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Modeling the causal effect of treatment initiation time on survival: Application to HIV/TB co†infection

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  • Liangyuan Hu
  • Joseph W. Hogan
  • Ann W. Mwangi
  • Abraham Siika

Abstract

The timing of antiretroviral therapy (ART) initiation for HIV and tuberculosis (TB) co†infected patients needs to be considered carefully. CD4 cell count can be used to guide decision making about when to initiate ART. Evidence from recent randomized trials and observational studies generally supports early initiation but does not provide information about effects of initiation time on a continuous scale. In this article, we develop and apply a highly flexible structural proportional hazards model for characterizing the effect of treatment initiation time on a survival distribution. The model can be fitted using a weighted partial likelihood score function. Construction of both the score function and the weights must accommodate censoring of the treatment initiation time, the outcome, or both. The methods are applied to data on 4903 individuals with HIV/TB co†infection, derived from electronic health records in a large HIV care program in Kenya. We use a model formulation that flexibly captures the joint effects of ART initiation time and ART duration using natural cubic splines. The model is used to generate survival curves corresponding to specific treatment initiation times; and to identify optimal times for ART initiation for subgroups defined by CD4 count at time of TB diagnosis. Our findings potentially provide ‘higher resolution’ information about the relationship between ART timing and mortality, and about the differential effect of ART timing within CD4 subgroups.

Suggested Citation

  • Liangyuan Hu & Joseph W. Hogan & Ann W. Mwangi & Abraham Siika, 2018. "Modeling the causal effect of treatment initiation time on survival: Application to HIV/TB co†infection," Biometrics, The International Biometric Society, vol. 74(2), pages 703-713, June.
  • Handle: RePEc:bla:biomet:v:74:y:2018:i:2:p:703-713
    DOI: 10.1111/biom.12780
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    References listed on IDEAS

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    1. Brent A. Johnson & Anastasios A. Tsiatis, 2005. "Semiparametric inference in observational duration-response studies, with duration possibly right-censored," Biometrika, Biometrika Trust, vol. 92(3), pages 605-618, September.
    2. Brent A. Johnson & Anastasios A. Tsiatis, 2004. "Estimating Mean Response as a Function of Treatment Duration in an Observational Study, Where Duration May Be Informatively Censored," Biometrics, The International Biometric Society, vol. 60(2), pages 315-323, June.
    3. Yongling Xiao & Michal Abrahamowicz & Erica E. M. Moodie & Rainer Weber & James Young, 2014. "Flexible Marginal Structural Models for Estimating the Cumulative Effect of a Time-Dependent Treatment on the Hazard: Reassessing the Cardiovascular Risks of Didanosine Treatment in the Swiss HIV Coho," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(506), pages 455-464, June.
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    Cited by:

    1. Xin Chen & Rui Song & Jiajia Zhang & Swann Arp Adams & Liuquan Sun & Wenbin Lu, 2022. "On estimating optimal regime for treatment initiation time based on restricted mean residual lifetime," Biometrics, The International Biometric Society, vol. 78(4), pages 1377-1389, December.
    2. Xiaofei Chen & Daniel F. Heitjan & Gerald Greil & Haekyung Jeon‐Slaughter, 2021. "Estimating the optimal timing of surgery from observational data," Biometrics, The International Biometric Society, vol. 77(2), pages 729-739, June.
    3. Liangyuan Hu & Lihua Li, 2022. "Using Tree-Based Machine Learning for Health Studies: Literature Review and Case Series," IJERPH, MDPI, vol. 19(23), pages 1-13, December.
    4. Hao Sun & Ashkan Ertefaie & Brent A. Johnson, 2022. "Estimating mean potential outcome under adaptive treatment length strategies in continuous time," Biometrics, The International Biometric Society, vol. 78(4), pages 1503-1514, December.

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