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A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
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Inference for meta-analysis with a suspected temporal trend.

Rose Baker1, Dan Jackson

  • 1Centre for Operational Research and Applied Statistics, University of Salford, UK. r.d.baker@salford.ac.uk

Biometrical Journal. Biometrische Zeitschrift
|August 27, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces an empirical Bayes meta-analysis to address suspected secular trends in treatment effects. The method adjusts for trends while preventing unstable estimates when evidence is weak.

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Medical Research Methodology

Background:

  • Secular trends in treatment effects can complicate meta-analyses.
  • Standard meta-regression may yield unreliable estimates with weak statistical significance for trends, especially with few studies.

Purpose of the Study:

  • To develop a robust meta-analysis methodology for situations with suspected but not statistically significant secular trends in treatment effects.
  • To provide a method that adjusts for trends without producing unstable estimates from limited data.

Main Methods:

  • Introduction of an empirical Bayes meta-analysis approach that shrinks secular trend estimates toward zero.
  • Exploration of frequentist methods and a fully Bayesian approach.
  • Development of a trend measure analogous to I-squared and exact significance tests.
  • Preferred method based on penalized or h-likelihood for computational simplicity and invariance.

Main Results:

  • The empirical Bayes method provides adjusted treatment effects while mitigating the risk of extreme estimates with weak trend evidence.
  • The h-likelihood approach offers computational advantages and invariance to the choice of time origin.
  • A new measure for trend and exact significance tests are presented.

Conclusions:

  • The proposed empirical Bayes meta-analysis, particularly the h-likelihood method, offers a reliable approach for handling suspected secular trends in meta-analysis.
  • It is recommended to supplement standard random-effects meta-analyses with h-likelihood analysis as a sensitivity analysis for trend assessment.