor: 2.279

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