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An Illustration of Inverse Probability Weighting to Estimate Policy-Relevant Causal Effects.

An Illustration of Inverse Probability Weighting to Estimate Policy-Relevant Causal Effects.
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Edwards JK, Cole SR, Lesko CR, Mathews WC, Moore RD, Mugavero MJ, Westreich D,


Edwards JK, Cole SR, Lesko CR, Mathews WC, Moore RD, Mugavero MJ, Westreich D, (click to view)

Edwards JK, Cole SR, Lesko CR, Mathews WC, Moore RD, Mugavero MJ, Westreich D,

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American journal of epidemiology 2016 07 28184(4) 336-44 doi 10.1093/aje/kwv339

Abstract

Traditional epidemiologic approaches allow us to compare counterfactual outcomes under 2 exposure distributions, usually 100% exposed and 100% unexposed. However, to estimate the population health effect of a proposed intervention, one may wish to compare factual outcomes under the observed exposure distribution to counterfactual outcomes under the exposure distribution produced by an intervention. Here, we used inverse probability weights to compare the 5-year mortality risk under observed antiretroviral therapy treatment plans to the 5-year mortality risk that would had been observed under an intervention in which all patients initiated therapy immediately upon entry into care among patients positive for human immunodeficiency virus in the US Centers for AIDS Research Network of Integrated Clinical Systems multisite cohort study between 1998 and 2013. Therapy-naïve patients (n = 14,700) were followed from entry into care until death, loss to follow-up, or censoring at 5 years or on December 31, 2013. The 5-year cumulative incidence of mortality was 11.65% under observed treatment plans and 10.10% under the intervention, yielding a risk difference of -1.57% (95% confidence interval: -3.08, -0.06). Comparing outcomes under the intervention with outcomes under observed treatment plans provides meaningful information about the potential consequences of new US guidelines to treat all patients with human immunodeficiency virus regardless of CD4 cell count under actual clinical conditions.

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