Objective: High dropout rates are common in randomized clinical trials (RCTs) for comorbid posttraumatic stress disorder and substance use disorders (PTSD + SUD). Optimizing attendance is a priority for PTSD + SUD treatment development, yet research has found few consistent associations to guide responsive strategies. In this study, we employed a data-driven pipeline for identifying salient and reliable predictors of attendance. Method: In a novel application of the iterative Random Forest algorithm (iRF), we investigated the association of individual level characteristics and session attendance in a completed RCT for PTSD + SUD (n = 70; women = 22 [31.4%]). iRF identified a group of potential predictor candidates for the total trial sessions attended; then, a Poisson regression model assessed the association between the iRF-identified factors and attendance. As a validation set, a parallel regression of significant predictors was conducted on a second, independent RCT for PTSD + SUD (n = 60; women = 48 [80%]). Results: Two testable hypotheses were derived from iRF's variable importance measures. Faster within-treatment improvement of PTSD symptoms was associated with greater session attendance with age moderating this relationship (p = .01): faster PTSD symptom improvement predicted fewer sessions attended among younger patients and more sessions among older patients. Full-time employment was also associated with fewer sessions attended (p = .02). In the validation set, the interaction between age and speed of PTSD improvement was significant (p = .05) and the employment association was not. Conclusions: Results demonstrate the potential of data-driven methods to identifying meaningful predictors as well as the dynamic contribution of symptom change during treatment to understanding RCT attendance. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

中文翻译：

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只见树木不见森林：使用机器学习方法预测同时发生的 PTSD 和物质使用障碍的试验出勤率。

目的：在共病创伤后应激障碍和物质使用障碍 (PTSD + SUD) 的随机临床试验 (RCT) 中，高辍学率很常见。优化出勤率是 PTSD + SUD 治疗开发的优先事项，但研究发现很少有一致的关联来指导响应策略。在这项研究中，我们采用了一个数据驱动的管道来识别出勤率的显着和可靠的预测因素。方法：在迭代随机森林算法 (iRF) 的一项新应用中，我们在完成的 PTSD + SUD 随机对照试验（n = 70；女性 = 22 [31.4%]）中调查了个体水平特征和会议出席率的关联。iRF 为参加的全部试验会议确定了一组潜在的预测候选者；然后，泊松回归模型评估了 iRF 识别的因素与出勤率之间的关联。作为验证集，对第二个独立的 PTSD + SUD 随机对照试验（n = 60；女性 = 48 [80%]）进行了重要预测因子的平行回归。结果：两个可检验的假设来自 iRF 的可变重要性测量。治疗期间 PTSD 症状的更快改善与更大的会议出席率相关，而年龄缓和了这种关系（p = .01）：更快的 PTSD 症状改善预示着年轻患者参加的会议更少，老年患者的会议更多。全职工作也与参加的课程较少有关（p = .02）。在验证集中，年龄和 PTSD 改善速度之间的交互作用是显着的 (p = .05)，而就业关联则不是。结论：结果证明了数据驱动方法在识别有意义的预测因子方面的潜力，以及治疗期间症状变化对了解 RCT 出勤率的动态贡献。（PsycInfo 数据库记录 (c) 2021 APA，保留所有权利）。