Internal Pilot Design for Balanced Repeated Measures

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  • Author: Xinrui Zhang, Keith Muller, Maureen Goodenow and Yueh-Yun Chi
  • Date: 08 January 2019
  • Copyright: Image copyright of Patrick Rhodes

Repeated measures are common in clinical trials and epidemiological studies. Designing studies with repeated measures requires reasonably accurate specifications of the variances and correlations to select an appropriate sample size. Underspecifying the variances leads to a sample size that is inadequate to detect a meaningful scientific difference, while overspecifying the variances results in an unnecessarily large sample size. Both lead to wasting resources and placing study participants in unwarranted risk. An internal pilot design allows sample size recalculation based on estimates of the nuisance parameters in the covariance matrix. In a paper published in Statistics in Medicine, the authors provide the theoretical results that account for the stochastic nature of the final sample size in a common class of linear mixed models.

The paper is available via the link below and the authors explain their findings in further detail below:

Internal pilot design for balanced repeated measures

Xinrui Zhang, Keith Muller, Maureen Goodenow and Yueh-Yun Chi

Statistics in Medicine, Volume 37, Issue 3, 10 February 2018, pages 375-389

DOI: https://doi.org/10.1002/sim.7524

thumbnail image: Internal Pilot Design for Balanced Repeated Measures

In designing a clinical trial or an epidemiological study, selecting an adequate sample size is essential to ensure that the study has enough statistical power to detect a difference of clinical and practical importance. One challenge for studies with outcomes measured repeatedly lies in specifying the patterns of variations across the repeated measurements. Under-specification of the variations may lead to a sample size that is inadequate to address the underlying scientific question, while over-specification may result in an unnecessarily large sample size. Either kind of mis-specification can waste resources and place study participants at unwarranted risk. An internal pilot design uses data collected in the first (internal pilot) stage of a study to estimate the variances and correlations. This paper includes the theoretical results that account for the special statistical properties of the process. The methods are illustrated by selecting a sample size for a study evaluating the impact of early antiretroviral therapy on the levels of inflammatory biomarkers for youth living with HIV. To detect biologically meaningful changes in the level of soluble CD14 and CD163 over time, a sample size of 112 is needed when 75% of the participants are allocated to receive early therapy, and the remaining 25% are assigned to receive standard of care.

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