1. Marginal structural models for case-cohort study designs to estimate the association of antiretroviral therapy initiation with incident AIDS or death.
- Author
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Cole SR, Hudgens MG, Tien PC, Anastos K, Kingsley L, Chmiel JS, and Jacobson LP
- Subjects
- Acquired Immunodeficiency Syndrome epidemiology, Acquired Immunodeficiency Syndrome mortality, Adult, Confounding Factors, Epidemiologic, Cost-Benefit Analysis, Data Interpretation, Statistical, Female, HIV Infections drug therapy, HIV Infections mortality, Humans, Male, Selection Bias, United States epidemiology, Acquired Immunodeficiency Syndrome drug therapy, Anti-HIV Agents therapeutic use, Antiretroviral Therapy, Highly Active, Cohort Studies, Epidemiologic Research Design, HIV-1, Proportional Hazards Models
- Abstract
To estimate the association of antiretroviral therapy initiation with incident acquired immunodeficiency syndrome (AIDS) or death while accounting for time-varying confounding in a cost-efficient manner, the authors combined a case-cohort study design with inverse probability-weighted estimation of a marginal structural Cox proportional hazards model. A total of 950 adults who were positive for human immunodeficiency virus type 1 were followed in 2 US cohort studies between 1995 and 2007. In the full cohort, 211 AIDS cases or deaths occurred during 4,456 person-years. In an illustrative 20% random subcohort of 190 participants, 41 AIDS cases or deaths occurred during 861 person-years. Accounting for measured confounders and determinants of dropout by inverse probability weighting, the full cohort hazard ratio was 0.41 (95% confidence interval: 0.26, 0.65) and the case-cohort hazard ratio was 0.47 (95% confidence interval: 0.26, 0.83). Standard multivariable-adjusted hazard ratios were closer to the null, regardless of study design. The precision lost with the case-cohort design was modest given the cost savings. Results from Monte Carlo simulations demonstrated that the proposed approach yields approximately unbiased estimates of the hazard ratio with appropriate confidence interval coverage. Marginal structural model analysis of case-cohort study designs provides a cost-efficient design coupled with an accurate analytic method for research settings in which there is time-varying confounding.
- Published
- 2012
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