Optimal Decision Criteria for the Study Design and Sample Size of a Biomarker-Driven Phase III Trial
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ORIGINAL ARTICLE
Optimal Decision Criteria for the Study Design and Sample Size of a Biomarker‑Driven Phase III Trial Akira Takazawa1,2 · Satoshi Morita2 Received: 4 July 2019 / Accepted: 26 November 2019 © The Drug Information Association, Inc 2020
Abstract Background The design and sample size of a phase III study for new medical technologies were historically determined within the framework of frequentist hypothesis testing. Recently, drug development using predictive biomarkers, which can predict efficacy based on the status of biomarkers, has attracted attention, and various study designs using predictive biomarkers have been suggested. Additionally, when choosing a study design, considering economic factors, such as the risk of development, expected revenue, and cost, is important. Methods Here, we propose a method to determine the optimal phase III design and sample size and judge whether the phase III study will be conducted using the expected net present value (eNPV). The eNPV is defined using the probability of success of the study calculated based on historical data, the revenue that will be obtained after the success of the phase III study, and the cost of the study. Decision procedures of the optimal phase III design and sample size considering historical data obtained up to the start of the phase III study were considered using numerical examples. Results Based on the numerical examples, the optimal study design and sample size depend on the mean treatment effect in the biomarker-positive and biomarker-negative populations obtained from historical data, the between-trial variance of response, the prevalence of the biomarker-positive population, and the threshold value of probability of success required to go to phase III study. Conclusions Thus, the design and sample size of a biomarker-driven phase III study can be appropriately determined based on the eNPV. Keywords Predictive biomarkers · Historical data · Probability of success · Net present value · Go/no-go decision
Introduction In recent years, the efficacy and safety requirements for new pharmaceutical products have increased; in this regard, a decrease in the success rate of drug development programs and the prolongation of the developmental period are regarded as problems. Drug development using biomarkers is considered one of the measures against these problems [1]. Predictive biomarkers are used to predict the difference in treatment efficacy based on the status of the respective * Akira Takazawa [email protected] 1
Data Science Department, ONO Pharmaceutical Co., Ltd., 8‑2, Kyutaromachi 1‑Chome, Chuo‑ku, Osaka 541‑8564, Japan
Department of Biomedical Statistics and Bioinformatics, Kyoto University Graduate School of Medicine, Kyoto, Japan
2
biomarker. These predictive biomarkers can also identify the patient population with the highest efficacy, thus reducing the required sample size of the study and increasing the possibility of detecting efficacy, compared to cases in which patient populations are not identified [2, 3]
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