Longitudinal exposure-response modeling plays an important role in optimizing dose and dosing regimens in clinical drug development. Certain clinical trials contain induction and maintenance phases where the maintenance treatment depends on the subjects' achieving the main endpoint outcome in the induction phase. Due to logistic difficulties and cost considerations, the main endpoint is usually collected more sparsely than a subcomponent (or other related endpoints). The sparse collection of the main endpoint hampers its longitudinal modeling. In principle, the frequent collection of a subcomponent allows its longitudinal modeling. However, the model evaluation via the visual predictive check (VPC) in the maintenance phase is difficult due to the requirement of the main-endpoint model to identify the treatment subgroups. This manuscript proposes a solution to this dilemma via the joint modeling of the main endpoint and the subcomponent. The challenges are illustrated by analyzing the data collected up to 60 weeks from a phase III trial of ustekinumab in patients with moderate-to-severe ulcerative colitis (UC). The main endpoint Mayo score, a commonly used composite endpoint to measure the severity of UC, was collected only at baseline, the end of the induction phase, and the end of the maintenance phase. The partial Mayo score, which is a major subset of the Mayo score, was collected at nearly every 4 weeks. A longitudinal joint exposure-response model, developed under a latent-variable Indirect Response modeling framework, described the Mayo score time course and facilitated the VPC model evaluation under a response-adaptive trial design.
Keywords: NONMEM; bounded outcome scores; discrete data; inflammatory bowel disease; population pharmacokinetic/pharmacodynamic modeling.