Abstract
Correlated data occur frequently in biomedical research. Examples include longitudinal studies, family studies, and ophthalmologic studies. In this paper, we present a method to compute sample sizes and statistical powers for studies involving correlated observations. This is a multivariate extension of the work by Self and Mauritsen (1988, Biometrics 44, 79-86), who derived a sample size and power formula for generalized linear models based on the score statistic. For correlated data, we appeal to a statistic based on the generalized estimating equation method (Liang and Zeger, 1986, Biometrika 73, 13-22). We highlight the additional assumptions needed to deal with correlated data. Some special cases that are commonly seen in practice are discussed, followed by simulation studies.
Original language | English (US) |
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Pages (from-to) | 937-947 |
Number of pages | 11 |
Journal | Biometrics |
Volume | 53 |
Issue number | 3 |
DOIs | |
State | Published - Sep 1997 |
Externally published | Yes |
Keywords
- Correlated data
- Generalized estimating equation
- Noncentral chi- square
- Sample size
- Statistical power
ASJC Scopus subject areas
- Statistics and Probability
- General Biochemistry, Genetics and Molecular Biology
- General Immunology and Microbiology
- General Agricultural and Biological Sciences
- Applied Mathematics