Model for the Analysis of Binary Longitudinal Pain Data Subject to Informative Dropout through Remedication
提出一种选择模型，通过互补对数-对数链接和对数伽马随机效应，处理止痛药比较中因再用药导致的信息性缺失，得到边际似然的闭式表达式，模拟和实际数据验证了模型效果。
Abstract We address the problem of accounting for informative dropout in the form of rescue medication when comparing pain relievers with respect to longitudinal binary pain-relief outcomes. We present a selection model approach for binary longitudinal data that accommodates informative dropout. The relationship between dropout or remedication and the binary pain-relief response is assumed to be characterized by a random effect. That is, conditional on this random effect, response and dropout are independent. Unlike previous approaches to this problem, which rely on numerical or approximation methods, we obtain a closed-form expression for the marginal log-likelihood of response and dropout by specifying a complementary log-log link function for both components and a conjugate log-gamma random effect distribution. A data analysis supported by simulation results suggest that the model fits reasonably well. Results are compared to those obtained from conventional, but somewhat inappropriate analyses. Key Words: Complementary log-log linkDiscrete time survivalGeneralized estimating equationLog-gamma random effectsMaximum likelihood.