An overview of variance inflation factors for sample-size calculation

Area of Science:

• Biostatistics
• Research Methodology
• Statistical Modeling

Background:

• Power and sample-size calculations are crucial for study design, often relying on software programs.
• Commercial software may not cover all factors influencing statistical power, such as design effects.
• Design effects can inflate the variance of test statistics, impacting study power.

Purpose of the Study:

• To quantify the impact of various design effects on statistical power.
• To provide researchers with methods to adjust power and sample size calculations.
• To extend the applicability of standard power calculations to complex study designs.

Main Methods:

• Review and quantification of design effects for multiple statistical and research designs.
• Analysis of variance inflation factors for factorial, crossover, and cluster randomization designs.
• Inclusion of methods for unequal sample sizes, multiarm studies, logistic and Cox regression, and linear mixed models, including missing data.

Main Results:

• Design effects were quantified for a wide range of complex research scenarios.
• Formulas and methods were presented to adjust power and sample size based on quantified design effects.
• The study provides a framework to apply these adjustments to standard two-group parallel designs.

Conclusions:

• Researchers can improve study design by incorporating quantified design effects into power and sample-size calculations.
• These adjustments allow for more accurate planning, especially in studies with complex designs or missing data.
• The findings empower researchers to move beyond standard software limitations for robust study planning.