Laboratory for Psychiatric Biostatistics, McLean Hospital, Belmont, MA, USA; Department of Psychiatry, Harvard Medical School, Boston, MA, USA.
Division of General Medicine, Brigham and Womens Hospital, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA.
Drug Alcohol Depend. 2023 Sep 1;250:110897. doi: 10.1016/j.drugalcdep.2023.110897. Epub 2023 Jul 13.
Missing data are a ubiquitous problem in longitudinal substance use disorder (SUD) clinical trials. In particular, the rates of missingness are often high and study participants often intermittently skip their scheduled outcome assessments, leading to so-called "non-monotone" missing data patterns. Moreover, when the primary outcome is a measure of substance use, study investigators often have strong prior beliefs based on their clinical experience that those participants with missing data are more likely to be using substances at those occasions, i.e., data are missing not at random (MNAR). Although approaches for handling missing data are well-developed when the missing data patterns are monotone, arising primarily from study participants withdrawing from the trial prematurely, fewer methods are available for non-monotone missingness. In this paper we review some conventional, as well as more novel, methods for handling non-monotone missingness in SUD trials when the repeatedly measured outcome variable is binary (e.g., denoting presence/absence of substance use). We compare and contrast the different approaches using data from a longitudinal clinical trial of four psychosocial treatments from the Collaborative Cocaine Treatment Study. We conclude by making some recommendations to the SUD research community concerning how more principled methods for handling missing data can be incorporated in the analysis and reporting of trial results.
在纵向物质使用障碍 (SUD) 临床试验中，缺失数据是一个普遍存在的问题。特别是，缺失率通常很高，研究参与者经常不定期地跳过预定的结果评估，导致所谓的“非单调”缺失数据模式。此外，当主要结果是物质使用的衡量标准时，研究调查人员通常根据他们的临床经验，对那些有缺失数据的参与者在这些情况下更有可能使用物质有强烈的先验信念，即数据的缺失不是随机的 (MNAR)。尽管在缺失数据模式单调时，处理缺失数据的方法已经很成熟，主要是由于研究参与者过早退出试验，但对于非单调缺失数据，可用的方法较少。在本文中，我们回顾了一些传统的和更新颖的方法，用于处理 SUD 试验中当重复测量的结果变量为二进制时的非单调缺失（例如，表示物质使用的存在/不存在）。我们使用来自合作可卡因治疗研究的四项心理社会治疗的纵向临床试验的数据来比较和对比不同的方法。最后，我们向 SUD 研究界提出一些建议，关于如何在分析和报告试验结果中纳入更有原则性的缺失数据处理方法。
