Optimizing the Analysis of Adherence Interventions Using Logistic Generalized Estimating Equations
Optimizing the Analysis of Adherence Interventions Using Logistic Generalized Estimating Equations
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DOI：
10.1007/s10461-011-9955-5
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发表时间：
2012-02-01
影响因子：
4.4
通讯作者：
Simoni, Jane M.
中科院分区：
文献类型：
--
作者：
Huh, David;Flaherty, Brian P.;Simoni, Jane M.
由于统计方法不够敏感，旨在改善艾滋病毒药物治疗依从性的干预措施可能被视为无效而不予考虑。横截面技术，如t检验是常见的领域，但可能不准确，由于机会发现和正态分布的无效假设的风险增加。在随机对照试验的二次分析中，使用逻辑广义估计方程（GEE）的两种方法-计划对比和生长曲线-被检查用于评估依从性数据的百分比。将逻辑GEE方法的结果与经典方差分析（ANOVA）进行比较。稳健和自举估计用于获得经验标准误差估计。Logistic GEE与计划对比或生长曲线结合稳健的标准误估计上级经典的方差分析检测干预效果。纵向模型的选择导致了推理的关键差异。应用研究人员的影响和建议进行了讨论。
Interventions aimed at improving HIV medication adherence could be dismissed as ineffective due to statistical methods that are not sufficiently sensitive. Cross-sectional techniques such as t tests are common to the field, but potentially inaccurate due to increased risk of chance findings and invalid assumptions of normal distribution. In a secondary analysis of a randomized controlled trial, two approaches using logistic generalized estimating equations (GEE)-planned contrasts and growth curves-were examined for evaluating percent adherence data. Results of the logistic GEE approaches were compared to classical analysis of variance (ANOVA). Robust and bootstrapped estimation was used to obtain empirical standard error estimates. Logistic GEE with either planned contrasts or growth curves in combination with robust standard error estimates was superior to classical ANOVA for detecting intervention effects. The choice of longitudinal model led to key differences in inference. Implications and recommendations for applied researchers are discussed.