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Open Access Research article

Meta-analytic methods for pooling rates when follow-up duration varies: a case study

James P Guevara1*, Jesse A Berlin2 and Fredric M Wolf3

Author Affiliations

1 Department of Pediatrics, The Children's Hospital of Philadelphia and the University of Pennsylvania School of Medicine, 3535 Market St, Room 1531, Philadelphia, PA 19104, USA

2 Department of Biostatistics and Epidemiology, Center for Clinical Epidemiology and Biostatistics, Center for Education and Research on Therapeutics, University of Pennsylvania School of Medicine, Blockley Hall, Room 611, Philadelphia, PA 19104, USA

3 Department of Medical Education and Biomedical Informatics, University of Washington School of Medicine, 1959 NE Pacific St, Room E-312, Seattle, WA 98195, USA

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BMC Medical Research Methodology 2004, 4:17  doi:10.1186/1471-2288-4-17

Published: 12 July 2004

Abstract

Background

Meta-analysis can be used to pool rate measures across studies, but challenges arise when follow-up duration varies. Our objective was to compare different statistical approaches for pooling count data of varying follow-up times in terms of estimates of effect, precision, and clinical interpretability.

Methods

We examined data from a published Cochrane Review of asthma self-management education in children. We selected two rate measures with the largest number of contributing studies: school absences and emergency room (ER) visits. We estimated fixed- and random-effects standardized weighted mean differences (SMD), stratified incidence rate differences (IRD), and stratified incidence rate ratios (IRR). We also fit Poisson regression models, which allowed for further adjustment for clustering by study.

Results

For both outcomes, all methods gave qualitatively similar estimates of effect in favor of the intervention. For school absences, SMD showed modest results in favor of the intervention (SMD -0.14, 95% CI -0.23 to -0.04). IRD implied that the intervention reduced school absences by 1.8 days per year (IRD -0.15 days/child-month, 95% CI -0.19 to -0.11), while IRR suggested a 14% reduction in absences (IRR 0.86, 95% CI 0.83 to 0.90). For ER visits, SMD showed a modest benefit in favor of the intervention (SMD -0.27, 95% CI: -0.45 to -0.09). IRD implied that the intervention reduced ER visits by 1 visit every 2 years (IRD -0.04 visits/child-month, 95% CI: -0.05 to -0.03), while IRR suggested a 34% reduction in ER visits (IRR 0.66, 95% CI 0.59 to 0.74). In Poisson models, adjustment for clustering lowered the precision of the estimates relative to stratified IRR results. For ER visits but not school absences, failure to incorporate study indicators resulted in a different estimate of effect (unadjusted IRR 0.77, 95% CI 0.59 to 0.99).

Conclusions

Choice of method among the ones presented had little effect on inference but affected the clinical interpretability of the findings. Incidence rate methods gave more clinically interpretable results than SMD. Poisson regression allowed for further adjustment for heterogeneity across studies. These data suggest that analysts who want to improve the clinical interpretability of their findings should consider incidence rate methods.