Skip to main content
Top
Published in: Archives of Public Health 1/2014

Open Access 01-12-2014 | Methodology

Metaprop: a Stata command to perform meta-analysis of binomial data

Authors: Victoria N Nyaga, Marc Arbyn, Marc Aerts

Published in: Archives of Public Health | Issue 1/2014

Login to get access

Abstract

Background

Meta-analyses have become an essential tool in synthesizing evidence on clinical and epidemiological questions derived from a multitude of similar studies assessing the particular issue. Appropriate and accessible statistical software is needed to produce the summary statistic of interest.

Methods

Metaprop is a statistical program implemented to perform meta-analyses of proportions in Stata. It builds further on the existing Stata procedure metan which is typically used to pool effects (risk ratios, odds ratios, differences of risks or means) but which is also used to pool proportions. Metaprop implements procedures which are specific to binomial data and allows computation of exact binomial and score test-based confidence intervals. It provides appropriate methods for dealing with proportions close to or at the margins where the normal approximation procedures often break down, by use of the binomial distribution to model the within-study variability or by allowing Freeman-Tukey double arcsine transformation to stabilize the variances. Metaprop was applied on two published meta-analyses: 1) prevalence of HPV-infection in women with a Pap smear showing ASC-US; 2) cure rate after treatment for cervical precancer using cold coagulation.

Results

The first meta-analysis showed a pooled HPV-prevalence of 43% (95% CI: 38%-48%). In the second meta-analysis, the pooled percentage of cured women was 94% (95% CI: 86%-97%).

