Purpose

In this paper we investigated a new method for dose-response analysis of longitudinal data in terms of precision and accuracy using simulations.

Methods

The new method, called Dose-Response Mixed Models for Repeated Measures (DR-MMRM), combines conventional Mixed Models for Repeated Measures (MMRM) and dose-response modeling. Conventional MMRM can be applied for highly variable repeated measure data and is a way to estimate the drug effect at each visit and dose, however without any assumptions regarding the dose-response shape. Dose-response modeling, on the other hand, utilizes information across dose arms and describes the drug effect as a function of dose. Drug development in chronic kidney disease (CKD) is complicated by many factors, primarily by the slow progression of the disease and lack of predictive biomarkers. Recently, new approaches and biomarkers are being explored to improve efficiency in CKD drug development. Proteinuria, i.e. urinary albumin-to-creatinine ratio (UACR) is increasingly used in dose finding trials in patients with CKD. We use proteinuria to illustrate the benefits of DR-MMRM.

Results

The DR-MMRM had higher precision than conventional MMRM and less bias than a dose-response model on UACR change from baseline to end-of-study (DR-EOS).

Conclusions

DR-MMRM is a promising method for dose-response analysis.

中文翻译：

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重复测量的剂量反应混合模型-一种评估剂量反应的新方法。

目的

在本文中，我们研究了一种新的用于纵向数据剂量响应分析的方法，该方法使用了模拟方法来进行精确度和准确度方面的研究。

方法

该新方法称为重复测量的剂量响应混合模型（DR-MMRM），将常规的重复测量混合模型（MMRM）与剂量响应建模相结合。常规MMRM可以用于高度可变的重复测量数据，并且是一种估算每次就诊和每次给药的药物效果的方法，但是无需对剂量反应形状做出任何假设。另一方面，剂量反应模型利用剂量组之间的信息，并将药物作用描述为剂量的函数。慢性肾脏病（CKD）的药物开发受到许多因素的影响，主要是疾病进展缓慢和缺乏可预测的生物标志物。最近，正在探索新的方法和生物标记物以提高CKD药物开发的效率。蛋白尿，即 尿白蛋白/肌酐比值（UACR）在CKD患者的剂量寻找试验中越来越多地使用。我们使用蛋白尿来说明DR-MMRM的益处。

结果

DR-MMRM具有比传统MMRM更高的精度，并且比从基线到研究结束（DR-EOS）的UACR变化的剂量反应模型的偏差更小。

结论

DR-MMRM是一种有希望的剂量反应分析方法。