A systematic approach to designing statistically powerful heteroscedastic 2 × 2 factorial studies while minimizing financial costs

• Main Authors: Show-Li Jan, Gwowen Shieh
• Format: Article
• Language: English
• Published: BMC 2016-08-01
• Series: BMC Medical Research Methodology
• Subjects: Budget; Factorial design; Heteroscedasticity; Interaction; Power; Sample size
• Online Access: http://link.springer.com/article/10.1186/s12874-016-0214-3

Abstract Background The 2 × 2 factorial design is widely used for assessing the existence of interaction and the extent of generalizability of two factors where each factor had only two levels. Accordingly, research problems associated with the main effects and interaction effects can be analyzed with the selected linear contrasts. Methods To correct for the potential heterogeneity of variance structure, the Welch-Satterthwaite test is commonly used as an alternative to the t test for detecting the substantive significance of a linear combination of mean effects. This study concerns the optimal allocation of group sizes for the Welch-Satterthwaite test in order to minimize the total cost while maintaining adequate power. The existing method suggests that the optimal ratio of sample sizes is proportional to the ratio of the population standard deviations divided by the square root of the ratio of the unit sampling costs. Instead, a systematic approach using optimization technique and screening search is presented to find the optimal solution. Results Numerical assessments revealed that the current allocation scheme generally does not give the optimal solution. Alternatively, the suggested approaches to power and sample size calculations give accurate and superior results under various treatment and cost configurations. Conclusions The proposed approach improves upon the current method in both its methodological soundness and overall performance. Supplementary algorithms are also developed to aid the usefulness and implementation of the recommended technique in planning 2 × 2 factorial designs.