The Effectiveness of the StaySafe Intervention Using a Paradigm for Predicting Missing Outcome Data

Abstract

Sample attrition is a confounding issue in the analysis of data collected in follow-up studies. The present study uses a regression procedure that includes a propensity score as a predictor in estimating imputed data. The utility of the procedure was addressed by comparing results from this augmented data with those from the original data. Data were from a randomized controlled study testing the utility of a tablet-based intervention designed to improve decision-making with respect to health risk behaviors. Outcomes included self-reported testing for HIV, STD, and hepatitis. Two samples were used (163 in community facilities and 348 in residential facilities). Seventy-eight in the community sample and 238 in the residential sample completed follow-up surveys. Propensity scores based on a stepwise logistic regression were used to make the calibration sample and the missing data sample as close as possible. Multilevel analysis was performed for each outcome and multiple imputation compared estimated mean differences for the augmented and original analyses. The model imputing missing data was effective for the three outcomes and increased power. Least square mean differences between augmented and original data appeared to be essentially the same for most of the outcomes. This protocol has been registered with https://www.clinicaltrials.gov/(NCT02777086).