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

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

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

Robert H Lyles,Hung-Mo Lin,John M Williamson,

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.

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

RH Lyles,HM Lin,JM Williamson,

For similar investigative techniques: epidemiologic methods: epidemiologic research design: sample size research abstracts see: investigative techniques: epidemiologic methods: epidemiologic research design: sample size research

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

PUBLICATION TYPE: Research Support, N.I.H., Extr

Journal: Statistics in medicine

VOLUME: 26

Page Numbers: 1632-48

Journal Abbreviation:

ISSN: 0277-6715

DAY: 30

MONTH: Mar

YEAR: 2007

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

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LANGUAGE: eng

NlmUniqueID: 8215016

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

KEYWORDS: Sample Size

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

AFFILIATION: Department of Biostatistics, The Rollins School of Public Health of Emory University, 1518 Clifton Rd. N.E., Atlanta, GA 30322, USA. rlyles@sph.emory.edu

Country: England

AGENCY: United States NIEHS

GRANT: R01 ES012458

ACRONYM: ES

MEDLINETA: Stat Med

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