Inverse probability weighting in STI/HIV prevention research: Methods for evaluating social and community interventions

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Article (peer-reviewed)

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Background: Intervention effects estimated from nonrandomized intervention studies are plagued by biases, yet social or structural intervention studies are rarely randomized. There are underutilized statistical methods available to mitigate biases due to self-selection, missing data, and confounding in longitudinal, observational data permitting estimation of causal effects. We demonstrate the use of Inverse Probability Weighting (IPW) to evaluate the effect of participating in a combined clinical and social sexually transmitted infection/human immunodeficiency virus prevention intervention on reduction of incident chlamydia and gonorrhea infections among sex workers in Brazil. Methods: We demonstrate the step-by-step use of IPW, including presentation of the theoretical background, data set up, model selection for weighting, application of weights, estimation of effects using varied modeling procedures, and discussion of assumptions for use of IPW. Results: A total of 420 sex workers contributed 840 data points on incident chlamydia and gonorrhea infection. Participators were compared with nonparticipators following application of inverse probability weights to correct for differences in covariate patterns between exposure groups and between those who remained in the intervention and those who were lost-to-follow-up. Estimators using 4 model selection procedures provided estimates of intervention effect between odds ratio 0.43 (95% CI, 0.22–0.85) and 0.53 (95% CI, 0.26–1.1). Conclusions: After correcting for selection bias, loss-to-follow-up, and confounding, our analysis suggests a protective effect of participating in the intervention. Evaluations of behavioral, social, and multilevel interventions to prevent sexually transmitted infection can benefit by introduction of weighting methods such as IPW.






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