Flexible evaluation of surrogacy in platform studies.

Trial-level surrogates are useful tools for improving the speed and cost effectiveness of trials but surrogates that have not been properly evaluated can cause misleading results. The evaluation procedure is often contextual and depends on the type of trial setting. There have been many proposed methods for trial-level surrogate evaluation, but none, to our knowledge, for the specific setting of platform studies. As platform studies are becoming more popular, methods for surrogate evaluation using them are needed. These studies also offer a rich data resource for surrogate evaluation that would not normally be possible. However, they also offer a set of statistical issues including heterogeneity of the study population, treatments, implementation, and even potentially the quality of the surrogate. We propose the use of a hierarchical Bayesian semiparametric model for the evaluation of potential surrogates using nonparametric priors for the distribution of true effects based on Dirichlet process mixtures. The motivation for this approach is to flexibly model relationships between the treatment effect on the surrogate and the treatment effect on the outcome and also to identify potential clusters with differential surrogate value in a data-driven manner so that treatment effects on the surrogate can be used to reliably predict treatment effects on the clinical outcome. In simulations, we find that our proposed method is superior to a simple, but fairly standard, hierarchical Bayesian method. We demonstrate how our method can be used in a simulated illustrative example (based on the ProBio trial), in which we are able to identify clusters where the surrogate is, and is not useful. We plan to apply our method to the ProBio trial, once it is completed.

Statistical controversies in clinical research: assessing pathologic complete response as a trial-level surrogate end point for early-stage breast cancer.

BACKGROUND: A trial-level surrogate end point for a randomized clinical trial may allow assessment of the relative benefits of the treatment to be performed at an earlier time point and potentially with a smaller sample size. However, determining whether an end point is a reliable trial-level surrogate based on results of previous trials is not straightforward. The question of trial-level surrogacy is easily confused with the question of individual-level surrogacy, and this confusion can lead to controversy. A recent example concerns the evaluation of pathologic complete response (pCR) as a surrogate for event-free survival (EFS) and overall survival (OS) in early-stage breast cancer. MATERIALS AND METHODS: The differences between individual-level surrogacy (i.e. for patients receiving a specific treatment, the surrogate end point predicts the definitive end point) and trial-level surrogacy (the results of the trial for the surrogate end point predict the results of the trial for the definitive end point) are discussed. Trial-level data used in two previous meta-analyses evaluating pCR as a trial-level surrogate for EFS and OS were re-analyzed using methods that appropriately account for the variability in pCR rates as well as the variability in the hazard ratios for EFS and OS. RESULTS: There is no evidence that pCR is a trial-level surrogate for EFS or OS, nor is there evidence that pCR could be used reliably to screen out nonpromising agents from further drug development. CONCLUSIONS: At present, neoadjuvant RCTs should continue to follow patients to observe EFS and OS to assess clinical benefit, and they should be designed with sufficient sample size to reliably assess EFS. However, one cannot rule out the possibility that future meta-analyses involving more trials and in which the patient population or class of treatments is restricted could suggest the validity of pCR as a trial-level surrogate for EFS or OS in some focused settings.