Controlling the risk of spurious findings from meta-regression

Statistics in Medicine

Volumn 23, Issue 11, 2004, Pages 1663-1682

  Higgins, Julian P T ^a   Thompson, Simon G ^a  

^a UNIVERSITY OF CAMBRIDGE   (United Kingdom)

Author keywords

False positive results; Meta analysis; Meta regression; Permutation test; Randomization tests; Simulation study; Weighted regression

Indexed keywords

ANALYTICAL ERROR; CORRELATION ANALYSIS; COVARIANCE; REGRESSION ANALYSIS; REVIEW; RISK MANAGEMENT; SIMULATION; STATISTICAL MODEL; STATISTICAL SIGNIFICANCE;

BCG VACCINE; COMPUTER SIMULATION; DEPRESSION; EXERCISE; FALSE POSITIVE REACTIONS; GEOGRAPHY; HUMANS; META-ANALYSIS; MONTE CARLO METHOD; REGRESSION ANALYSIS;

EID: 2942695870     PISSN: 02776715     EISSN: None     Source Type: Journal    
DOI: 10.1002/sim.1752     Document Type: Review

References (33)

1. Colditz GA, Brewer TF, Berkey CS, Wilson ME, Burdick E, Fineberg HV, Mosteller F. Efficacy of BCG vaccine in the prevention of tuberculosis: meta-analysis of the published literature. Journal of the American Medical Association 1994; 271:698-702.
2. Law MR, Wald NJ, Thompson SG. By how much and how quickly does reduction in serum cholesterol concentration lower risk of ischaemic heart disease? British Medical Journal 1994; 308:367-373.
3. Lijmer JG, Mol BW, Heisterkamp S, Bonsel GJ, Prins MH, van der Meulen JHP, Bossuyt PMM. Empirical evidence of design-related bias in studies of diagnostic tests. Journal of American Medical Association 1999; 282:1061-1066.
4. Berlin JA, Antman EM. Advantages and limitations of metaanalytic regressions of clinical trials data. Online Journal of Current Clinical Trials 1994; 4:Doc No. 134.
5. Davey Smith G, Egger M, Phillips AN. Meta-analysis: beyond the grand mean? British Medical Journal 1997; 315:1610-1614.
6. Lau J, Ioannidis JP, Schmid CH. Summing up evidence: one answer is not always enough. The Lancet 1998; 351:123-127.
7. Thompson SG, Higgins JPT. How should meta-regression analyses be undertaken and interpreted? Statistics in Medicine 2002; 21:1559-1574.
8. Peduzzi P, Concato J, Feinstein AR, Holford TR. Importance of events per independent variable in proportional hazards regression analysis. II. Accuracy and precision of regression estimates. Journal of Clinical Epidemiology 1995; 48:1503-1510.
9. Peduzzi P, Concato J, Kemper E, Holford TR, Feinstein AR. A simulation study of the number of events per variable in logistic regression analysis. Journal of Clinical Epidemiology 1996; 49:1373-1379.
10. Huque MF, Dubey SD. A meta-analysis methodology for utilizing study-level covariate information from clinical trials. Communications in Statistics - Theory and Methods 1994; 23:377-394.
11. Stram DO. Meta-analysis of published data using a linear mixed-effects model. Biometrics 1996; 52:536-544.
12. Thompson SG, Sharp SJ. Explaining heterogeneity in meta-analysis: a comparison of methods. Statistics in Medicine 1999; 18:2693-2708.
13. Greenland S. Quantitative methods in the review of epidemiologic literature. Epidemiologic Reviews 1987; 9:1-30.
14. Berkey CS, Hoaglin DC, Mosteller F, Colditz GA. A random-effects regression model for meta-analysis. Statistics in Medicine 1995; 14:395-411.
15. Sharp S. Meta-analysis regression. Stata Technical Bulletin 1998; 42:16-22.
16. Knapp G, Hartung J. Improved tests for a random effects meta-regression with a single covariate. Stalistics in Medicine 2003; 22:2693-2710.
17. Hochberg J, Tamhane A. Multiple Comparison procedures. Wiley: New York, 1987.
18. Rothman KJ. No adjustments are needed for multiple comparisons. Epidemiology 1990; 1:43-46.
19. Lawlor DA, Hopker SW. The effectiveness of exercise as an intervention in the management of depression: systematic review and meta-regression analysis of randomised controlled trials. British Medical Journal 2001; 322:1-8.
20. Sánchez-Meca J, Marín-Martínez F. Testing continuous moderators in meta-analysis: a comparison of procedures. British Journal of Mathematical and Statistical Psychology 1998; 51:311-326.
21. Marín-Martínez F, Sánchez-Meca J. Testing for dichotomous moderators in meta-analysis. Journal of Experimental Education 1998; 67:69-81.
22. Higgins JPT, Thompson SG. Quantifying heterogeneity in a meta-analysis. Statistics in Medicine 2002; 21:1539-1558.
23. Edgington ES. Randomization Tests. Marcel Dekker: New York, 1995.
24. Manly BFJ. Randomization, Bootstrap and Monte Carlo Methods in Biology. Chapman & Hall: London, 1997.
25. Sprent P. Data Driven Statistical Methods. Chapman & Hall: London, 1998.
26. Noreen EW. Computer-Intensive Methods for Testing Hypotheses: an Introduction. Wiley: New York, 1989.
27. Thompson SG. Why sources of heterogeneity in meta-analysis should be investigated. British Medical Journal 1994; 309:1351-1355.
28. Higgins J, Thompson S, Deeks J, Altman D. Statistical heterogeneity in systematic reviews of clinical trials: a critical appraisal of guidelines and practice. Journal of Health Service Research Policy 2002; 7:51-61.
29. Westfall PH, Young SS. Resampling-Based Multiple Testing: Examples and Methods for P-Value Adjustment. Wiley: New York, 1993.
30. Goodman SN. Multiple comparisons, explained. American Journal of Epidemiology 1998; 147:807-812.
31. DuMouchel WH, Harris JE. Bayes methods for combining the results of cancer studies in humans and other species. Journal of the American Statistical Association 1983; 78:293-308.
32. Greenland S, Robins JM. Empirical Hayes adjustments for multiple comparisons are sometimes useful. Epidemiology 1991; 2:244-251.
33. Greenland S. When should epidemiologic regressions use random coefficients? Biometrics 2000; 56:915-921.