Handling Baseline Differences and Missing Items in a Longitudinal Study of HIV Risk Among Runaway Youths

Abstract

Many longitudinal studies in field settings present challenges due to selection bias and incomplete data. A motivating example is provided by an intervention study aimed at preventing HIV transmission among runaway youths housed at shelters in New York City. Two shelters with 167 youths received the intervention, and two shelters with 144 youths received the control treatment. The number of unprotected sexual acts in the prior three months for each youth was assessed at a baseline interview and (to the extent possible) at five follow-up time points. Among observed items, there is strong evidence of a lack of balance on baseline characteristics between the intervention and control groups; meanwhile, beyond occasional missing items among participants interviewed at baseline, there were three items about baseline characteristics added after the study began, resulting in a few items being missing on a large percentage of the study sample. Here, we outline two strategies for handling the complexities of this data set, both of which make use of propensity scores to address the imbalances across treatment groups. One approach relies on available cases and ad hoc choices to simplify the steps leading up to a linear mixed model analysis in SAS PROC MIXED; the other approach uses multiple imputation strategies to reflect uncertainty due to missing values in an analogous linear mixed model analysis. Ultimately we did not find substantial qualitative differences in this setting between the available-case and imputed-data approaches. But in both cases, we find that the considerable imbalance on covariates between treatment arms constrains the ability to draw inferences about the intervention effect, suggesting the importance of evaluating propensity-score distributions in quasi-experimental intervention research.