Comparisons of Methods for Analysis of Repeated Binary Responses with Missing Data
Comparisons of Methods for Analysis of Repeated Binary Responses with Missing Data
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DOI：
10.1080/10543401003687129
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发表时间：
2011-01-01
影响因子：
1.1
通讯作者：
Zhan, Xiaojiang
中科院分区：
文献类型：
--
作者：
Liu, G. Frank;Zhan, Xiaojiang
选择合适的分析方法来分析缺失数据的重复二元响应非常重要，但也具有挑战性。使用最后一次观察结转 (LOCF) 方法的传统方法在参数估计和假设检验中都可能存在偏差。广义估计方程（GEE）方法仅在缺失数据完全随机缺失时才有效，这在许多临床试验中可能无法满足。文献中已经提出了几种基于似然或伪似然方法以及基于多重插补的方法的随机效应模型。在本文中，我们使用全似然法或伪似然法、GEE 和几种多重插补方法来评估随机效应模型。仿真用于比较这些方法在不同仿真设置下的结果和性能。
It is important yet challenging to choose an appropriate analysis method for the analysis of repeated binary responses with missing data. The conventional method using the last observation carried forward (LOCF) approach can be biased in both parameter estimates and hypothesis tests. The generalized estimating equations (GEE) method is valid only when missing data are missing completely at random, which may not be satisfied in many clinical trials. Several random-effects models based on likelihood or pseudo-likelihood methods and multiple-imputation-based methods have been proposed in the literature. In this paper, we evaluate the random-effects models with full- or pseudo-likelihood methods, GEE, and several multiple-imputation approaches. Simulations are used to compare the results and performance among these methods under different simulation settings.