The Effects of the Number of Cohorts, Degree of Overlap Among Cohorts, and Frequency of Observation on Power in Accelerated Longitudinal Designs
Abstract
With the accelerated longitudinal design data of different age cohorts are used to study individual development over a broad age span during a period of shorter duration. When planning an accelerated longitudinal study one must decide on the number of cohorts, the degree of overlap among cohorts, and the frequency of observation. This paper provides a framework to study the effects of these three design factors on the statistical power to detect a linear change. As no simple mathematical formulae for these relations exist, an example is used to illustrate how the effects of these three design factors can be evaluated. It is shown that the optimal number of cohorts, the optimal degree of overlap among cohorts, and the optimal frequency of observation depend on the total number of subjects and the total number of measurements. R code for evaluating the power of longitudinal designs is provided.
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