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Open Access Highly Accessed Research article

A simulation study of sample size for multilevel logistic regression models

Rahim Moineddin234*, Flora I Matheson12 and Richard H Glazier1234

Author Affiliations

1 Centre for Research on Inner City Health, St. Michael's Hospital, Toronto, Canada

2 Department of Public Health Sciences, University of Toronto, Toronto, Canada

3 Department of Family and Community Medicine, University of Toronto, Canada

4 Institute for Clinical Evaluative Sciences, Toronto, Canada

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BMC Medical Research Methodology 2007, 7:34  doi:10.1186/1471-2288-7-34

Published: 16 July 2007

Abstract

Background

Many studies conducted in health and social sciences collect individual level data as outcome measures. Usually, such data have a hierarchical structure, with patients clustered within physicians, and physicians clustered within practices. Large survey data, including national surveys, have a hierarchical or clustered structure; respondents are naturally clustered in geographical units (e.g., health regions) and may be grouped into smaller units. Outcomes of interest in many fields not only reflect continuous measures, but also binary outcomes such as depression, presence or absence of a disease, and self-reported general health. In the framework of multilevel studies an important problem is calculating an adequate sample size that generates unbiased and accurate estimates.

Methods

In this paper simulation studies are used to assess the effect of varying sample size at both the individual and group level on the accuracy of the estimates of the parameters and variance components of multilevel logistic regression models. In addition, the influence of prevalence of the outcome and the intra-class correlation coefficient (ICC) is examined.

Results

The results show that the estimates of the fixed effect parameters are unbiased for 100 groups with group size of 50 or higher. The estimates of the variance covariance components are slightly biased even with 100 groups and group size of 50. The biases for both fixed and random effects are severe for group size of 5. The standard errors for fixed effect parameters are unbiased while for variance covariance components are underestimated. Results suggest that low prevalent events require larger sample sizes with at least a minimum of 100 groups and 50 individuals per group.

Conclusion

We recommend using a minimum group size of 50 with at least 50 groups to produce valid estimates for multi-level logistic regression models. Group size should be adjusted under conditions where the prevalence of events is low such that the expected number of events in each group should be greater than one.