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Design and Analysis of Cancer Clinical Trials
Published in Yingwei Peng, Binbing Yu, Cure Models, 2021
Endpoints are measurable clinical and biological findings that are used for the development and assessment of treatment options (Fiteni et al., 2014). In the treatment of cancer, endpoints can be classified into two categories: patient-centered clinical endpoints including OS and health-related quality of life (QoL), and tumor-centered clinical endpoints such as PFS. Surrogate endpoints are tumor-centered clinical endpoints that can be used as substitutes for patient-centered clinical endpoints, particularly OS. The choice of endpoints in oncology trials is a major problem. The published Consolidated Standards of Reporting Trials (CONSORT) best-practice guidelines encourage reporting clearly defined primary and secondary outcome measures. OS is the gold standard of endpoints, but as increasing numbers of effective salvage treatments become available for many types of cancer, much larger numbers of patients are included; this requires a longer follow-up period and increases the cost of clinical trials.
Selected Statistical Topics of Regulatory Importance
Published in Demissie Alemayehu, Birol Emir, Michael Gaffney, Interface between Regulation and Statistics in Drug Development, 2020
Demissie Alemayehu, Birol Emir, Michael Gaffney
Surrogate endpoints relate to a small class of biomarkers that serve as a substitute for clinical outcomes, which directly measure how patients feel, function, or survive. Surrogate endpoints are particularly preferred when the desired clinical outcomes are not readily obtainable for practical or ethical reasons. Thus, the primary function of a surrogate endpoint is to predict, but not measure, clinical benefit or harm. However, the validity and reliability of a surrogate endpoint must first be established before it can be used in medical research or clinical practice. This requires extensive testing to see how well they predict, or correlate with, clinical benefit. In general, the predictive capacity of a surrogate endpoint is evaluated based on data from a variety of sources, including epidemiologic, therapeutic, pathophysiologic, or other scientific experiments. Depending on the level of clinical validation, surrogate endpoints may be classified as candidate, reasonably likely, or validated. Surrogate endpoints are considered candidate when they are still under evaluation for their predictive ability, whereas reasonably likely surrogate endpoints require support based on strong mechanistic and/or epidemiologic rationale.
Meta-Analytic Approach to Evaluation of Surrogate Endpoints
Published in Christopher H. Schmid, Theo Stijnen, Ian R. White, Handbook of Meta-Analysis, 2020
Tomasz Burzykowski, Marc Buyse, Geert Molenberghs, Ariel Alonso, Wim Van der Elst, Ziv Shkedy
The suggestion of Buyse and Molenberghs (1998) to evaluate surrogates by using data from multiple randomized trials coincided with the proposal of The Biomarker Definitions Working Group (2001; Ellenberg and Hamilton, 1989). The definitions formulated by the latter group have since been widely adopted. A clinical endpoint is considered the most credible indicator of drug response and defined as a characteristic or variable that reflects how a patient feels, functions, or survives. In clinical trials aimed at establishing the worth of new therapies, clinically relevant endpoints should be used, unless a biomarker or other endpoint is available that has risen to the status of surrogate endpoint. A biomarker is defined as a characteristic that can be objectively measured as an indicator of healthy or pathological biological processes, or pharmacological responses to therapeutic intervention. A surrogate endpoint is a biomarker that is intended for substituting a clinical endpoint. A surrogate endpoint is expected to predict clinical benefit, harm, or lack of these. Toward this aim, a prediction model is needed. Such a model can be built using data from multiple randomized trials.
The notion of Surrogacy in Health Technology Assessment: an insight in the processes of Germany, UK and France
Published in Journal of Medical Economics, 2022
Panagiotis Petrou, Olga Pitsillidou, M. J. Postma
In oncology, surrogate endpoints are tumor-centered clinical endpoints that infer clinical benefit to the patient and are employed as a proxy for a patient-centered clinical endpoint. These endpoints refer to biological markers, either laboratory or histology ones, such as tumor response, circulating tumor cells, disease-free survival (DFS) and progression-free survival (PFS), which can define therapeutic response to an intervention. The rationale of surrogate endpoints is nested in the prediction of survival well in advance, thus perpetuating to fewer patients and shorter and cheaper trials. In some cases, as in the cases of crossover to subsequent treatments, the use of surrogate endpoints is justified. A validation must precede, which constitutes an intricate but obligatory process. Many surrogate endpoints, which meet the criteria of being assessable earlier in a patient’s life, have been assessed. Nevertheless, a simple correlation does not suffice3 (Table 1).
Abiraterone acetate plus prednisone/prednisolone in hormone-sensitive and castration-resistant metastatic prostate cancer
Published in Expert Review of Precision Medicine and Drug Development, 2021
Jürgen E Gschwend, Kurt Miller
In this context, there is an ongoing debate about the use of surrogate endpoints to support approval of cancer drugs. In prostate cancer clinical trials, OS can be a tough endpoint as it is often a more slowly progressing disease, thus delaying survival analysis. With several treatment options for advanced prostate cancer available, patients may receive multiple lines of therapies, making it increasingly difficult to assess the effect of new therapeutic options on OS in realistic and justifiable timeframes, especially in earlier stages of the disease. Surrogate endpoints, if established in high-quality validation studies, provide great potential to draw profound conclusions on efficacy and safety early, allowing faster access to treatment options for patients with unmet medical needs. Whereas rPFS has been suggested as a surrogate for OS in the metastatic setting [46], there was previously no earlier endpoint for nmCRPC trials.
Predicting therapeutic response through biomarker analysis in psoriatic arthritis, an example of precision medicine
Published in Expert Review of Precision Medicine and Drug Development, 2020
Biomarkers may be classified into the following categories: prognostic, predictive, pharmacodynamic biomarkers and surrogate end-points [38,40]. With relevance to this paper, a predictive biomarker is defined as a baseline characteristic that categorizes patients by their likelihood for response to a particular treatment and may predict a favorable response or an adverse event. A pharmacodynamic (or activity) biomarker demonstrates a biological response that has occurred in a patient after having received a therapeutic intervention. A surrogate endpoint is intended to substitute for a clinical efficacy endpoint and is expected to predict clinical benefit (or harm or lack of benefit or harm). Surrogate endpoints are often considered to be a subset of pharmacodynamic biomarkers. Biomarkers may have valuable applications in the evaluation of drug toxicity and prediction of clinical response, and thus is an invaluable tool in precision medicine [10,38].