Toward Evidence-Based Medical Statistics. 1: The P Value Fallacy

@article{Goodman1999TowardEM,
  title={Toward Evidence-Based Medical Statistics. 1: The P Value Fallacy},
  author={Steven N. Goodman},
  journal={Annals of Internal Medicine},
  year={1999},
  volume={130},
  pages={995-1004}
}
  • S. Goodman
  • Published 15 June 1999
  • Medicine
  • Annals of Internal Medicine
The past decade has seen the rise of evidence-based medicine, a movement that has focused attention on the importance of using clinical studies for empirical demonstration of the efficacy of medical interventions. Increasingly, physicians are being called on to assess such studies to help them make clinical decisions and understand the rationale behind recommended practices. This type of assessment requires an understanding of research methods that until recently was not expected of physicians… 
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References

SHOWING 1-10 OF 151 REFERENCES
Toward Evidence-Based Medical Statistics. 2: The Bayes Factor
TLDR
The Bayes factor is explored, as nonmathematically as possible, the Bayesian approach to measuring evidence and combining information and epistemologic uncertainties that affect all statistical approaches to inference.
The philosophical limits of evidence‐based medicine
  • M. Tonelli
  • Medicine
    Academic medicine : journal of the Association of American Medical Colleges
  • 1998
TLDR
Despite its promise, EBM currently fails to provide an adequate account of optimal medical practice and a broader understanding of medical knowledge and reasoning is necessary.
Clinical trials and statistical verdicts: probable grounds for appeal.
TLDR
Classic analysis is most misleading when the hypothesis in question is already unlikely to be true, when the baseline event rate is low, or when the observed differences are small.
Reporting Bayesian analyses of clinical trials.
TLDR
This work focuses on two types of prior that deserve consideration, the first of which is the non-informative prior giving standardized likelihood distributions as post-trial probability distributions and the second which has a spike of probability mass at the point of no treatment effect.
Quantification and the Quest for Medical Certainty
Since its inception in World War II, the clinical trial has evolved into a standard procedure in determining therapeutic efficacy in many Western industrial democracies. Its features include a
Problems in the "evidence" of "evidence-based medicine".
The end of the p value?
TLDR
The nuts and bolts of calculating the confidence intervals of various types of data are described in a series of articles in the British Medical Journal, and below the authors review some aspects of the approach that are particularly relevant to papers published in theBritish Heart Journal.
Bayesian statistical methods
In this week's BMJ, Lilford and Braunholtz (p 603) explain the basis of Bayesian statistical theory.1 They explore its use in evaluating evidence from medical research and incorporating such evidence
Comments on Bayesian and frequentist analysis and interpretation of clinical trials.
  • L. Fisher
  • Mathematics
    Controlled clinical trials
  • 1996
...
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