Correlated ordinal response data often arise in public health studies. Sample-size (power) calculations are a crucial step in designing such studies to ensure an adequate sample to detect a significant effect. Here we extend Rochon's method of sample-size estimation with a repeated binary response to the ordinal case. The proposed sample-size calculations are based on an analysis with generalized estimating equations (GEE) and inference with the Wald test. Simulation results demonstrate the merit of the proposed power calculations. Analysis of an arthritis clinical trial is used for illustration.
Description
Sample-size calculations for studies with correlated ordinal outcomes. - PubMed - NCBI
%0 Journal Article
%1 Kim:2005:Stat-Med:16149125
%A Kim, H Y
%A Williamson, J M
%A Lyles, C M
%D 2005
%J Stat Med
%K CorrelatedData SampleSize statistics
%N 19
%P 2977-2987
%R 10.1002/sim.2162
%T Sample-size calculations for studies with correlated ordinal outcomes
%U https://www.ncbi.nlm.nih.gov/pubmed/16149125
%V 24
%X Correlated ordinal response data often arise in public health studies. Sample-size (power) calculations are a crucial step in designing such studies to ensure an adequate sample to detect a significant effect. Here we extend Rochon's method of sample-size estimation with a repeated binary response to the ordinal case. The proposed sample-size calculations are based on an analysis with generalized estimating equations (GEE) and inference with the Wald test. Simulation results demonstrate the merit of the proposed power calculations. Analysis of an arthritis clinical trial is used for illustration.
@article{Kim:2005:Stat-Med:16149125,
abstract = {Correlated ordinal response data often arise in public health studies. Sample-size (power) calculations are a crucial step in designing such studies to ensure an adequate sample to detect a significant effect. Here we extend Rochon's method of sample-size estimation with a repeated binary response to the ordinal case. The proposed sample-size calculations are based on an analysis with generalized estimating equations (GEE) and inference with the Wald test. Simulation results demonstrate the merit of the proposed power calculations. Analysis of an arthritis clinical trial is used for illustration.},
added-at = {2019-11-11T23:38:49.000+0100},
author = {Kim, H Y and Williamson, J M and Lyles, C M},
biburl = {https://www.bibsonomy.org/bibtex/2b938963fb34fbb1040ca3128f6ed2c7f/jkd},
description = {Sample-size calculations for studies with correlated ordinal outcomes. - PubMed - NCBI},
doi = {10.1002/sim.2162},
interhash = {175fdd26c538a704581645fc3c4669b2},
intrahash = {b938963fb34fbb1040ca3128f6ed2c7f},
journal = {Stat Med},
keywords = {CorrelatedData SampleSize statistics},
month = oct,
number = 19,
pages = {2977-2987},
pmid = {16149125},
timestamp = {2019-11-11T23:38:49.000+0100},
title = {Sample-size calculations for studies with correlated ordinal outcomes},
url = {https://www.ncbi.nlm.nih.gov/pubmed/16149125},
volume = 24,
year = 2005
}