期刊
BIOMETRICAL JOURNAL
卷 59, 期 2, 页码 315-330出版社
WILEY
DOI: 10.1002/bimj.201600107
关键词
Generalized estimating equation; Longitudinal studies; Optimal design; Power; Working correlation matrix
资金
- NCI NIH HHS [P30 CA091842, P50 CA196510] Funding Source: Medline
Longitudinal studies are often applied in biomedical research and clinical trials to evaluate the treatment effect. The association pattern within the subjectmust be considered in both sample size calculation and the analysis. One of the most important approaches to analyze such a study is the generalized estimating equation (GEE) proposed by Liang and Zeger, in which working correlation structure is introduced and the association pattern within the subject depends on a vector of association parameters denoted by.. The explicit sample size formulas for two-group comparison in linear and logistic regression models are obtained based on the GEE method by Liu and Liang. For cluster randomized trials (CRTs), researchers proposed the optimal sample sizes at both the cluster and individual level as a function of sampling costs and the intracluster correlation coefficient (ICC). In these approaches, the optimal sample sizes depend strongly on the ICC. However, the ICC is usually unknown for CRTs and multicenter trials. To overcome this shortcoming, Van Breukelen et al. consider a range of possible ICC values identified from literature reviews and present Maximin designs (MMDs) based on relative efficiency (RE) and efficiency under budget and cost constraints. In this paper, the optimal sample size and number of repeated measurements using GEE models with an exchangeable working correlation matrix is proposed under the considerations of fixed budget, where optimal refers to maximum power for a given sampling budget. The equations of sample size and number of repeated measurements for a known parameter value rho are derived and a straightforward algorithm for unknown rho is developed. Applications in practice are discussed. We also discuss the existence of the optimal design when an AR(1) working correlation matrix is assumed. Our proposed method can be extended under the scenarios when the true and working correlation matrix are different.
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