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Estimating Causal Treatment Effects from Longitudinal HIV Natural History Studies Using Marginal Structural Models
Author(s) -
Ko Hyejin,
Hogan Joseph W.,
Mayer Kenneth H.
Publication year - 2003
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/1541-0420.00018
Subject(s) - marginal structural model , natural history , confounding , inverse probability weighting , causal inference , medicine , psychological intervention , prospective cohort study , cohort study , weighting , demography , econometrics , statistics , mathematics , pathology , radiology , propensity score matching , psychiatry , sociology
Summary . Several recently completed and ongoing studies of the natural history of HIV infection have generated a wealth of information about its clinical progression and how this progression is altered by therepeutic interventions and environmental factors. Natural history studies typically follow prospective cohort designs, and enroll large numbers of participants for long‐term prospective follow‐up (up to several years). Using data from the HIV Epidemiology Research Study (HERS), a six‐year natural history study that enrolled 871 HIV‐infected women starting in 1993, we investigate the therapeutic effect of highly active antiretroviral therapy regimens (HAART) on CD4 cell count using the marginal structural modeling framework and associated estimation procedures based on inverse‐probability weighting (developed by Robins and colleagues). To evaluate treatment effects from a natural history study, specialized methods are needed because treatments are not randomly prescribed and, in particular, the treatment‐response relationship can be confounded by variables that are time‐varying. Our analysis uses CD4 data on all follow‐up visits over a two‐year period, and includes sensitivity analyses to investigate potential biases attributable to unmeasured confounding. Strategies for selecting ranges of a sensitivity parameter are given, as are intervals for treatment effect that reflect uncertainty attributable both to sampling and to lack of knowledge about the nature and existence of unmeasured confounding. To our knowledge, this is the first use in “real data” of Robins's sensitivity analysis for unmeasured confounding (Robins, 1999a, Synthese 121, 151–179). The findings from our analysis are consistent with recent treatment guidelines set by the U.S. Panel of the International AIDS Society (Carpenter et al., 2000, Journal of the American Medical Association 280, 381–391).