Ten Have T R, Kunselman A R, Pulkstenis E P, Landis J R
Center for Epidemiology and Biostatistics, University of Pennsylvania School of Medicine, Philadelphia 19104-6021, USA.
Biometrics. 1998 Mar;54(1):367-83.
A shared parameter model with logistic link is presented for longitudinal binary response data to accommodate informative drop-out. The model consists of observed longitudinal and missing response components that share random effects parameters. To our knowledge, this is the first presentation of such a model for longitudinal binary response data. Comparisons are made to an approximate conditional logit model in terms of a clinical trial dataset and simulations. The naive mixed effects logit model that does not account for informative drop-out is also compared. The simulation-based differences among the models with respect to coverage of confidence intervals, bias, and mean squared error (MSE) depend on at least two factors: whether an effect is a between- or within-subject effect and the amount of between-subject variation as exhibited by variance components of the random effects distributions. When the shared parameter model holds, the approximate conditional model provides confidence intervals with good coverage for within-cluster factors but not for between-cluster factors. The converse is true for the naive model. Under a different drop-out mechanism, when the probability of drop-out is dependent only on the current unobserved observation, all three models behave similarly by providing between-subject confidence intervals with good coverage and comparable MSE and bias but poor within-subject confidence intervals, MSE, and bias. The naive model does more poorly with respect to the within-subject effects than do the shared parameter and approximate conditional models. The data analysis, which entails a comparison of two pain relievers and a placebo with respect to pain relief, conforms to the simulation results based on the shared parameter model but not on the simulation based on the outcome-driven drop-out process. This comparison between the data analysis and simulation results may provide evidence that the shared parameter model holds for the pain data.
提出了一种具有逻辑链接的共享参数模型，用于纵向二元响应数据，以适应信息性失访。该模型由共享随机效应参数的观察到的纵向和缺失响应成分组成。据我们所知，这是首次针对纵向二元响应数据提出此类模型。根据一个临床试验数据集和模拟，将其与近似条件logit模型进行了比较。还比较了未考虑信息性失访的朴素混合效应logit模型。模型之间基于模拟的置信区间覆盖范围、偏差和均方误差（MSE）的差异至少取决于两个因素：效应是受试者间效应还是受试者内效应，以及随机效应分布的方差成分所表现出的受试者间变异量。当共享参数模型成立时，近似条件模型为聚类内因素提供具有良好覆盖范围的置信区间，但对聚类间因素则不然。朴素模型的情况则相反。在不同的失访机制下，当失访概率仅取决于当前未观察到的观测值时，所有三个模型的表现类似，提供具有良好覆盖范围的受试者间置信区间以及可比的MSE和偏差，但受试者内置信区间、MSE和偏差较差。朴素模型在受试者内效应方面比共享参数模型和近似条件模型表现更差。数据分析涉及比较两种止痛药和一种安慰剂在缓解疼痛方面的效果，符合基于共享参数模型的模拟结果，但不符合基于结果驱动的失访过程的模拟结果。数据分析与模拟结果之间的这种比较可能提供证据表明共享参数模型适用于疼痛数据。
