Progress and Application of Bayesian Approach in the Early Research and Development of New Anticancer Drugs.
10.3779/j.issn.1009-3419.2022.102.43
- Author:
Huiyao HUANG
1
;
Meiruo LIU
2
;
Xiyan LI
2
;
Xinyu MENG
3
;
Dandan CUI
4
;
Ye LENG
5
;
Yu TANG
1
;
Ning LI
1
Author Information
1. Clinical Trials Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
2. Department of Biostatistics and Data Sciences, Boehringer Ingelheim (China) Investment Co Ltd, Shanghai 201203, China.
3. School of Population and Global Health, the University of Melbourne, Victoria 3010, Australia.
4. Clinical Trials Center, National Cancer Center/National Clinical Research Center for Cancer/Hebei Cancer Hospital, Chinese Academy of Medical Sciences, Langfang 065001, China.
5. School of Basic Medicine and Clinical
Pharmacy, China Pharmaceutical University, Nanjing 211198, China.
- Publication Type:Journal Article
- Keywords:
Bayesian;
Exploratory trial;
Neoplasm;
Statistical design
- MeSH:
Humans;
Bayes Theorem;
Lung Neoplasms/drug therapy*;
Research Design;
Antineoplastic Agents/therapeutic use*;
Biological Products;
Pharmaceutical Preparations
- From:
Chinese Journal of Lung Cancer
2022;25(10):730-734
- CountryChina
- Language:Chinese
-
Abstract:
Bayesian statistics is an approach for learning from evidences as it accumulates, combining prior distribution with current information on a quantity of interest, in which posterior distribution and inferences are being updated each time new data become available using Bayes' Theorem. Though frequentist approach has dominated medical studies, Bayesian approach has been more and more widely recognized by its flexibility and efficiency. Research and development (R&D) on anti-cancer new drugs have been so hot globally in recent years in spite of relatively high failure rate. It is the common demand of pharmaceutical enterprises and researchers to identify the optimal dose, regime and right population in the early-phase R&D stage more accurately and efficiently, especially when the following three major changes have been observed. The R&D on anticancer drugs have transformed from chemical drugs to biological products, from monotherapy to combination therapy, and the study design has also gradually changed from traditional way to innovative and adaptive mode. This also raises a number of subsequent challenges on decision-making of early R&D, such as inability to determine MTD, flexibility to deal with delayed toxicity, delayed response and dose-response changing relationships. It is because of the above emerging changes and challenges that the Bayesian approach is getting more and more attention from the industry. At least, Bayesian approach has more information for decision-making, which could potentially help enterprises achieve higher efficiency, shorter period and lower investment. This study also expounds the application of Bayesian statistics in the early R&D on anticancer new drugs, and compares and analyzes its idea and application scenarios with frequentist statistics, aiming to provide macroscopic and systematic reference for all related stakeholders.
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