The National Research Council panel on prevention and treatment of missing data in clinical trials recommends that primary analysis methods are carefully selected before appropriate sensitivity analysis methods can be chosen. In this paper, we recommend an appropriate primary analysis method for handling CD4 count data from the IMPI trial and trials with similar settings. The estimand of interest in the IMPI trial is the effectiveness estimand hypothesis. We discussed, compared, and contrasted results from complete case analysis and simple imputation methods, with the direct-likelihood and multiple imputation methods. The simple imputation methods produced biased estimates of treatment effect. However, the maximum likelihood and the multiple imputation methods produced consistent estimates of treatment effect. The maximum likelihood or the multiple imputation approaches produced unbiased and consistent estimates. Therefore, either the maximum likelihood or the multiple imputation methods, under the assumption that the data are missing at random can be considered as primary analysis method when one is considering sensitivity analysis to dropout using the CD4 count data from the IMPI trial and other trials with similar settings.

翻译：国家研究理事会临床试验中缺失数据的预防和处理问题小组建议,在选择适当的敏感性分析方法之前,应仔细选择主要分析方法;在本文件中,我们建议采用一种适当的主要分析方法,处理IMPI试验和类似情况下的试验中CD4计数数据;对IMPI试验感兴趣的估计是有效性估计假设;我们讨论了完整的案例分析和简单估算方法以及直接相似和多重估算方法的比较和对比结果;简单的估算方法产生了对治疗效果的偏差估计;然而,最大可能性和多重估算方法产生了对治疗效果的一致估计;最大可能性或多重估算方法产生了不公正和一致的估计;因此,在假设数据是随机缺失的情况下,在考虑使用IMPI试验和类似情况下的其他试验的CD4计数数据进行敏感分析时,可以将最大可能性或多重估算方法视为主要分析方法。