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1.  Metaprop: a Stata command to perform meta-analysis of binomial data 
Archives of Public Health  2014;72(1):39.
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.
doi:10.1186/2049-3258-72-39
PMCID: PMC4373114  PMID: 25810908
Meta-analysis; Stata; Binomial; Logistic-normal; Confidence intervals; Freeman-Tukey double arcsine transformation
2.  High burden of breast cancer in Belgium: recent trends in incidence (1999-2006) and historical trends in mortality (1954-2006) 
Introduction
In Belgium, breast cancer mortality has been monitored since 1954, whereas cancer incidence data have only been made available for a few years. In this article we update historical trends of breast cancer mortality and describe the recent breast cancer incidence.
Methods
Incidence data were extracted from the Belgium Cancer Registry from 2004 to 2006 for the Walloon and Brussels Regions and Belgium, and from 1999 to 2006 for the Flemish Region. The Directorate-general Statistics and Economic information provided the mortality data for the years 1954-1999 and 2004. The regional authorities of the Flemish and Brussels Regions provided the mortality data for the years 2000-2003 and 2005-2006.
Results
In 2004, the World age-standardised breast cancer incidence for the whole of Belgium was 110 per 100, 000 person-years for all ages; and 172, 390 and 345 per 100, 000 person-years for the 35-49, 50-69, and 70+ age groups, respectively. The incidence rate was slightly higher in each age group in the Brussels Region. In Flanders, where the incidence could be observed during a longer period, an increase was observed until 2003 in the 50-69 age group, followed by a decrease. To the contrary, in the oldest age group, incidence continued to rise over the whole period, whereas no change in incidence was observed between 1999 and 2006 in the 35-49 age group.
Mortality increased until the late 1980s and afterwards decreased in all regions and in age groups younger than 70. In women of 70 years and older, the decline began later.
Conclusions
The burden of breast cancer in Belgium is very high. In 2004, Belgium ranked first for the age-standardised incidence rate in Europe for all ages combined and in the 35-49 and 50-69 age groups. The impact of the known risk factors and of mammographic screening should be further studied. The mortality rate in Belgium ranked lower than incidence, suggesting favourable survival. Plausible explanations for the discrepancy between incidence and mortality are discussed.
doi:10.1186/0778-7367-69-2
PMCID: PMC3436615  PMID: 22958447

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