To date, only few studies have attempted to investigate non-ignorable dropout during Internet-based interventions by applying an NMAR model, which includes missing data indicators in its equations. Here, the Muthen-Roy model was used to investigate change and dropout patterns in a sample of patients with mild-to-moderate depression symptoms (N = 483) who were randomized to a 12-week Internet-based intervention (deprexis, identifier: NCT01636752). Participants completed the PHQ-9 biweekly during the treatment. We identified four change-dropout patterns: Participants showing high impairment, improvement and low dropout probability (C3, N = 134) had the highest rate of reliable change at 6- and 12-month follow-up. A further pattern was characterized by high impairment, deterioration and high dropout probability (C2, N = 32), another by low impairment, improvement and high dropout probability (C1, N = 198). The last pattern was characterized by high impairment, no change and low dropout probability (C4, N = 119). In addition to deterioration, also rapid improvement may lead to dropout as a result of a perceived "good enough" dosage of treatment. This knowledge may strengthen sensitivity for the mechanisms of dropout and help to consider its meaning in efforts to optimize treatment selection.

中文翻译：

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通过应用Muthen-Roy模型，在基于Internet的抑郁症干预措施中识别出变化脱落模式。

迄今为止，只有极少的研究尝试通过应用NMAR模型来研究基于Internet的干预措施中的不可忽视的辍学现象，该模型在其方程式中包括缺失的数据指标。在这里，Muthen-Roy模型用于调查轻度至中度抑郁症状（N = 483）的患者样本中的变化和辍学模式，这些患者被随机分配到基于互联网的12周干预措施（deprexis，标识符： NCT01636752）。在治疗期间，参与者每两周完成一次PHQ-9。我们确定了四种辍学模式：在6个月和12个月的随访中，表现出较高的障碍，改善和辍学概率低（C3，N = 134）的参与者具有最高的可靠改变率。另一模式的特征是高损害，恶化和高辍学概率（C2，N = 32），另一种特征是低损害，改善和较高的辍学率（C1，N = 198）。最后一种模式的特点是损伤高，无变化且辍学概率低（C4，N = 119）。除了恶化之外，由于感觉到的“足够好”的治疗剂量，快速改善也可能导致辍学。这些知识可能会增强对辍学机制的敏感性，并有助于在优化治疗选择的过程中考虑其含义。