nonmem covariate analysis with placebo subjects

Hu C, Zhou H, Sharma A (2017) Landmark and longitudinal exposure-response analyses in drug development. Overgaard RV, Ingwersen SH, Tornoe CW (2015) Establishing good practices for exposure-response analysis of clinical endpoints in drug development. 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. Separate and joint latent variable Type I IDR models of PGA and PASI scores were developed and compared.

The Psoriasis area and severity Index (PASI) data, ranging from 0 to 72 with 0.1 increments, were also available.

The primary endpoint was based on the 6-point physician’s global assessment (PGA) score. 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. 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.

Benefits of fully incorporating relevant information in longitudinal exposure–response modeling through joint modeling have recently been shown. While primary clinical trial endpoints often have few categories and thus provide only limited information, sometimes there may be additional, more informative endpoints. Exposure–response modeling is important to optimize dose and dosing regimens in clinical drug development.

Nonmem covariate analysis with placebo subjects