Development and validation of a prediction model for the probability of responding to placebo in antidepressant trials: a pooled analysis of individual patient data
Development and validation of a prediction model for the probability of responding to placebo in antidepressant trials: a pooled analysis of individual patient data
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抗抑郁试验中安慰剂反应概率的预测模型的开发和验证：个体患者数据的汇总分析
DOI：
10.1136/ebmental-2018-300073
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Furukawa Toshi A
中科院分区：
文献类型：
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作者：
Shinohara Kiyomi;Tanaka Shiro;Imai Hissei;Noma Hisashi;Maruo Kazushi;Cipriani Andrea;Yamawaki Shigeto;Furukawa Toshi A
背景在明显的药物反应者中识别潜在的安慰剂反应者对于剖析抑郁症的药物特异性和非特异性作用至关重要。目的该项目旨在开发和测试抗抑郁试验中安慰剂反应概率的预测模型。这样的模型将使我们能够估计抗抑郁药试验中药物反应者出现安慰剂反应的概率。方法我们确定了在日本进行的第二代抗抑郁药治疗重度抑郁症的所有安慰剂对照、双盲随机对照试验 (RCT)，并向制药公司索取其个体患者数据 (IPD)。我们从比较米氮平、艾司西酞普兰、度洛西汀、帕罗西汀和安慰剂的四项 II/III 期随机对照试验中获得了 IPD (n=1493)。在四项临床试验的 1493 名参与者中，440 名分配给安慰剂的参与者被纳入分析。我们的主要结局是缓解，定义为研究终点时汉密尔顿抑郁量表下降 50% 或更多。我们使用多变量逻辑回归来开发预测模型。通过向后变量选择来测试所有可用的预测变量候选者，并为预测模型选择协变量。使用 Hosmer-Lemeshow 检验进行校准并使用 ROC 曲线下面积进行区分来评估模型的性能。结果四项试验中安慰剂反应率在 31% 到 59% 之间（总平均值：43%）。从所有候选变量中选择了四个变量并包含在最终模型中：发病年龄、基线年龄、身体症状和研究水平差异。最终模型在校准（Hosmer-Lemeshow p=0.92）和区分（ROC 曲线下面积 (AUC)：0.70）方面表现令人满意。结论我们的模型有望帮助研究人员区分更有可能对安慰剂产生反应的个体和不太可能有反应的个体。临床意义应该收集更大的样本和更精确的个体参与者信息，以获得更好的性能。有必要对独立数据集的外部有效性进行检查。试验注册号CRD42017055912。
BackgroundIdentifying potential placebo responders among apparent drug responders is critical to dissect drug-specific and nonspecific effects in depression.ObjectiveThis project aimed to develop and test a prediction model for the probability of responding to placebo in antidepressant trials. Such a model will allow us to estimate the probability of placebo response among drug responders in antidepressants trials.MethodsWe identified all placebo-controlled, double-blind randomised controlled trials (RCTs) of second generation antidepressants for major depressive disorder conducted in Japan and requested their individual patient data (IPD) to pharmaceutical companies. We obtained IPD (n=1493) from four phase II/III RCTs comparing mirtazapine, escitalopram, duloxetine, paroxetine and placebo. Out of 1493 participants in the four clinical trials, 440 participants allocated to placebo were included in the analyses. Our primary outcome was response, defined as 50% or greater reduction on Hamilton Rating Scale for Depression at study endpoint. We used multivariable logistic regression to develop a prediction model. All available candidate of predictor variables were tested through a backward variable selection and covariates were selected for the prediction model. The performance of the model was assessed by using Hosmer-Lemeshow test for calibration and the area under the ROC curve for discrimination.FindingsPlacebo response rates differed between 31% and 59% (grand average: 43%) among four trials. Four variables were selected from all candidate variables and included in the final model: age at onset, age at baseline, bodily symptoms, and study-level difference. The final model performed satisfactorily in terms of calibration (Hosmer-Lemeshow p=0.92) and discrimination (the area under the ROC curve (AUC): 0.70).ConclusionsOur model is expected to help researchers discriminate individuals who are more likely to respond to placebo from those who are less likely so.Clinical implicationsA larger sample and more precise individual participant information should be collected for better performance. Examination of external validity in independent datasets is warranted.Trial registration numberCRD42017055912.