%0 Journal Article %1 Tu:2004:Stat-Med:15344187 %A Tu, X M %A Kowalski, J %A Zhang, J %A Lynch, K G %A Crits-Christoph, P %D 2004 %J Stat Med %K CorrelatedData LongitudinalDataAnalysis RandomEffects SampleSize gee review statistics %N 18 %P 2799-2815 %R 10.1002/sim.1869 %T Power analyses for longitudinal trials and other clustered designs %U https://www.ncbi.nlm.nih.gov/pubmed/15344187 %V 23 %X Existing methods for power and sample size estimation for longitudinal and other clustered study designs have limited applications. In this paper, we review and extend existing approaches to improve these limitations. In particular, we focus on power analysis for the two most popular approaches for clustered data analysis, the generalized estimating equations and the linear mixed-effects models. By basing the derivation of the power function on the asymptotic distribution of the model estimates, the proposed approach provides estimates of power that are consistent with the methods of inference for data analysis. The proposed methodology is illustrated with numerous examples that are motivated by real study designs.

@article{Tu:2004:Stat-Med:15344187, abstract = {Existing methods for power and sample size estimation for longitudinal and other clustered study designs have limited applications. In this paper, we review and extend existing approaches to improve these limitations. In particular, we focus on power analysis for the two most popular approaches for clustered data analysis, the generalized estimating equations and the linear mixed-effects models. By basing the derivation of the power function on the asymptotic distribution of the model estimates, the proposed approach provides estimates of power that are consistent with the methods of inference for data analysis. The proposed methodology is illustrated with numerous examples that are motivated by real study designs.}, added-at = {2018-09-21T01:18:44.000+0200}, author = {Tu, X M and Kowalski, J and Zhang, J and Lynch, K G and Crits-Christoph, P}, biburl = {https://www.bibsonomy.org/bibtex/2c912f85aa4d5004cd61af8cba57e9d81/jkd}, description = {Power analyses for longitudinal trials and other clustered designs. - PubMed - NCBI}, doi = {10.1002/sim.1869}, interhash = {93b033e8cb61634dd1ba0436faf8c485}, intrahash = {c912f85aa4d5004cd61af8cba57e9d81}, journal = {Stat Med}, keywords = {CorrelatedData LongitudinalDataAnalysis RandomEffects SampleSize gee review statistics}, month = sep, number = 18, pages = {2799-2815}, pmid = {15344187}, timestamp = {2019-11-17T06:33:20.000+0100}, title = {Power analyses for longitudinal trials and other clustered designs}, url = {https://www.ncbi.nlm.nih.gov/pubmed/15344187}, volume = 23, year = 2004 }