Shardell Michelle, El-Kamary Samer S
Department of Epidemiology and Preventive Medicine, University of Maryland School of Medicine, Baltimore, Maryland, USA.
J Biopharm Stat. 2009 Nov;19(6):1018-38. doi: 10.1080/10543400903242779.
We use the framework of coarsened data to motivate performing sensitivity analysis in the presence of incomplete data. To perform the sensitivity analysis, we specify pattern-mixture models to allow departures from the assumption of coarsening at random, a generalization of missing at random and independent censoring. We apply the concept of coarsening to address potential bias from missing data and interval-censored data in a randomized controlled trial of an herbal treatment for acute hepatitis. Computer code using SAS PROC NLMIXED for fitting the models is provided.
我们使用粗化数据框架来推动在存在不完整数据的情况下进行敏感性分析。为了进行敏感性分析，我们指定模式混合模型，以允许偏离随机粗化假设，这是随机缺失和独立删失的一种推广。我们应用粗化概念来解决在一项治疗急性肝炎的草药随机对照试验中缺失数据和区间删失数据导致的潜在偏差。提供了使用SAS PROC NLMIXED拟合模型的计算机代码。
