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Trial Sequential Analysis in systematic reviews with meta-analysis.

Jørn Wetterslev1,2, Janus Christian Jakobsen3,4,5,6, Christian Gluud3,6

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BMC Medical Research Methodology
|March 8, 2017
PubMed
Summary
This summary is machine-generated.

Most meta-analyses lack statistical power, leading to errors. Trial Sequential Analysis (TSA) offers adjusted thresholds for significance, improving accuracy by controlling type I and type II errors when information size is insufficient.

Keywords:
DiversityFixed-effect modelGroup sequential analysisHeterogeneityInformation sizeInterim analysisMeta-analysisRandom-effects modelSample sizeTrial sequential analysis

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Area of Science:

  • Medical Statistics
  • Evidence Synthesis
  • Clinical Trial Methodology

Background:

  • Many meta-analyses, including Cochrane reviews, lack sufficient statistical power to reliably detect or refute intervention effects.
  • Insufficient sample sizes in meta-analyses lead to an increased risk of false positive (Type I) and false negative (Type II) errors.
  • Traditional significance thresholds (95% confidence intervals, 5% p-value) are inadequate when the required information size is not met.

Purpose of the Study:

  • To introduce and explain a methodology for interpreting meta-analysis results with adjusted significance thresholds.
  • To address the limitations of traditional meta-analyses when the required information size is not achieved.
  • To enhance the reliability of systematic reviews by improving statistical power and error control.

Main Methods:

  • Utilized the Lan-DeMets trial sequential monitoring boundaries within Trial Sequential Analysis (TSA).
  • Defined and calculated the diversity-adjusted required information size and the corresponding number of required trials.
  • Employed a frequentistic approach to control both Type I and Type II errors.

Main Results:

  • TSA provides adjusted confidence intervals and restricted significance thresholds when the required information size is not reached.
  • Trial Sequential Analysis demonstrates superior control of Type I and Type II errors compared to traditional meta-analysis.
  • Empirical studies confirm that TSA reduces spurious conclusions in systematic reviews.

Conclusions:

  • Trial Sequential Analysis offers a robust method for analyzing meta-analytic data with transparent assumptions.
  • TSA significantly improves the control of Type I and Type II errors over traditional meta-analysis methods.
  • Adopting TSA enhances the validity and reliability of systematic reviews and meta-analyses.