Performance of tests of association in misspecified generalized linear models

Published

Journal Article

We examine the effects of modelling errors, such as underfitting and overfitting, on the asymptotic power of tests of association between an explanatory variable x and an outcome in the setting of generalized linear models. The regression function for x is approximated by a polynomial or another simple function, and a chi-square statistic is used to test whether the coefficients of the approximation are simultaneously equal to zero. Adding terms to the approximation increases asymptotic power if and only if the fit of the model increases by a certain quantifiable amount. Although a high degree of freedom approximation offers robustness to the shape of the unknown regression function, a low degree of freedom approximation can yield much higher asymptotic power even when the approximation is very poor. In practice, it is useful to compute the power of competing test statistics across the range of alternatives that are plausible a priori. This approach is illustrated through an application in epidemiology. © 2006 Elsevier B.V. All rights reserved.

Full Text

Duke Authors

Cited Authors

  • O'Brien, SM; Kupper, LL; Dunson, DB

Published Date

  • 2006-09-01

Published In

Volume / Issue

  • 136 / 9

Start / End Page

  • 3090 - 3100

International Standard Serial Number (ISSN)

  • 0378-3758

Digital Object Identifier (DOI)

  • 10.1016/j.jspi.2004.12.004

Citation Source

  • Scopus