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Principles for the ethical analysis of clinical and translational research
Author(s) -
Gelfond Jonathan A.L.,
Heitman Elizabeth,
Pollock Brad H.,
Klugman Craig M.
Publication year - 2011
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.4282
Subject(s) - cornerstone , data science , translational research informatics , translational research , computer science , statistical hypothesis testing , management science , process (computing) , engineering ethics , informatics , statistical analysis , translational science , health informatics , medicine , statistics , mathematics , political science , pathology , public health , economics , art , law , engineering , visual arts , operating system , engineering informatics
Statistical analysis is a cornerstone of the scientific method and evidence‐based medicine, and statisticians serve an increasingly important role in clinical and translational research by providing objective evidence concerning the risks and benefits of novel therapeutics. Researchers rely on statistics and informatics as never before to generate and test hypotheses and to discover patterns of disease hidden within overwhelming amounts of data. Too often, clinicians and biomedical scientists are not adequately proficient in statistics to analyze data or interpret results, and statistical expertise may not be properly incorporated within the research process. We argue for the ethical imperative of statistical standards, and we present ten nontechnical principles that form a conceptual framework for the ethical application of statistics in clinical and translational research. These principles are drawn from the literature on the ethics of data analysis and the American Statistical Association Ethical Guidelines for Statistical Practice. Copyright © 2011 John Wiley & Sons, Ltd.