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. 2013 Jul 31;13:100.
doi: 10.1186/1471-2288-13-100.

Selecting a Sample Size for Studies With Repeated Measures

Free PMC article

Selecting a Sample Size for Studies With Repeated Measures

Yi Guo et al. BMC Med Res Methodol. .
Free PMC article

Abstract

Many researchers favor repeated measures designs because they allow the detection of within-person change over time and typically have higher statistical power than cross-sectional designs. However, the plethora of inputs needed for repeated measures designs can make sample size selection, a critical step in designing a successful study, difficult. Using a dental pain study as a driving example, we provide guidance for selecting an appropriate sample size for testing a time by treatment interaction for studies with repeated measures. We describe how to (1) gather the required inputs for the sample size calculation, (2) choose appropriate software to perform the calculation, and (3) address practical considerations such as missing data, multiple aims, and continuous covariates.

Figures

Figure 1
Figure 1
Hypothetical trends of pain memory.
Figure 2
Figure 2
The hypotheses page in GLIMMPSE.
Figure 3
Figure 3
Power curves for the dental pain study.

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References

    1. Muller KE, Lavange LM, Ramey SL, Ramey CT. Power Calculations for General Linear Multivariate Models Including Repeated Measures Applications. J Am Stat Assoc. 1992;87(420):1209–1226. doi: 10.1080/01621459.1992.10476281. - DOI - PMC - PubMed
    1. Muller K, Stewart P. Linear model theory: Univariate, multivariate and mixed models. New York, NY: Wiley; 2006. Sample size for linear mixed models; pp. 385–386.
    1. Gurka MJ, Edwards LJ, Muller KE. Avoiding bias in mixed model inference for fixed effects. Stat Med. 2011;30(22):2696–2707. doi: 10.1002/sim.4293. - DOI - PMC - PubMed
    1. Cheng J, Edwards LJ, Maldonado-Molina MM, Komro KA, Muller KE. Real longitudinal data analysis for real people: Building a good enough mixed model. Stat Med. 2010;29(4):504–520. - PMC - PubMed
    1. Muller KE, Edwards LJ, Simpson SL, Taylor DJ. Statistical tests with accurate size and power for balanced linear mixed models. Stat Med. 2007;26(19):3639–3660. doi: 10.1002/sim.2827. - DOI - PubMed

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