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- W4220700764 abstract "The call for patient-focused drug development is loud and clear, as expressed in the twenty-first Century Cures Act and in recent guidelines and initiatives of regulatory agencies. Among the factors contributing to modernized drug development and improved health-care activities are easily interpretable measures of clinical benefit. In addition, special care is needed for cancer trials with time-to-event endpoints if the treatment effect is not constant over time.To quantify the potential clinical survival benefit for a new patient, would he/she be treated with the test or control treatment.We propose the predictive individual effect which is a patient-centric and tangible measure of clinical benefit under a wide variety of scenarios. It can be obtained by standard predictive calculations under a rank preservation assumption that has been used previously in trials with treatment switching.We discuss four recent Oncology trials that cover situations with proportional as well as non-proportional hazards (delayed treatment effect or crossing of survival curves). It is shown that the predictive individual effect offers valuable insights beyond p-values, estimates of hazard ratios or differences in median survival.Compared to standard statistical measures, the predictive individual effect is a direct, easily interpretable measure of clinical benefit. It facilitates communication among clinicians, patients, and other parties and should therefore be considered in addition to standard statistical results." @default.
- W4220700764 created "2022-04-03" @default.
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- W4220700764 date "2022-03-16" @default.
- W4220700764 modified "2023-10-16" @default.
- W4220700764 title "The Predictive Individual Effect for Survival Data" @default.
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- W4220700764 doi "https://doi.org/10.1007/s43441-022-00386-0" @default.
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