Clinical and Quantitative Pharmacology, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Gothenburg, Sweden.
Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden.
Pharm Res. 2020 Jul 31;37(8):157. doi: 10.1007/s11095-020-02882-0.
In this paper we investigated a new method for dose-response analysis of longitudinal data in terms of precision and accuracy using simulations.
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.
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).
DR-MMRM is a promising method for dose-response analysis.
本文通过模拟，从精度和准确性两方面研究了一种新的纵向数据剂量反应分析方法。
新方法称为重复测量的剂量反应混合模型（DR-MMRM），它将传统的重复测量混合模型（MMRM）和剂量反应建模相结合。传统的 MMRM 可用于高度可变的重复测量数据，是一种估计每次就诊和剂量的药物效果的方法，但不考虑剂量反应形状的任何假设。另一方面，剂量反应建模利用各剂量臂的信息，并将药物效果描述为剂量的函数。慢性肾脏病（CKD）的药物开发受到许多因素的影响，主要是由于疾病进展缓慢且缺乏预测性生物标志物。最近，正在探索新的方法和生物标志物来提高 CKD 药物开发的效率。蛋白尿，即尿白蛋白与肌酐比值（UACR），在 CKD 患者的剂量发现试验中越来越多地被使用。我们使用蛋白尿来说明 DR-MMRM 的优势。
DR-MMRM 在 UACR 从基线到研究结束（DR-EOS）的变化方面，比传统的 MMRM 具有更高的精度，比剂量反应模型的偏差更小。
DR-MMRM 是一种很有前途的剂量反应分析方法。
