Literature DB >> 28960243

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

Liangyuan Hu1, Joseph W Hogan2, Ann W Mwangi3, Abraham Siika3.   

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.
© 2017, The International Biometric Society.

Entities:  

Keywords:  Antiretroviral therapy (ART); Electronic health records; Informative censoring; Inverse weighting; Marginal structural model; Time-varying confounders

Mesh:

Substances:

Year:  2017        PMID: 28960243      PMCID: PMC5874166          DOI: 10.1111/biom.12780

Source DB:  PubMed          Journal:  Biometrics        ISSN: 0006-341X            Impact factor:   2.571


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