Evaluating Nonresponse Weighting Adjustment for the Population-Based HIV Impact Assessment Surveys on Incorporating Survey Outcomes

Background: The nonresponse weighting adjustment of the Population-based HIV Impact Assessment (PHIA) surveys uses the weighting class method in combination with a tree analysis to identify predictors significant to response propensity. Variable selection for this type of nonresponse adjustment identifies auxiliary variables correlated with response propensity alone and produces 1 set of weights applicable for all analyses of the survey data. An alternative approach identifies auxiliary variables correlated to both the response probability and selected key outcome variables. This approach may identify a different set of variables for the nonresponse adjustments and may produce more efficient estimates for the key outcome variables.

Setting: The PHIA surveys from 2016 to 2017.

Methods: Weighting class, joint-classification, and two-step modeling.

Results: There was little difference among estimates produced by the alternative weighting methods and the PHIA estimates. The joint-classification method produced more efficient estimates (ie, smaller design effects) compared with the PHIA method, while the two-step method was inconclusive.

Conclusions: The efficiency of the estimates produced by the PHIA weighting method closely resembles those specifically targeted at key survey outcomes and serves well as a multipurpose weight.