Why add anything to nothing? The arcsine difference as a measure of treatment effect in meta-analysis with zero cells

Statistics in Medicine

Volumn 28, Issue 5, 2009, Pages 721-738

  Rücker, Gerta ^a   Schwarzer, Guido ^a   Carpenter, James ^a,b   Olkin, Ingram ^c  

^a UNIVERSITY OF FREIBURG   (Germany)

^b LONDON SCHOOL OF HYGIENE AND TROPICAL MEDICINE   (United Kingdom)

^c STANFORD UNIVERSITY   (United States)

Author keywords

Arcsine transformation; Effect measures; Meta analysis; Rare events; Zero event trials

Indexed keywords

ARCSINE DIFFERENCE; ATTRIBUTABLE RISK; CLINICAL RESEARCH; CLINICAL TRIAL; CONTROLLED CLINICAL TRIAL; CORONARY ARTERY BYPASS GRAFT; FLUCTUATING ASYMMETRY; HUMAN; INTERNAL CONSISTENCY; INTERVENTION STUDY; META ANALYSIS; OFF PUMP CORONARY SURGERY; OFF PUMP SURGERY; PROBABILITY; RANDOM SAMPLE; REVIEW; RISK ASSESSMENT; RISK BENEFIT ANALYSIS; SIMULATION; STATISTICAL ANALYSIS; SYSTEMATIC ERROR; VARIANCE; ALGORITHM; ANALYSIS OF VARIANCE; ARTICLE; COMPUTER SIMULATION; CONFIDENCE INTERVAL; EPIDEMIOLOGY; METHODOLOGY; POSTOPERATIVE COMPLICATION; RISK; SAMPLE SIZE; STATISTICAL MODEL; TREATMENT OUTCOME;

ALGORITHMS; ANALYSIS OF VARIANCE; BIAS (EPIDEMIOLOGY); CLINICAL TRIALS AS TOPIC; COMPUTER SIMULATION; CONFIDENCE INTERVALS; CORONARY ARTERY BYPASS, OFF-PUMP; HUMANS; META-ANALYSIS AS TOPIC; MODELS, STATISTICAL; ODDS RATIO; POSTOPERATIVE COMPLICATIONS; RISK; RISK ASSESSMENT; SAMPLE SIZE; TREATMENT OUTCOME;

EID: 64549090671     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.3511     Document Type: Review

References (46)

