Selecting a sample size for studies with repeated measures
By Yi Guo, H. Logan, D. Glueck, K. Muller. BMC Medical Research Methodology, 2013
364 citations recorded by the source corpus.
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.
Subfields: Management Science and Operations Research, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/fa971b4d413a0e109cc51d723b3eecf6eb3f41ac
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In Management Science and Operations Research
Citations in the corpus
- Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker (2016, background): “This is crucial for any measure to be used in longitudinal studies, and has an important bearing on the sample sizes required to detect significant effects on repeated measures (Guo et al., 2013).”