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

Full 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 (c) 2006 John Wiley & Sons, Ltd.

Learn Faster Today Improve your study skills

Author information

Author/s: Lyles, Robert H (RH); Lin, Hung-Mo (HM); Williamson, John M (JM);

Affiliation: Department of Biostatistics, The Rollins School of Public Health of Emory University, 1518 Clifton Rd. N.E., Atlanta, GA 30322, USA. rlyles(-atsign-)sph.emory.edu

Grants: R01 ES012458 (Agency:NIEHS NIH HHS)

Journal and publication information

Publication Type: Journal Article; Research Support, N.I.H., Extramural

Journal: Statistics in medicine (Stat Med), published in England. (Language: eng)

Reference: 2007-Mar; vol 26 (issue 7) : pp 1632-48

Dates: Created 2007/02/19; Completed 2007/05/15; Revised 2007/12/03;

PMID: 16817148, status: MEDLINE (last retrieval date: 12/26/2008)

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

External Links for this article (including full text providers, if available):

Click Electronic Full-text Provider Links to see options for finding the electronic full text links to this article. Note there may be a subscription or fee required for access to the full text. See our FAQ for information on finding FREE full text articles.

This article may also be located in paper journal collections available in many libraries. Use the Journal and Publication Information above to find the full article.

MeSH headings (categories)

This article was linked to the MESH Headings shown below.

These are the highest related articles currently in the database:

See 100+ related articles.