Longitudinal Assessment Design and Statistical Power for Detecting an Intervention Impact

Longitudinal Assessment Design and Statistical Power for Detecting an Intervention Impact In evaluating randomized control trials (RCTs), statistical power analyses are necessary to choose a sample size which strikes the balance between an insufficient and an excessive design, with the latter leading to misspent resources. With the growing popularity of using longitudinal data to evaluate RCTs, statistical power calculations have become more complex. Specifically, with repeated measures, the number and frequency of measurements per person additionally influence statistical power by determining the precision with which intra-individual change can be measured as well as the reliability with which inter-individual differences in change can be assessed. The application of growth mixture models has shown that the impact of universal interventions is often concentrated among a small group of individuals at the highest level of risk. General sample size calculations were consequently not sufficient to determine whether statistical power is adequate to detect the desired effect. Currently, little guidance exists to recommend a sufficient assessment design to evaluating intervention impact. To this end, Monte Carlo simulations are conducted to assess the statistical power and precision when manipulating study duration and assessment frequency. Estimates were extracted from a published evaluation of the proximal of the Good Behavior Game (GBG) on the developmental course of aggressive behavior. Results indicated that the number of time points and the frequency of assessments influence statistical power and precision. Recommendations for the assessment design of longitudinal studies are discussed. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Prevention Science Springer Journals

Longitudinal Assessment Design and Statistical Power for Detecting an Intervention Impact

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Publisher
Springer US
Copyright
Copyright © 2016 by Society for Prevention Research
Subject
Medicine & Public Health; Public Health; Health Psychology; Child and School Psychology
ISSN
1389-4986
eISSN
1573-6695
D.O.I.
10.1007/s11121-016-0646-3
Publisher site
See Article on Publisher Site

Abstract

In evaluating randomized control trials (RCTs), statistical power analyses are necessary to choose a sample size which strikes the balance between an insufficient and an excessive design, with the latter leading to misspent resources. With the growing popularity of using longitudinal data to evaluate RCTs, statistical power calculations have become more complex. Specifically, with repeated measures, the number and frequency of measurements per person additionally influence statistical power by determining the precision with which intra-individual change can be measured as well as the reliability with which inter-individual differences in change can be assessed. The application of growth mixture models has shown that the impact of universal interventions is often concentrated among a small group of individuals at the highest level of risk. General sample size calculations were consequently not sufficient to determine whether statistical power is adequate to detect the desired effect. Currently, little guidance exists to recommend a sufficient assessment design to evaluating intervention impact. To this end, Monte Carlo simulations are conducted to assess the statistical power and precision when manipulating study duration and assessment frequency. Estimates were extracted from a published evaluation of the proximal of the Good Behavior Game (GBG) on the developmental course of aggressive behavior. Results indicated that the number of time points and the frequency of assessments influence statistical power and precision. Recommendations for the assessment design of longitudinal studies are discussed.

Journal

Prevention ScienceSpringer Journals

Published: Apr 22, 2016

References

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