1. Greenland S. Interpretation and choice of effect measures in epidemiologic analyses. American Journal of Epidemiology 1987; 125:761-768.
2. Sinclair JC, Bracken MB. Clinically useful measures of effect in binary analyses of randomized trials. Journal of Clinical Epidemiology 1994; 47:881-889.
3. Engels EA, Schmid CH, Terrin N, Olkin I, Lau J. Heterogeneity and statistical significance in meta-analysis: an empirical study of 125 meta-analyses. Statistics in Medicine 2000; 19:1707-1728.
4. Deeks JJ. Issues in the selection of a summary statistic for meta-analysis of clinical trials with binary outcomes. Statistics in Medicine 2002; 21:1575-1600.
5. Duncan B, Olkin I. Bias of estimates of the number needed to treat. Statistics in Medicine 2005; 24:1837-1848.
6. White IW, Elbourne D. Assessing subgroup effects with binary data: can the use of different effect measures lead to different conclusions? BMC Medical Research Methodology 2005; 2005(5):15. http://www.biomedcentral.com/1471-2288/5/15. Accessed 27 October 2008.
7. Newcombe RG. A deficiency of the odds ratio as a measure of effect size. Statistics in Medicine 2006; 25:4235-4240.
8. Sweeting MJ, Sutton AJ, Lambert PC. What to add to nothing? Use and avoidance of continuity corrections in meta-analysis of sparse data. Statistics in Medicine 2004; 23:1351-1375.
9. Friedrich JO, Adhikari NK, Beyene J. Inclusion of zero total event trials in meta-analyses maintains analytic consistency and incorporates all available data. BMC Medical Research Methodology 2007; 23:5.
10. Sutton AJ, Cooper N, Lambert P, Jones DR, Abrams KR, Sweeting M. Meta-analysis of rare and adverse event data. Expert Review of Pharmacoeconomics and Outcomes Research 2002; 2:367-379.
11. Bradburn MJ, Deeks JJ, Berlin JA, Localio AR. Much ado about nothing: a comparison of the performance of meta-analytical methods with rare events. Statistics in Medicine 2007; 26:53-77.
12. Kuss O, Gromann C. An exact test for meta-analysis with binary endpoints. Methods of Information in Medicine 2007; 46:662-668.
13. Rücker G, Schwarzer G, Carpenter JR. Arcsine test for publication bias in meta-analyses with binary outcomes. Statistics in Medicine 2008; 27(5):746-763.
14. McCullagh P, Nelder J. Generalized Linear Models. Chapman & Hall: London, 1989.
15. Fisher R, Yates F. Statistical Tables for Biological, Agricultural and Medical Research (3rd edn, 1948). Oliver and Boyd: Edinburgh, 1938.
16. Anscombe F. The transformation of Poisson, binomial and negative-binomial data. Biometrika 1948; 35(3-4): 246-254.
17. Freeman MF, Tukey JW. Transformations related to the angular and the square root. Annals of Mathematical Statistics 1950; 21:607-611.
18. Armitage P, Berry G, Matthews JNS. Statistical Methods in Medical Research. Blackwell Science: Oxford, 1987.
19. A'hern RP. Widening eligibility to phase II trials: constant arcsine difference phase II trials. Controlled Clinical Trials 2004; 25:251-264.
20. Choudhury JB. Non-parametric confidence interval estimation for competing risks analysis: application to contraceptive data. Statistics in Medicine 2002; 21:1129-1144.
21. Hoover DR, Blackwelder WC. Allocation of subjects to test null relative risks smaller than one. Statistics in Medicine 2001; 20:3071-3082.
22. Kulinskaya E, Staudte RG. Confidence intervals for the standardized effect arising in the comparison of two normal populations. Statistics in Medicine 2007; 26:2853-2871. DOI: 10.1002/ sim.2751.
23. Hamza TH, van Houwelingen HC, Stijnen T. Random effects meta-analysis of proportions: the binomial distribution should be used to model the within-study variability. Journal of Clinical Epidemiology 2007; DOI: 10.1016/j.jclinepi.2007.03.016.
24. Trikalinos TA, Olkin I. A method for the meta-analysis of mutually exclusive binary outcomes. Statistics in Medicine 2008; DOI: 10.1002/sim.3299. www.interscience.wiley.com. Accessed 27 October 2008.
25. Tversky A, Kahneman D. The framing of decisions and the psychology of choice. Science 1981; 211:453-458.
26. Higgins JP, Green S. Cochrane Handbook for Systematic Reviews of Interventions Version 5.0.0 (version 5.0.0 edition 2008). http://www.cochrane-handbook.org. Accessed 27 October 2008.
27. Cornfield J. A method of estimating comparative rates from clinical data. Applications to cancer of the lung, breast and cervix. Journal of the National Cancer Institute 1951; 11:1269-1275.
28. Mosteller F. Association and estimation in contingency tables. Journal of the American Statistical Association 1968; 63:1-28.
29. Fleiss JL, Levin B, Paik MC. Statistical Methods for Rates and Proportions (3rd edn). Wiley: New York, 2003.
30. Hutton J. Number needed to treat: properties and problems. Journal of the Royal Statistical Society Series A - Statistics in Society 2000; 163(3):403-415.
31. Higgins JPT, Thompson SG. Quantifying heterogeneity in a meta-analysis. Statistics in Medicine 2002; 21: 1539-1558.
32. Cheng DC, Bainbridge D, Martin JE, Novick RJ. The evidence-based perioperative clinical outcomes research group. Does off-pump coronary artery bypass reduce mortality, morbidity, and resource utilization when compared with conventional coronary artery bypass? A meta-analysis of randomized trials. Anesthesiology 2005; 102(1): 188-203.
33. Olkin I. Statistical and theoretical considerations in meta-analysis. Journal of Clinical Epidemiology 1995; 48:133-146.
34. Nissen SE, Wolski K. Effect of rosiglitazone on the risk of myocardial infarction and death from cardiovascular diseases. The New England Journal of Medicine 2007; 356(24):2457-2471.
35. Rücker G, Schumacher M. Simpson's paradox visualized: the example of the rosiglitazone meta-analysis. BMC Medical Research Methodology 2008; 8(1):34.
36. Greenland S, Robins JM. Estimation of a common effect parameter from sparse follow-up data. Biometrics 1985; 41:55-68.
37. van Houwelingen HC, Zwinderman KH, Stijnen T. A bivariate approach to meta-analysis. Statistics in Medicine 1993; 12:2273-2284.
38. Yusuf S, Peto R, Lewis J, Collins R, Sleight P. Beta blockade during and after myocardial infarction: an overview of the randomized trials. Progress in Cardiovascular Diseases 1985; 27:335-371.
39. Kasuya E. Angular transformation - another effect of different sample sizes. Ecological Research 2004; 19: 165-167.
40. Greenland S, Salvan A. Bias in the one-step method for pooling study results. Statistics in Medicine 1990; 9:247-252.
41. Mahomed K. Iron supplementation in pregnancy. The Cochrane Database of Systematic Reviews 2000, Issue 1, 2000. Art. No.: CD000117. DOI: 10.1002/14651858.CD000117.
42. Diamond GA, Bax L, Kaul S. Uncertain effects of rosiglitazone on the risk for myocardial infarction and cardiovascular death. Annals of Internal Medicine 2007; 147(8):578-581.
43. Whitehead A, Whitehead J. A general parametric approach to the meta-analysis of randomized clinical trials. Statistics in Medicine 1991; 10(11):1665-1677.
44. Warn DE, Thompson SG, Spiegelhalter DJ. Bayesian random effects meta-analysis of trials with binary outcomes: methods for the absolute risk difference and relative risk scales. Statistics in Medicine 2002; 21:1601-1623.
45. R Development Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria 2006. ISBN 3-900051-07-0. http://www.R-project.org. Accessed 27 October 2008.
46. Schwarzer G. meta: An R package for meta-analysis. R News 2007; 7(3):40-45. http://cran.r-project.org/doc/Rnews. Accessed 27 October 2008.