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Partially Hidden Markov Model for Time-Varying Principal Stratification in HIV Prevention Trials

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  • James Y. Dai
  • Peter B. Gilbert
  • Benoît R. Mâsse

Abstract

It is frequently of interest to estimate the intervention effect that adjusts for post-randomization variables in clinical trials. In the recently completed HPTN 035 trial, there is differential condom use between the three microbicide gel arms and the no-gel control arm, so intention-to-treat (ITT) analyses only assess the net treatment effect that includes the indirect treatment effect mediated through differential condom use. Various statistical methods in causal inference have been developed to adjust for post-randomization variables. We extend the principal stratification framework to time-varying behavioral variables in HIV prevention trials with a time-to-event endpoint, using a partially hidden Markov model (pHMM). We formulate the causal estimand of interest, establish assumptions that enable identifiability of the causal parameters, and develop maximum likelihood methods for estimation. Application of our model on the HPTN 035 trial reveals an interesting pattern of prevention effectiveness among different condom-use principal strata.

Suggested Citation

  • James Y. Dai & Peter B. Gilbert & Benoît R. Mâsse, 2012. "Partially Hidden Markov Model for Time-Varying Principal Stratification in HIV Prevention Trials," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(497), pages 52-65, March.
    • Handle: RePEc:taf:jnlasa:v:107:y:2012:i:497:p:52-65
    DOI: 10.1080/01621459.2011.643743
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    References listed on IDEAS

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    1. Scott, Steven L. & James, Gareth M. & Sugar, Catherine A., 2005. "Hidden Markov Models for Longitudinal Comparisons," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 359-369, June.
    2. Leroux, Brian G., 1992. "Maximum-likelihood estimation for hidden Markov models," Stochastic Processes and their Applications, Elsevier, vol. 40(1), pages 127-143, February.
    3. Constantine E. Frangakis & Donald B. Rubin, 2002. "Principal Stratification in Causal Inference," Biometrics, The International Biometric Society, vol. 58(1), pages 21-29, March.
    4. Peter B. Gilbert & Ronald J. Bosch & Michael G. Hudgens, 2003. "Sensitivity Analysis for the Assessment of Causal Vaccine Effects on Viral Load in HIV Vaccine Trials," Biometrics, The International Biometric Society, vol. 59(3), pages 531-541, September.
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    1. Ruijin Lu & Tonja R. Nansel & Zhen Chen, 2023. "A Perception-Augmented Hidden Markov Model for Parent–Child Relations in Families of Youth with Type 1 Diabetes," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 15(1), pages 288-308, April.

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