PubPredict: Prediction of progression and survival in oncology leveraging publications and early efficacy data

• Published in: Pharmaceutical statistics : the journal of the pharmaceutical industry Vol. 22; no. 5; pp. 963 - 973
• Main Authors: Zhang, Jianqi, Guo, Yufei, Zhou, Junyi, Rasmussen, Hans Erik
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Inc 01.09.2023; Wiley Subscription Services, Inc
• Subjects: clinical trial design; continuous‐time markov chain; Disease-Free Survival; Drug development; Humans; Neoplasms - drug therapy; oncology; Response rates; simulation; time to event endpoints; treatment crossover; treatment crossover; clinical trial design; time to event endpoints; continuous-time markov chain; simulation; oncology
• ISSN: 1539-1604; 1539-1612; 1539-1612
• DOI: 10.1002/pst.2321

In oncology/hematology early phase clinical trials, efficacies were often observed in terms of response rate, depth, timing, and duration. However, the true clinical benefits that eventually support registrational purpose are progression‐free survival (PFS) and/or overall survival (OS), the follow‐up of which are typically not long enough in early phase trials. This gap imposes challenges in strategies for late phase drug development. In this article, we tackle the question by leveraging published study to establish a quantitative link between early efficacy outcomes and late phase efficacy endpoints. We used solid tumor cancer as disease model. We modeled the disease course of a RECISTv1.1 assessed solid tumor with a continuous Markov chain (CMC) model. We parameterize the transition intensity matrix of a CMC model based on published aggregate‐level summary statistics, and then simulate subject‐level time‐to‐event data. The simulated data is shown to have good approximation to published studies. PFS and/or OS could be predicted with the transition intensity matrix modified given clinical knowledge to reflect various assumptions on response rate, depth, timing, and duration. The authors have built a R shiny application named PubPredict, the tool implements the algorithm described above and allows customized features including multiple response levels, treatment crossover and varying follow‐up duration. This toolset has been applied to advise phase 3 trial design when only early efficacy data are available from phase 1 or 2 studies.