A multistate model for early decision‐making in oncology

Abstract

The development of oncology drugs progresses through multiple phases, where after each phase, a decision is made about whether to move a molecule forward. Early phase efficacy decisions are often made on the basis of single-arm studies based on a set of rules to define whether the tumor improves ("responds"), remains stable, or progresses (response evaluation criteria in solid tumors [RECIST]). These decision rules are implicitly assuming some form of surrogacy between tumor response and long-term endpoints like progression-free survival (PFS) or overall survival (OS). With the emergence of new therapies, for which the link between RECIST tumor response and long-term endpoints is either not accessible yet, or the link is weaker than with classical chemotherapies, tumor response-based rules may not be optimal. In this paper, we explore the use of a multistate model for decision-making based on singlearm early phase trials. The multistate model allows to account for more information than the simple RECIST response status, namely, the time to get to response, the duration of response, the PFS time, and time to death. We propose to base the decision on efficacy on the OS hazard ratio (HR) comparing historical control to data from the experimental treatment, with the latter predicted from a multistate model based on early phase data with limited survival follow-up. Using two case studies, we illustrate feasibility of the estimation of such an OS HR. We argue that, in the presence of limited follow-up and small sample size, and making realistic assumptions within the multistate model, the OS prediction is acceptable and may lead to better early decisions within the development of a drug. K E Y W O R D Sclinical trial, decision-making, multistate model

Multistate model for pharmacometric analyses of overall survival in HER2‐negative breast cancer patients treated with docetaxel

“… 39 Moreover, when patients are allowed to switch to secondary therapies, regardless of whether secondary therapies consisting of drugs are from the same class 40 or a chemotherapy cocktail, the traditional survival analysis becomes weaker to identify any treatment benefit of the new treatment. Beyer et al 12 demonstrated the application of multistate models in oncology trials. Furthermore, the multistate model can be used to predict the probability of intermediate events (states) for a future clinical trial population.…”

“…Multistate models have been recommended and has been increasingly used for such data. 9 , 10 , 11 For the analysis of survival data, Beyer et al 12 developed a multistate model, where the transition hazards of intermediate events were modeled using semiparametric models with a treatment arm as a binary covariate. The implementation of multistate models in a nonlinear mixed effect (NLME) modeling framework would allow NLME‐derived covariate evaluation.…”

Optimal decision‐making in oncology development programs based on probability of success for phase III utilizing phase II/III data on response and overall survival

“…This reflects rather the practical restrictions in observing the data than the true mechanism of action. Alternatively, a multistate model 24 could be considered: initial state would be “SD and alive”, the final state would be “death”. There are also two intermediate states “CR/PR” and “PD,” which lead to different hazards of death.…”

“…These hazards for “CR/PR” and “PD” could be chosen in a way that expected response rates at t 0 are reflected. Such an approach was recently implemented 24 …”

A Longitudinal Study of the Bladder Cancer Applying a State-Space Model with Non-Exponential Staying Time in States

“…Different extensions of this model have been introduced in the literature [8][9][10][11]. Dynamic models based in the Cox one are Reference [12][13][14][15], among others.…”