Mixed effects logistic regression models for longitudinal binary response data with informative drop-out.
Mixed effects logistic regression models for longitudinal binary response data with informative drop-out.
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DOI：
10.2307/2534023
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发表时间：
1998-03
期刊：
影响因子：
1.9
通讯作者：
Thomas R. Ten Have;Allen R. Kunselman;Erik P. Pulkstenis;J. Richard Landis
中科院分区：
文献类型：
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作者：
Thomas R. Ten Have;Allen R. Kunselman;Erik P. Pulkstenis;J. Richard Landis
本文提出了一个具有逻辑连接的共享参数模型，用于纵向二项响应数据，以适应信息脱落。该模型由共享随机效应参数的观测纵向和缺失响应分量组成。据我们所知，这是第一次提出这样一个模型的纵向二进制响应数据。在临床试验数据集和模拟方面的近似条件logit模型进行比较。天真的混合效应logit模型，不考虑信息脱落也进行了比较。模型之间在置信区间覆盖率、偏倚和均方误差（MSE）方面基于模拟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分量所显示的受试者间变异量。当共享参数模型成立时，近似条件模型提供的置信区间对类内因子具有良好的覆盖率，但对类间因子则没有。对于朴素模型，情况正好相反。在不同的脱落机制下，当脱落概率仅取决于当前未观察到的观察结果时，所有三个模型的表现相似，提供了受试者间置信区间，具有良好的覆盖率和可比的MSE和偏倚，但受试者内置信区间、MSE和偏倚较差。与共享参数和近似条件模型相比，朴素模型在受试者内效应方面表现更差。数据分析需要比较两种止痛药和安慰剂在止痛方面的效果，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脱落过程的模拟结果。数据分析和模拟结果之间的这种比较可以提供共享参数模型适用于疼痛数据的证据。
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.