Clinical Pharmacology and Pharmacometrics, Janssen Research & Development, LLC, 1400 McKean Road, PO Box 776, Spring House, PA, 19477, USA.
J Pharmacokinet Pharmacodyn. 2022 Jun;49(3):283-291. doi: 10.1007/s10928-021-09796-3. Epub 2021 Nov 20.
Exposure-response modeling is important to optimize dose and dosing regimens in clinical drug development. While primary clinical trial endpoints often have few categories and thus provide only limited information, sometimes there may be additional, more informative endpoints. Benefits of fully incorporating relevant information in longitudinal exposure-response modeling through joint modeling have recently been shown. This manuscript aims to further investigate the benefit of joint modeling of an ordered categorical primary endpoint with a related near-continuous endpoint, through the sharing of model parameters in the latent variable indirect response (IDR) modeling framework. This is illustrated by analyzing the data collected through up to 116 weeks from a phase 3b response-adaptive trial of ustekinumab in patients with psoriasis. The primary endpoint was based on the 6-point physician's global assessment (PGA) score. The Psoriasis area and severity Index (PASI) data, ranging from 0 to 72 with 0.1 increments, were also available. Separate and joint latent variable Type I IDR models of PGA and PASI scores were developed and compared. The results showed that the separate PGA model had a substantial structural bias, which was corrected by the joint modeling of PGA and PASI scores.
暴露-反应建模对于优化临床药物开发中的剂量和给药方案非常重要。虽然主要的临床试验终点通常只有少数几个类别，因此提供的信息有限，但有时可能会有其他更具信息量的终点。最近已经证明，通过联合建模充分纳入纵向暴露-反应建模中的相关信息具有优势。本文旨在通过在潜在变量间接反应 (IDR) 建模框架中共享模型参数，进一步研究联合建模有序分类主要终点与相关连续终点的益处。通过分析来自银屑病患者的 3b 期反应适应性试验中收集的长达 116 周的数据来说明这一点。主要终点基于 6 分医生整体评估 (PGA) 评分。也可获得范围从 0 到 72 分，增量为 0.1 的银屑病面积和严重程度指数 (PASI) 数据。分别和联合潜在变量 I 型 IDR 模型的 PGA 和 PASI 评分进行了开发和比较。结果表明，单独的 PGA 模型存在显著的结构偏差，通过 PGA 和 PASI 评分的联合建模得到了纠正。
