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Updating meta-analyses leads to larger type I errors than publication bias
George F Borm1, A Rogier T Donders
1Department of Epidemiology, Biostatistics and HTA (EBH 133), Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands. g.borm@ebh.umcn.nl
Journal of Clinical Epidemiology
|January 13, 2009
Summary
Periodically updating meta-analyses inflates Type I error rates 2- to 5-fold, more than publication bias. Researchers should account for this error inflation when interpreting meta-analysis results.
Area of Science:
- Biostatistics
- Medical Research Methodology
Background:
- Periodic updating of meta-analyses is common practice.
- This practice may lead to an inflated Type I error rate, potentially affecting research conclusions.
- Publication bias is another known factor that can distort meta-analysis findings.
Purpose of the Study:
- To quantify the extent of Type I error inflation due to meta-analysis updates.
- To compare this inflation with that caused by publication bias.
- To propose a method for adjusting for inflation caused by meta-analysis updates.
Main Methods:
- The study employed simulation methods to estimate error rates.
- Error rates were assessed under conditions of periodic meta-analysis updating.
Main Results:
- Updating meta-analyses generally resulted in a 2- to 5-fold inflation of Type I error rates.
- This inflation was found to be greater than that caused by publication bias.
- A rule of thumb was established for meta-analysis robustness based on P-values and the number of updates.
Conclusions:
- Meta-analyses are frequently updated until a definitive conclusion is achieved.
- It is crucial to consider the inflated error rate when interpreting meta-analysis results.
- Adjusting for error inflation is important for accurate scientific interpretation.
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