Background: Missing cases present a challenge to our ability to evaluate the effects of web-based psychotherapy trials. As missing cases are often lost to follow-up, less is known about their characteristics, their likely clinical outcomes, or the likely effect of the treatment being trialed. Objective: The aim of this study is to explore the characteristics of missing cases, their likely treatment outcomes, and the ability of different statistical models to approximate missing posttreatment data. Methods: A sample of internet-delivered cognitive behavioral therapy participants in routine care (n=6701, with 36.26% missing cases at posttreatment) was used to identify predictors of dropping out of treatment and predictors that moderated clinical outcomes, such as symptoms of psychological distress, anxiety, and depression. These variables were then incorporated into a range of statistical models that approximated replacement outcomes for missing cases, and the results were compared using sensitivity and cross-validation analyses. Results: Treatment adherence, as measured by the rate of progress of an individual through the treatment modules, and higher pretreatment symptom scores were identified as the dominant predictors of missing cases probability (Nagelkerke R²=60.8%) and the rate of symptom change. Low treatment adherence, in particular, was associated with increased odds of presenting as missing cases during posttreatment assessment (eg, odds ratio 161.1:1) and, at the same time, attenuated the rate of symptom change across anxiety (up to 28% of the total symptom with 48% reduction effect), depression (up to 41% of the total with 48% symptom reduction effect), and psychological distress symptom outcomes (up to 52% of the total with 37% symptom reduction effect) at the end of the 8-week window. Reflecting this pattern of results, statistical replacement methods that overlooked the features of treatment adherence and baseline severity underestimated missing case symptom outcomes by as much as 39% at posttreatment. Conclusions: The treatment outcomes of the cases that were missing at posttreatment were distinct from those of the remaining observed sample. Thus, overlooking the features of missing cases is likely to result in an inaccurate estimate of the effect of treatment.

中文翻译：

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基于Web的心理治疗中失踪病例的预测，结果和统计解决方案：方法学的复制和阐述研究

背景：遗失案件对我们评估基于网络的心理治疗试验效果的能力构成了挑战。由于遗失的病例常常丢失以进行随访，因此对其特征，可能的临床结果或正在尝试的治疗的可能效果知之甚少。目的：本研究的目的是探讨失踪病例的特征，可能的治疗结果以及不同统计模型近似估计失踪后数据的能力。方法：使用互联网提供的接受常规行为护理的认知行为治疗参与者的样本（n = 6701，治疗后失踪病例为36.26％），用于识别退出治疗的预测因素和缓解临床结果的预测因素，例如心理困扰，焦虑和沮丧。然后将这些变量合并到一系列统计模型中，这些模型近似于丢失病例的替代结果，并使用敏感性和交叉验证分析对结果进行比较。结果：根据患者通过治疗模块的进展速度来衡量的治疗依从性以及较高的治疗前症状评分被确定为漏诊病例的主要预测指标（Nagelkerke R ²= 60.8％）和症状变化率。尤其是低的治疗依从性与治疗后评估中出现遗失病例的几率增加有关（例如，比值比为161.1：1），同时减弱了整个焦虑症的症状变化率（高达28％）最终症状总缓解率达到48％），抑郁症（症状缓解率最高达到总症状的41％）和心理困扰症状结局（症状缓解率最高达到总症状的37％）达到52％ 8周窗口的时间。为了反映这种结果模式，忽略治疗依从性和基线严重性特征的统计学替代方法在治疗后低估了失误病例征兆达39％。结论：在治疗后遗失的病例的治疗结果与其余观察到的样品截然不同。因此，忽视失踪病例的特征可能会导致治疗效果的估计不准确。