Surrogate Evaluation and Data Transparency

Initial attempts to validate a surrogate endpoint using data from a single trial have been largely unsuccessful, and today, much of the attention has shifted to the meta-analytic setting in which data are available from several trials. Getting individual patient data from multiple trials was, up until today, a tall order. The principal investigators of the trials had to be convinced to share their data, the protection of patient anonymity was often an insurmountable barrier to data access, and hence there were only rare opportunities to perform the analyses described in this book (Buyse, 2009). The last few years have seen a welcome and timely shift toward making patient-level data from clinical trials available for further analysis. The pharmaceutical industry led this initiative (Nisen and Rockhold, 2013), and implemented a system that is attracting a fast-growing number of research proposals (Strom et al., 2014). The European Medicines Agency (EMA) will soon require that patient-level data be made available for all drugs approved in Europe (Bonini et al., 2014). The International Committee of Medical Journal Editors now mandates that a data sharing plan be available for the trials published in their journals (Taich- man et al., 2016). These developments truly revolutionize clinical research, by making it possible to conduct meta-analyses that were previously arduous, hugely time-consuming, and often downright impossible.

As far as surrogacy analyses are concerned, a key limitation of the analyses performed so far is that they were performed as a “one-off” exercise in the context of a specific treatment comparison. But, as mentioned above, a leap of faith is required to apply current knowledge to future treatments that have substantially different modes of action, and in clinical environments that evolve rapidly. The near real-time availability of individual patient data from randomized clinical trials will facilitate updates of previously performed meta-analyses, and an on-going evaluation of surrogate endpoints.

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