Traditional reviews, meta-analyses and pooled analyses in epidemiology

被引:434
作者
Blettner, M
Sauerbrei, W
Schlehofer, B
Scheuchenpflug, T
Friedenreich, C
机构
[1] German Canc Res Ctr, Div Epidemiol, D-69120 Heidelberg, Germany
[2] Int Agcy Res Canc, F-69372 Lyon 08, France
[3] Univ Freiburg, Inst Med Biometry & Med Informat, D-79104 Freiburg, Germany
[4] Alberta Canc Board, Div Epidemiol Prevent & Screening, Calgary, AB T2N 4N2, Canada
关键词
epidemiological methods; meta-analysis; pooled analysis; reviews; risk factors;
D O I
10.1093/ije/28.1.1
中图分类号
R1 [预防医学、卫生学];
学科分类号
1004 ; 120402 ;
摘要
Background The use of review articles and meta-analysis has become an important part of epidemiological research, mainly for reconciling previously conducted studies that have inconsistent results, Numerous methodologic issues particularly with respect to biases and the use of meta-analysis are still controversial. Methods Four methods summarizing data from epidemiological studies are described. The rationale for meta-analysis and the statistical methods used are outlined. The strengths and limitations of these methods are compared particularly with respect to their ability to investigate heterogeneity between studies and to provide quantitative risk estimation. Results Meta-analyses from published data are in general insufficient to calculate a pooled estimate since published estimates are based on heterogeneous populations, different study designs and mainly different statistical models. More reliable results can be expected if individual data are available for a pooled analysis, although some heterogeneity still remains, Large prospective planned meta-analysis of multicentre studies would be preferable to investigate small risk factors, however this type of meta-analysis is expensive and rime-consuming. Conclusion For a full assessment of risk factors with a high prevalence in the general population, pooling of data will become increasingly important. Future research needs to focus on the deficiencies of review methods, in particular, the errors and biases that can be produced when studies are combined that have used different designs, methods and analytic models.
引用
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页码:1 / 9
页数:9
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