Graham J W, Donaldson S I
Institute for Prevention Research, University of Southern California, Alhambra 91803-1358.
J Appl Psychol. 1993 Feb;78(1):119-28. doi: 10.1037/0021-9010.78.1.119.
Evaluations of psychological interventions are often criticized because of differential attrition, which is cited as a severe threat to validity. The present study shows that differential attrition is not a problem unless the mechanism causing the attrition is inaccessible (unavailable for analysis). With a simulation study, we show that conclusions about program effects (a) are unbiased when there is no differential attrition, even with usual complete cases analysis; (b) may be severely biased when based on usual complete cases analyses and there is differential attrition; (c) are unbiased when based on the expectation-maximization (EM) algorithm, even when there is differential attrition, as long as the attrition mechanism is accessible; and (d) are biased, even with the EM algorithm, when the attrition mechanism is inaccessible. Following Little and Rubin (1987), we advocate the collection of new data from a random sample of subjects with initially missing data. On the basis of these data, we propose a simple correction to the EM algorithm estimates. In our study, the correction produced unbiased estimates of program effects parameters, even with an inaccessible attrition mechanism and substantial differential attrition.
心理干预评估常常因其不同程度的失访而受到批评，这被视为对效度的严重威胁。本研究表明，不同程度的失访并非问题，除非导致失访的机制无法探究（无法进行分析）。通过一项模拟研究，我们发现关于项目效果的结论：(a) 在不存在不同程度失访的情况下，即使采用常规的完整病例分析，也是无偏的；(b) 基于常规的完整病例分析且存在不同程度失访时，可能会有严重偏差；(c) 基于期望最大化（EM）算法时，即使存在不同程度失访，只要失访机制可探究，就是无偏的；(d) 当失访机制无法探究时，即使采用EM算法，也会有偏差。遵循利特尔和鲁宾（1987年）的观点，我们主张从最初数据缺失的受试者随机样本中收集新数据。基于这些数据，我们对EM算法估计值提出了一种简单的校正方法。在我们的研究中，即使存在无法探究的失访机制和显著的不同程度失访，这种校正也能产生无偏的项目效果参数估计值。
