Biostatistical Considerations When Using RWD and RWE in Clinical Studies for Regulatory Purposes: A Landscape Assessment

被引:29
作者
Levenson, Mark [1 ]
He, Weili [2 ]
Chen, Jie [3 ]
Fang, Yixin [2 ]
Faries, Douglas [4 ]
Goldstein, Benjamin A. [5 ,6 ]
Ho, Martin [7 ]
Lee, Kwan [8 ]
Mishra-Kalyani, Pallavi [1 ]
Rockhold, Frank [5 ,6 ]
Wang, Hongwei [2 ]
Zink, Richard C. [9 ]
机构
[1] US FDA, CDER, Silver Spring, MD 20903 USA
[2] AbbVie, Data & Stat Sci, Global Med Affairs Stat, N Chicago, IL 60064 USA
[3] Overland Pharmaceut, Dover, DE USA
[4] Eli Lilly & Co, Global Stat Sci, Indianapolis, IN 46285 USA
[5] Duke Univ, Dept Biostat & Bioinformat, Durham, NC USA
[6] Duke Univ, Duke Clin Res Inst, Durham, NC USA
[7] US FDA, CBER, Silver Spring, MD USA
[8] Janssen Res & Dev, Stat & Decis Sci, Spring House, PA USA
[9] Lexitas Pharma Serv Inc, Durham, NC USA
关键词
Development; Medical product; Regulatory; SECONDARY DATA SOURCES; STATISTICAL STRATEGIES; SAFETY DATA; DESIGN; DATABASE;
D O I
10.1080/19466315.2021.1883473
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Real-world evidence (RWE), derived from data from "real-world" clinical practice and medical product utilization, is an increasingly important source of evidence that holds great potential to increase efficiency and improve clinical development and life cycle management of medical products. Regulatory agencies, public-private partnerships, and health technology assessment organizations have launched major initiatives and released guidance to address considerations in the use of RWE to inform regulatory decision making. However, many challenges remain on how RWE could be best used for various types of regulatory decisions from statistical perspectives. To address the relevant statistical challenges, a working group under the auspices of the ASA Biopharmaceutical Section was established. This article reviews the biostatistical challenges and methods for the use of RWE for medical product development. There are two companion articles as the output from the same working group that address focused topics. The article by Chen et al. provides the current landscape on the use of RWE to inform clinical study design and analysis, and the article by Ho et al. presents a review of causal inference framework for design and analysis of studies using RWE.
引用
收藏
页码:3 / 13
页数:11
相关论文
共 98 条
[1]  
ABRAHAMOWICZ M, 2016, STAT MED, V35
[2]   Estimands and Their Role in Clinical Trials [J].
Akacha, Mouna ;
Bretz, Frank ;
Ohlssen, David ;
Rosenkranz, Gerd ;
Schmidli, Heinz .
STATISTICS IN BIOPHARMACEUTICAL RESEARCH, 2017, 9 (03) :268-271
[3]  
[Anonymous], 2000, E10 ICH
[4]  
[Anonymous], 2019, E9R1 ICH
[5]   A Tutorial and Case Study in Propensity Score Analysis: An Application to Estimating the Effect of In-Hospital Smoking Cessation Counseling on Mortality [J].
Austin, Peter C. .
MULTIVARIATE BEHAVIORAL RESEARCH, 2011, 46 (01) :119-151
[6]  
Baiocchi M., 2014, STAT MED, V33
[7]   Good Research Practices for Comparative Effectiveness Research: Defining, Reporting and Interpreting Nonrandomized Studies of Treatment Effects Using Secondary Data Sources: The ISPOR Good Research Practices for Retrospective Database Analysis Task Force Report-Part I [J].
Berger, Marc L. ;
Mamdani, Muhammad ;
Atkins, David ;
Johnson, Michael L. .
VALUE IN HEALTH, 2009, 12 (08) :1044-1052
[8]  
BERGER ML, 2014, VALUE HEALTH, V17
[9]  
BERGER ML, 2017, PHARMACOEPIDEM DR S, V26
[10]  
Chen J., 2020, STAT BIOPHARM RES