Speaker: Kaspar Rufibach, Department of Biostatistics, F. Hoffmann-La Roche, Basel
Title:“Before going BIG – seize opportunities for small data: use of multistate models to improve decision-making and effect quantification in clinical trials”
Critical decisions in clinical trials are typically based on a tiny fraction of the collected data only. As an example, in early development oncology clinical trials, the decision whether to move a molecule to Phase 3 is typically based on response proportions and duration of response in those that respond, while in Phase 3 the primary endpoint will be progression-free (PFS) or overall survival (OS). Effects on response-based short-term endpoints seldom translate in effects on these relevant endpoints. We propose to make decisions not based on intermediate endpoints, but on a prediction of the OS hazard ratio (HR) between data of the new molecule collected in the early phase trial and historical data of the control treatment. This HR prediction is using a multistate model based on the various disease states a patient may go through until death. This yields a gating strategy with improved operating characteristics compared to traditional decision rules in the context of early phase clinical trials.
Immunotherapeutic agents in oncology are often assumed to exhibit hazard functions that are not proportional to those of control treatments. It is then a matter of debate how to best plan a trial for such a drug and quantify the effect. Instead of trying to model the survival functions of PFS and OS directly and trying to derive a meaningful effect quantifier based on these, the effects on each transition in a multistate model can be used to power a trial and quantify the effect. We illustrate that the survival functions resulting from such an approach mimick the pattern often observed in immunotherapy.
The conclusions of this talk are that (1) multistate models are a useful and underutilized tool in the analysis of clinical trial data and (2) there are still many opportunities to use the (small) data that we collect today more efficiently. Biostatisticians are ideally placed to seize these opportunities.
This is partly based on joint work with Ulrich Beyer, Matthias Meller, David Dejardin, and Uli Burger.