Huang Chiung-Yu, Qin Jing, Follmann Dean A
Mathematical Statisticians, Biostatistics Research Branch, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892.
J Am Stat Assoc. 2008 Sep 1;103(483):1270-1280. doi: 10.1198/016214508000000625.
The pretest-posttest study design is commonly used in medical and social science research to assess the effect of a treatment or an intervention. Recently, interest has been rising in developing inference procedures that improve efficiency while relaxing assumptions used in the pretest-posttest data analysis, especially when the posttest measurement might be missing. In this article we propose a semiparametric estimation procedure based on empirical likelihood (EL) that incorporates the common baseline covariate information to improve efficiency. The proposed method also yields an asymptotically unbiased estimate of the response distribution. Thus functions of the response distribution, such as the median, can be estimated straightforwardly, and the EL method can provide a more appealing estimate of the treatment effect for skewed data. We show that, compared with existing methods, the proposed EL estimator has appealing theoretical properties, especially when the working model for the underlying relationship between the pretest and posttest measurements is misspecified. A series of simulation studies demonstrates that the EL-based estimator outperforms its competitors when the working model is misspecified and the data are missing at random. We illustrate the methods by analyzing data from an AIDS clinical trial (ACTG 175).
前后测研究设计常用于医学和社会科学研究中，以评估治疗或干预措施的效果。最近，人们对开发推理程序的兴趣日益浓厚，这些程序在放宽前后测数据分析中使用的假设的同时提高了效率，特别是在可能缺失后测测量值的情况下。在本文中，我们提出了一种基于经验似然（EL）的半参数估计程序，该程序纳入了共同的基线协变量信息以提高效率。所提出的方法还产生了响应分布的渐近无偏估计。因此，响应分布的函数，如中位数，可以直接估计，并且EL方法可以为偏态数据的治疗效果提供更有吸引力的估计。我们表明，与现有方法相比，所提出的EL估计器具有吸引人的理论性质，特别是当前后测测量之间潜在关系的工作模型被错误设定时。一系列模拟研究表明，当工作模型被错误设定且数据随机缺失时，基于EL的估计器优于其竞争对手。我们通过分析一项艾滋病临床试验（ACTG 175）的数据来说明这些方法。
