PDF(Pubmed)
Abstract：
Analyses of longitudinal data with non-linear mixed-effects models (NLMEM) are typically associated with high power, but sometimes at the cost of inflated type I error. Approaches to overcome this problem were published recently, such as model-averaging across drug models (MAD), individual model-averaging (IMA), and combined Likelihood Ratio Test (cLRT). This work aimed to assess seven NLMEM approaches in the same framework: treatment effect assessment in balanced two-armed designs using real natural history data with or without the addition of simulated treatment effect. The approaches are MAD, IMA, cLRT, standard model selection (STDs), structural similarity selection (SSs), randomized cLRT (rcLRT), and model-averaging across placebo and drug models (MAPD). The assessment included type I error, using Alzheimer\'s Disease Assessment Scale-cognitive (ADAS-cog) scores from 817 untreated patients and power and accuracy in the treatment effect estimates after the addition of simulated treatment effects. The model selection and averaging among a set of pre-selected candidate models were driven by the Akaike information criteria (AIC). The type I error rate was controlled only for IMA and rcLRT; the inflation observed otherwise was explained by the placebo model misspecification and selection bias. Both IMA and rcLRT had reasonable power and accuracy except under a low typical treatment effect.
摘要：
使用非线性混合效应模型（NLMEM）对纵向数据进行分析通常与高功率相关，但有时以膨胀的I型错误为代价。最近公布了克服这个问题的方法,例如跨药物模型的模型平均(MAD)，个体模型平均(IMA)，和组合似然比测试(cLRT)。这项工作旨在在同一框架中评估七种NLMEM方法：使用真实的自然历史数据在平衡的双臂设计中评估治疗效果，并添加或不添加模拟治疗效果。方法很糟糕，IMA,cLRT,标准型号选择(STD)，结构相似性选择(SS)，随机cLRT（rcLRT），以及安慰剂和药物模型（MAPD）之间的模型平均。评估包括I类错误，使用817名未经治疗的患者的阿尔茨海默病评估量表-认知（ADAS-cog）评分，以及添加模拟治疗效果后的治疗效果评估的功效和准确性。一组预选候选模型中的模型选择和平均由Akaike信息标准(AIC)驱动。仅IMA和rcLRT控制了I型错误率；否则观察到的通货膨胀可以通过安慰剂模型的错误规范和选择偏差来解释。IMA和rcLRT均具有合理的功率和准确性，但典型治疗效果较低。