Conclusion

By using metaprop, no studies with 0% or 100% proportions were excluded from the meta-analysis. Furthermore, study specific and pooled confidence intervals always were within admissible values, contrary to the original publication, where metan was used.
Appendix
Available only for authorised users
Literature
1.
go back to reference Agresti A, Coull BA: Approximate is better than ’exact’ for interval estimation of binomial proportions. Am Stat. 1998, 52 (2): 119-126. Agresti A, Coull BA: Approximate is better than ’exact’ for interval estimation of binomial proportions. Am Stat. 1998, 52 (2): 119-126.
2.
go back to reference Breslow NE, Clayton DG: Approximate inference in generalized linear mixed models. J Am Stat Assoc. 1993, 88: 9-25. Breslow NE, Clayton DG: Approximate inference in generalized linear mixed models. J Am Stat Assoc. 1993, 88: 9-25.
3.
go back to reference Miller JJ: The inverse of the Freeman-Tukey double arcsine transformation. Am Stat. 1978, 32 (4): 138- Miller JJ: The inverse of the Freeman-Tukey double arcsine transformation. Am Stat. 1978, 32 (4): 138-
4.
go back to reference Hamza TH, van Houwelingen HC, Stijnen T: The binomial distribution of meta-analysis was preferred to model within-study variability. J Clin Epidemiol. 2008, 61: 41-51. 10.1016/j.jclinepi.2007.03.016.CrossRefPubMed Hamza TH, van Houwelingen HC, Stijnen T: The binomial distribution of meta-analysis was preferred to model within-study variability. J Clin Epidemiol. 2008, 61: 41-51. 10.1016/j.jclinepi.2007.03.016.CrossRefPubMed
5.
go back to reference Molenberghs G, Verbeke G, Iddib S, Demétrio CGB: A combined beta and normal random-effects model for repeated, over-dispersed binary and binomial data. J Multivar Anal. 2012, 111: 94-109.CrossRef Molenberghs G, Verbeke G, Iddib S, Demétrio CGB: A combined beta and normal random-effects model for repeated, over-dispersed binary and binomial data. J Multivar Anal. 2012, 111: 94-109.CrossRef
6.
go back to reference DerSimonian R, Laird N: Meta-analysis in clinical trials. Control Clin Trials. 1986, 7: 177-188. 10.1016/0197-2456(86)90046-2.CrossRefPubMed DerSimonian R, Laird N: Meta-analysis in clinical trials. Control Clin Trials. 1986, 7: 177-188. 10.1016/0197-2456(86)90046-2.CrossRefPubMed
7.
go back to reference Engel E, Keen A: A simple approach for the analysis of generalized linear mixed models. Stat Neerl. 1994, 48: 1-22. 10.1111/j.1467-9574.1994.tb01428.x.CrossRef Engel E, Keen A: A simple approach for the analysis of generalized linear mixed models. Stat Neerl. 1994, 48: 1-22. 10.1111/j.1467-9574.1994.tb01428.x.CrossRef
8.
go back to reference Molenberghs G, Verbeke G, Demétrio CGB, Vieira AMC: A family of generalized linear models for repeated measures with normal and conjugate random effects. Stat Sci. 2010, 3: 325-347.CrossRef Molenberghs G, Verbeke G, Demétrio CGB, Vieira AMC: A family of generalized linear models for repeated measures with normal and conjugate random effects. Stat Sci. 2010, 3: 325-347.CrossRef
9.
go back to reference Jackson D, Bowden J, Baker R: How does the Dersimonian and Laird procedure for random effects meta-analysis compare with its more efficient but harder to compute counterparts?. J Stat Plan Inference. 2010, 140: 961-970. 10.1016/j.jspi.2009.09.017.CrossRef Jackson D, Bowden J, Baker R: How does the Dersimonian and Laird procedure for random effects meta-analysis compare with its more efficient but harder to compute counterparts?. J Stat Plan Inference. 2010, 140: 961-970. 10.1016/j.jspi.2009.09.017.CrossRef
10.
go back to reference Harris R, Bradburn M, Deeks J, Harbord R, Altman D, Sterne J: metan: fixed- and random-effects meta-analysis. Stata J. 2008, 8 (1): 3-28. Harris R, Bradburn M, Deeks J, Harbord R, Altman D, Sterne J: metan: fixed- and random-effects meta-analysis. Stata J. 2008, 8 (1): 3-28.
11.
go back to reference Box GEP, Hunter JS, Hunter WG: Statistics for experimenters. 1978, Hoboken (NJ), USA: J Wiley & Sons Inc, Wiley Series in Probability and Statistics Box GEP, Hunter JS, Hunter WG: Statistics for experimenters. 1978, Hoboken (NJ), USA: J Wiley & Sons Inc, Wiley Series in Probability and Statistics
12.
go back to reference Freeman MF, Tukey JW: Transformations related to the angular and the square root. Ann Math Stats. 1950, 21 (4): 607-611. 10.1214/aoms/1177729756.CrossRef Freeman MF, Tukey JW: Transformations related to the angular and the square root. Ann Math Stats. 1950, 21 (4): 607-611. 10.1214/aoms/1177729756.CrossRef
