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Viewpoint: model selection uncertainty, pre-specification, and model averaging.

Björn Bornkamp1

  • 1Novartis Pharma AG, 4002, Basel, Switzerland.

Pharmaceutical Statistics
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Summary
This summary is machine-generated.

Model selection in science challenges standard statistical guarantees. This review explores pre-specification and model averaging to address model selection uncertainty and improve reliable scientific inference.

Keywords:
dose-response, modelling

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Area of Science:

  • Statistics
  • Empirical Sciences
  • Scientific Methodology

Background:

  • Scientific progress depends on model selection and inference.
  • Model selection can invalidate standard statistical properties, like confidence interval coverage.
  • This poses a challenge for reliable scientific conclusions.

Purpose of the Study:

  • To review the dilemma of statistical guarantees after model selection.
  • To examine the role of pre-specification in mitigating model selection issues.
  • To illustrate model averaging as a technique for addressing model selection uncertainty.

Main Methods:

  • Literature review and conceptual analysis.
  • Examination of statistical properties under model selection.
  • Illustration of model averaging techniques.

Main Results:

  • Standard statistical properties are compromised by post-selection inference.
  • Pre-specification can help maintain statistical validity.
  • Model averaging offers a method to reduce uncertainty arising from model selection.

Conclusions:

  • Addressing model selection uncertainty is crucial for robust empirical science.
  • Pre-specification and model averaging are key strategies.
  • These methods enhance the reliability of scientific inferences.