Intent-to-treat analysis for longitudinal studies with drop-outs
Intent-to-treat analysis for longitudinal studies with drop-outs
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作者：
Little, R;Yau, L
我们考虑对涉及可能退出的纵向数据的临床试验进行意向治疗（IT）分析。常见方法，例如最后观察结转插补或基于假设随机丢失模型的不完整数据方法，在 IT 设置中存在严重缺陷。我们提出了一种方法，该方法涉及基于“治疗”模型对退出后的缺失值进行多重插补，如果已知，则使用退出后的实际剂量，或者如果未知，则使用包含各种合理替代假设的插补剂量。然后使用 IT 方法对多重插补数据集进行分析，其中受试者按随机分组而不是实际接受的剂量进行分类。使用 Rubin 的方法（1987 年，调查中无答复的多重插补）合并多重插补数据集的结果。该方法的一个新颖特征是插补模型与用于分析填充数据的模型不同。该方法应用于他克林治疗阿尔茨海默病的临床试验数据。
We consider intent-to-treat (IT) analysis of clinical trials involving longitudinal data subject to drop-out. Common methods, such as Last Observation Carried Forward imputation or incomplete-data methods based on models that assume random dropout, have serious drawbacks in the IT setting. We propose a method that involves multiple imputation of the missing values following drop-out based on an ''as treated'' model, using actual dose after drop-out if this is known, or imputed doses that incorporate a variety of plausible alternative assumptions if unknown. The multiply-imputed data sets are then analyzed using IT methods, where subjects are classified by randomization group rather than by the dose actually received. Results from the multiply-imputed data sets are combined using the methods of Rubin (1987, Multiple Imputation for Nonresponse in Surveys). A novel feature of the proposed method is that the models for imputation differ from the model used for the analysis of the filled-in data. The method is applied to data on a clinical trial for Tacrine in the treatment of Alzheimer's disease.