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Likelihood methods for measuring statistical evidence.

Jeffrey D Blume1

  • 1Center for Statistical Sciences, Brown University, Providence, RI 02912, USA. jblume@stat.brown.edu

Statistics in Medicine
|September 3, 2002
PubMed
Summary
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The evidential paradigm uses likelihood ratios to measure statistical evidence, allowing for data re-examination without altering evidence strength. This approach offers controllable probabilities of misleading evidence, ensuring reliable study designs.

Area of Science:

  • Statistics
  • Biostatistics
  • Clinical Trials

Background:

  • Traditional statistical methods like p-values can be affected by repeated data examination.
  • The evidential paradigm offers an alternative framework for interpreting statistical evidence.

Purpose of the Study:

  • To introduce and illustrate the evidential paradigm for measuring statistical evidence using likelihood ratios.
  • To demonstrate the advantages of the evidential paradigm, particularly its robustness to data re-examination.

Main Methods:

  • Utilizing likelihood ratios to quantify the strength of statistical evidence.
  • Analyzing the probability of observing misleading evidence under various study designs.
  • Applying the evidential paradigm to a real-world clinical trial, the University Group Diabetes Program (UGDP).

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Main Results:

  • Likelihood ratios provide a measure of statistical evidence unaffected by the number of data examinations.
  • The evidential paradigm allows for controllable probabilities of misleading and weak evidence.
  • The UGDP trial serves as a practical example for illustrating the application and benefits of the evidential paradigm.

Conclusions:

  • The evidential paradigm offers a reliable and robust framework for statistical evidence interpretation.
  • Likelihood ratios are a superior metric for assessing evidence strength compared to p-values, especially in studies involving data re-examination.
  • The evidential paradigm enhances the assurance of study design reliability without compromising statistical evidence integrity.