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Quantifying an Adherence Path-Specific Effect of Antiretroviral Therapy in the Nigeria PEPFAR Program

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  • Caleb H. Miles
  • Ilya Shpitser
  • Phyllis Kanki
  • Seema Meloni
  • Eric J. Tchetgen Tchetgen

Abstract

Since the early 2000s, evidence has accumulated for a significant differential effect of first-line antiretroviral therapy (ART) regimens on human immunodeficiency virus (HIV) viral load suppression. This finding was replicated in our data from the Harvard President’s Emergency Plan for AIDS Relief (PEPFAR) program in Nigeria. Investigators were interested in finding the source of these differences, that is, understanding the mechanisms through which one regimen outperforms another, particularly via adherence. This question can be naturally formulated via mediation analysis with adherence playing the role of a mediator. Existing mediation analysis results, however, have relied on an assumption of no exposure-induced confounding of the intermediate variable, and generally require an assumption of no unmeasured confounding for nonparametric identification. Both assumptions are violated by the presence of drug toxicity. In this article, we relax these assumptions and show that certain path-specific effects remain identified under weaker conditions. We focus on the path-specific effect solely mediated by adherence and not by toxicity and propose an estimator for this effect. We illustrate with simulations and present results from a study applying the methodology to the Harvard PEPFAR data. Supplementary materials for this article are available online.

Suggested Citation

  • Caleb H. Miles & Ilya Shpitser & Phyllis Kanki & Seema Meloni & Eric J. Tchetgen Tchetgen, 2017. "Quantifying an Adherence Path-Specific Effect of Antiretroviral Therapy in the Nigeria PEPFAR Program," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(520), pages 1443-1452, October.
  • Handle: RePEc:taf:jnlasa:v:112:y:2017:i:520:p:1443-1452
    DOI: 10.1080/01621459.2017.1295862
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    Cited by:

    1. Guanglei Hong & Fan Yang & Xu Qin, 2023. "Posttreatment confounding in causal mediation studies: A cutting‐edge problem and a novel solution via sensitivity analysis," Biometrics, The International Biometric Society, vol. 79(2), pages 1042-1056, June.
    2. Xiang Zhou, 2022. "Semiparametric estimation for causal mediation analysis with multiple causally ordered mediators," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(3), pages 794-821, July.
    3. Ryan M. Andrews & Ilya Shpitser & Oscar Lopez & William T. Longstreth & Paulo H. M. Chaves & Lewis Kuller & Michelle C. Carlson, 2020. "Examining the causal mediating role of brain pathology on the relationship between diabetes and cognitive impairment: the Cardiovascular Health Study," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(4), pages 1705-1726, October.

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