Joe George W, Lehman Wayne E K, Yang Yang, Knight Kevin
Texas Christian University, USA.
Eval Health Prof. 2025 Sep;48(3):374-383. doi: 10.1177/01632787231212462. Epub 2023 Nov 13.
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).
样本流失是随访研究中收集的数据进行分析时的一个混杂问题。本研究使用一种回归程序，该程序在估计插补数据时将倾向得分作为一个预测变量。通过比较这些扩充数据与原始数据的结果来探讨该程序的效用。数据来自一项随机对照研究，该研究测试了一种基于平板电脑的干预措施的效用，该干预措施旨在改善与健康风险行为相关的决策。结果包括自我报告的艾滋病毒、性传播感染和肝炎检测。使用了两个样本（社区设施中的163个和住宅设施中的348个）。社区样本中的78个和住宅样本中的238个完成了随访调查。基于逐步逻辑回归的倾向得分被用于使校准样本和缺失数据样本尽可能接近。对每个结果进行了多水平分析，并通过多重插补比较了扩充分析和原始分析的估计平均差异。插补缺失数据的模型对这三个结果有效且提高了效能。对于大多数结果，扩充数据和原始数据之间的最小二乘平均差异似乎基本相同。本方案已在https://www.clinicaltrials.gov/（NCT02777086）注册。
