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Marginal Regression for Binary Longitudinal Data in Adaptive Clinical Trials

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  • BRAJENDRA C. SUTRADHAR
  • ATANU BISWAS
  • WASIMUL BARI

Abstract

. In an adaptive clinical trial research, it is common to use certain data‐dependent design weights to assign individuals to treatments so that more study subjects are assigned to the better treatment. These design weights must also be used for consistent estimation of the treatment effects as well as the effects of the other prognostic factors. In practice, there are however situations where it may be necessary to collect binary responses repeatedly from an individual over a period of time and to obtain consistent estimates for the treatment effect as well as the effects of the other covariates in such a binary longitudinal set up. In this paper, we introduce a binary response‐based longitudinal adaptive design for the allocation of individuals to a better treatment and propose a weighted generalized quasi‐likelihood approach for the consistent and efficient estimation of the regression parameters including the treatment effects.

Suggested Citation

  • Brajendra C. Sutradhar & Atanu Biswas & Wasimul Bari, 2005. "Marginal Regression for Binary Longitudinal Data in Adaptive Clinical Trials," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 32(1), pages 93-113, March.
    • Handle: RePEc:bla:scjsta:v:32:y:2005:i:1:p:93-113
    DOI: 10.1111/j.1467-9469.2005.00417.x
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    Cited by:

    1. Atkinson, Anthony C. & Biswas, Atanu, 2017. "Optimal response and covariate-adaptive biased-coin designs for clinical trials with continuous multivariate or longitudinal responses," Computational Statistics & Data Analysis, Elsevier, vol. 113(C), pages 297-310.
    2. Atkinson, Anthony C. & Biswas, Atanu, 2017. "Optimal response and covariate-adaptive biased-coin designs for clinical trials with continuous multivariate or longitudinal responses," LSE Research Online Documents on Economics 66761, London School of Economics and Political Science, LSE Library.

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