13.
go back to reference Clopper CJ, Pearson ES: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika. 1934, 26 (4): 404-413. 10.1093/biomet/26.4.404.CrossRef Clopper CJ, Pearson ES: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika. 1934, 26 (4): 404-413. 10.1093/biomet/26.4.404.CrossRef
14.
go back to reference Brown LD, Cai TT, DasGupta A: Interval estimation for a binomial proportion. Stat Sci. 2001, 16: 404-413. Brown LD, Cai TT, DasGupta A: Interval estimation for a binomial proportion. Stat Sci. 2001, 16: 404-413.
15.
go back to reference Newcombe RG: Two-sided confidence intervals for the single proportion: comparison of seven methods. Stat Med. 1998, 17: 857-872. 10.1002/(SICI)1097-0258(19980430)17:8<857::AID-SIM777>3.0.CO;2-E.CrossRefPubMed Newcombe RG: Two-sided confidence intervals for the single proportion: comparison of seven methods. Stat Med. 1998, 17: 857-872. 10.1002/(SICI)1097-0258(19980430)17:8<857::AID-SIM777>3.0.CO;2-E.CrossRefPubMed
16.
go back to reference Wilson EB: Probable inference, the law of succession, and statistical inference. J Am Stat Assoc. 1927, 22 (158): 209-212. 10.1080/01621459.1927.10502953.CrossRef Wilson EB: Probable inference, the law of succession, and statistical inference. J Am Stat Assoc. 1927, 22 (158): 209-212. 10.1080/01621459.1927.10502953.CrossRef
17.
go back to reference Arbyn M, Martin-Hirsch P, Buntinx F, Ranst MV, Paraskevaidis E, Dillner J: Triage of women with equivocal or low-grade cervical cytology results a meta-analysis of the hpv test positivity rate. J Cell Mol Med. 2009, 13 (4): 648-659. 10.1111/j.1582-4934.2008.00631.x.CrossRefPubMedPubMedCentral Arbyn M, Martin-Hirsch P, Buntinx F, Ranst MV, Paraskevaidis E, Dillner J: Triage of women with equivocal or low-grade cervical cytology results a meta-analysis of the hpv test positivity rate. J Cell Mol Med. 2009, 13 (4): 648-659. 10.1111/j.1582-4934.2008.00631.x.CrossRefPubMedPubMedCentral
18.
go back to reference Dolman L, Sauvaget C, Muwonge R, Sankaranarayanan R: Meta-analysis of the efficacy of cold coagulation as a treatment method for cervical intra-epithelial neoplasis: a systematic review. BJOG. 2014, 121: 929-942. 10.1111/1471-0528.12655.CrossRefPubMed Dolman L, Sauvaget C, Muwonge R, Sankaranarayanan R: Meta-analysis of the efficacy of cold coagulation as a treatment method for cervical intra-epithelial neoplasis: a systematic review. BJOG. 2014, 121: 929-942. 10.1111/1471-0528.12655.CrossRefPubMed
19.
go back to reference Arbyn M, Ronco G, Anttila A, Meijer CJLM, Poljak M, Ogilvie G, Koliopoulos G, Naucler P, Sankaranarayanan R, Petok J: Evidence regarding human papillomavirus testing in secondary prevention of cervical cancer. Vaccine. 2012, 30 (Suppl 5): F88-F99.CrossRefPubMed Arbyn M, Ronco G, Anttila A, Meijer CJLM, Poljak M, Ogilvie G, Koliopoulos G, Naucler P, Sankaranarayanan R, Petok J: Evidence regarding human papillomavirus testing in secondary prevention of cervical cancer. Vaccine. 2012, 30 (Suppl 5): F88-F99.CrossRefPubMed
20.
go back to reference Arbyn M, Roelens J, Simoens C, Buntinx F, Paraskevaidis E, Martin-Hirsch PP, Prendiville WJ: Human papillomavirus testing versus repeat cytology for triage of minor cytological cervical lesions. Cochrane Database Syst Rev. 2013, 3 (CD008054): 1-201. Arbyn M, Roelens J, Simoens C, Buntinx F, Paraskevaidis E, Martin-Hirsch PP, Prendiville WJ: Human papillomavirus testing versus repeat cytology for triage of minor cytological cervical lesions. Cochrane Database Syst Rev. 2013, 3 (CD008054): 1-201.
21.
go back to reference Westfall PH, Young SS: Resampling-based multiple testing: examples and methods for P-value adjustment. 1993, Hoboken (NJ), USA: John Wiley & Sons Westfall PH, Young SS: Resampling-based multiple testing: examples and methods for P-value adjustment. 1993, Hoboken (NJ), USA: John Wiley & Sons
Metadata
Title
Metaprop: a Stata command to perform meta-analysis of binomial data
Authors
Victoria N Nyaga
Marc Arbyn
Marc Aerts
Publication date
01-12-2014
Publisher
BioMed Central
Published in
Archives of Public Health / Issue 1/2014
Electronic ISSN: 2049-3258
DOI
https://doi.org/10.1186/2049-3258-72-39

Other articles of this Issue 1/2014

Archives of Public Health 1/2014 Go to the issue