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Statistical Use in Clinical Studies: Is There Evidence of a Methodological Shift?
Author(s) -
Dali Yi,
Dihui Ma,
Gaoming Li,
Liang Zhou,
Qin Xiao,
Yanqi Zhang,
Xiaoyu Liu,
Hongru Chen,
Julia Christine Pettigrew,
Dong Yi,
Ling Liu,
Yazhou Wu
Publication year - 2015
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0140159
Subject(s) - medicine , sample size determination , credibility , clinical trial , logistic regression , statistical significance , confidence interval , publication bias , medline , research design , covariate , clinical study design , statistical analysis , statistical model , statistics , mathematics , political science , law
Background Several studies indicate that the statistical education model and level in medical training fails to meet the demands of clinicians, especially when they want to understand published clinical research. We investigated how study designs and statistical methods in clinical studies have changed in the last twenty years, and we identified the current trends in study designs and statistical methods in clinical studies. Methods We reviewed 838 eligible clinical study articles that were published in 1990, 2000, and 2010 in four journals New England Journal of Medicine, Lancet, Journal of the American Medical Association and Nature Medicine. The study types, study designs, sample designs, data quality controls, statistical methods and statistical software were examined. Results Substantial changes occurred in the past twenty years. The majority of the studies focused on drug trials (61.6%, n = 516). In 1990, 2000, and 2010, there was an incremental increase in RCT studies (74.4%, 82.8%, and 84.0%, respectively, p = 0.013). Over time, there was increased attention on the details of selecting a sample and controlling bias, and there was a higher frequency of utilizing complex statistical methods. In 2010, the most common statistical methods were confidence interval for superiority and non-inferiority comparison (41.6%), survival analysis (28.5%), correction analysis for covariates (18.8%) and Logistic regression (15.3%). Conclusions These findings indicate that statistical measures in clinical studies are continuously developing and that the credibility of clinical study results is increasing. These findings provide information for future changes in statistical training in medical education.

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