title: An overview of variance inflation factors for sample-size calculation.
abstract: 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.
authors.affiliation: Department of Veterans Affairs, Palo Alto Health Care System (151-K), Palo Alto, CA 94304, USA.
authors.forename: F Y
authors.initials: FY
authors.lastname: Hsieh
authors.forename: Philip W
authors.initials: PW
authors.lastname: Lavori
authors.forename: Harvey J
authors.initials: HJ
authors.lastname: Cohen
authors.forename: John R
authors.initials: JR
authors.lastname: Feussner
mesh_terms: D000704:Analysis of Variance; D003627:Data Interpretation, Statistical; D006302:Health Services Research / Q000379:methods / Q000706:statistics & numerical data; D006801:Humans; D012107:Research Design; D018401:Sample Size; D012984:Software; D014481:United States