A practical approach to computing power for generalized linear models with nominal, count, or ordinal responses

Lyles, Robert H.; Lin, Hung‐Mo; Williamson, John M.

doi:10.1002/sim.2617

Cited by 57Statistical Methods in Clinical TrialsStatistical Methods in EpidemiologyStatistical Methods and Bayesian Inference

A practical approach to computing power for generalized linear models with nominal, count, or ordinal responses

Lyles, Robert H., Lin, Hung‐Mo, Williamson, John M.·2007·Statistics in Medicine

DOI 10.1002/sim.2617

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Abstract

Data analysts facing study design questions on a regular basis could derive substantial benefit from a straightforward and unified approach to power calculations for generalized linear models. Many current proposals for dealing with binary, ordinal, or count outcomes are conceptually or computationally demanding, limited in terms of accommodating covariates, and/or have not been extensively assessed for accuracy assuming moderate sample sizes. Here, we present a simple method for estimating conditional power that requires only standard software for fitting the desired generalized linear model for a non‐continuous outcome. The model is fit to an appropriate expanded data set using easily calculated weights that represent response probabilities given the assumed values of the parameters. The variance–covariance matrix resulting from this fit is then used in conjunction with an established non‐central chi square approximation to the distribution of the Wald statistic. Alternatively, the model can be re‐fit under the null hypothesis to approximate power based on the likelihood ratio statistic. We provide guidelines for constructing a representative expanded data set to allow close approximation of unconditional power based on the assumed joint distribution of the covariates. Relative to prior proposals, the approach proves particularly flexible for handling one or more continuous covariates without any need for discretizing. We illustrate the method for a variety of outcome types and covariate patterns, using simulations to demonstrate its accuracy for realistic sample sizes. Copyright © 2006 John Wiley & Sons, Ltd.

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Authors: Lyles, R. H., Lin, H., & Williamson, J. M.
Affiliations: Emory University (US)
Pennsylvania State University (US)
Centers for Disease Control and Prevention (US)
Corresponding author: Lyles, Robert H.
ORCID: Lyles, Robert H.
Lin, Hung‐Mo
Williamson, John M.
Venue: Statistics in Medicine
Publisher: Wiley
Published: 2006-06-30
Volume: 26
Issue: 7
Pages: 1632-1648
ISSN: 0277-6715 (ISSN-L)
1097-0258
DOI: 10.1002/sim.2617
OpenAlex: W1984684068
Cited by: 57
References: 26
Citations by year: 2026: 3 · 2025: 4 · 2024: 2 · 2023: 4 · 2022: 3 · 2021: 5 · 2020: 4 · 2019: 3 · 2018: 2 · 2017: 3 · 2016: 2 · 2015: 2 · 2014: 5 · 2013: 2 · 2012: 1
FWCI: 2.19
Type: journal article
Language: en
Open access: closed
Licence: onlinelibrary.wiley.com/termsAndConditions#vor (vor)
Locations: Statistics in Medicine (publishedVersion)
PubMed (publishedVersion)
Funders: National Institute of Environmental Health Sciences (R01ES012458)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
Keywords: power analysis, generalized linear models, conditional power, ordinal outcomes, count outcomes, Wald statistic, likelihood ratio statistic, continuous covariates, research design
MeSH terms: Computer Simulation, Data Interpretation, Statistical, Humans, Linear Models, Sample Size

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A practical approach to computing power for generalized linear models with nominal, count, or ordinal responses

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