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Inference for two-stage adaptive treatment strategies using mixture distributions

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  • Abdus S. Wahed

Abstract

Treatment of complex diseases such as cancer, leukaemia, acquired immune deficiency syndrome and depression usually follows complex treatment regimes consisting of time varying multiple courses of the same or different treatments. The goal is to achieve the largest overall benefit defined by a common end point such as survival. Adaptive treatment strategy refers to a sequence of treatments that are applied at different stages of therapy based on the individual's history of covariates and intermediate responses to the earlier treatments. However, in many cases treatment assignment depends only on intermediate response and prior treatments. Clinical trials are often designed to compare two or more adaptive treatment strategies. A common approach that is used in these trials is sequential randomization. Patients are randomized on entry into available first-stage treatments and then on the basis of the response to the initial treatments are randomized to second-stage treatments, and so on. The analysis often ignores this feature of randomization and frequently conducts separate analysis for each stage. Recent literature suggested several semiparametric and Bayesian methods for inference related to adaptive treatment strategies from sequentially randomized trials. We develop a parametric approach using mixture distributions to model the survival times under different adaptive treatment strategies. We show that the estimators proposed are asymptotically unbiased and can be easily implemented by using existing routines in statistical software packages. Copyright (c) 2010 Royal Statistical Society.

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  • Abdus S. Wahed, 2010. "Inference for two-stage adaptive treatment strategies using mixture distributions," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(1), pages 1-18.
  • Handle: RePEc:bla:jorssc:v:59:y:2010:i:1:p:1-18
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    File URL: http://www.blackwell-synergy.com/doi/abs/10.1111/j.1467-9876.2009.00679.x
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    1. S. A. Murphy, 2003. "Optimal dynamic treatment regimes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(2), pages 331-355.
    2. Yuliya Lokhnygina & Jeffrey D. Helterbrand, 2007. "Cox Regression Methods for Two-Stage Randomization Designs," Biometrics, The International Biometric Society, vol. 63(2), pages 422-428, June.
    3. Abdus S. Wahed & Anastasios A. Tsiatis, 2006. "Semiparametric efficient estimation of survival distributions in two-stage randomisation designs in clinical trials with censored data," Biometrika, Biometrika Trust, vol. 93(1), pages 163-177, March.
    4. Jared K. Lunceford & Marie Davidian & Anastasios A. Tsiatis, 2002. "Estimation of Survival Distributions of Treatment Policies in Two-Stage Randomization Designs in Clinical Trials," Biometrics, The International Biometric Society, vol. 58(1), pages 48-57, March.
    5. Abdus S. Wahed & Anastasios A. Tsiatis, 2004. "Optimal Estimator for the Survival Distribution and Related Quantities for Treatment Policies in Two-Stage Randomization Designs in Clinical Trials," Biometrics, The International Biometric Society, vol. 60(1), pages 124-133, March.
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