An overview of variance inflation factors for sample-size calculation.

Journal Article (Review)

For power and sample-size calculations, most practicing researchers rely on power and sample-size software programs to design their studies. There are many factors that affect the statistical power that, in many situations, go beyond the coverage of commercial software programs. Factors commonly known as design effects influence statistical power by inflating the variance of the test statistics. The authors quantify how these factors affect the variances so that researchers can adjust the statistical power or sample size accordingly. The authors review design effects for factorial design, crossover design, cluster randomization, unequal sample-size design, multiarm design, logistic regression, Cox regression, and the linear mixed model, as well as missing data in various designs. To design a study, researchers can apply these design effects, also known as variance inflation factors to adjust the power or sample size calculated from a two-group parallel design using standard formulas and software.

Full Text

Duke Authors

Cited Authors

  • Hsieh, FY; Lavori, PW; Cohen, HJ; Feussner, JR

Published Date

  • September 2003

Published In

Volume / Issue

  • 26 / 3

Start / End Page

  • 239 - 257

PubMed ID

  • 12971199

International Standard Serial Number (ISSN)

  • 0163-2787

Digital Object Identifier (DOI)

  • 10.1177/0163278703255230

Language

  • eng

Conference Location

  • United States