Power analysis of longitudinal studies with piecewise linear growth and attrition
In longitudinal research, the development of some outcome variable(s) over time (or age) is studied. Such relations are not necessarily smooth, and piecewise growth models may be used to account for differential growth rates before and after a turning point in time. Such models have been well develo...
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Published in | Behavior research methods Vol. 54; no. 6; pp. 2939 - 2948 |
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Main Author | |
Format | Journal Article |
Language | English |
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New York
Springer US
01.12.2022
Springer Nature B.V |
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Abstract | In longitudinal research, the development of some outcome variable(s) over time (or age) is studied. Such relations are not necessarily smooth, and piecewise growth models may be used to account for differential growth rates before and after a turning point in time. Such models have been well developed, but the literature on power analysis for these models is scarce. This study investigates the power needed to detect differential growth for linear–linear piecewise growth models in further detail while taking into account the possibility of attrition. Attrition is modeled using the Weibull survival function, which allows for increasing, decreasing or constant attrition across time. Furthermore, this work takes into account the realistic situation where subjects do not necessarily have the same turning point. A multilevel mixed model is used to model the relation between time and outcome, and to derive the relation between sample size and power. The required sample size to achieve a desired power is smallest when the turning points are located halfway through the study and when all subjects have the same turning point. Attrition has a diminishing effect on power, especially when the probability of attrition is largest at the beginning of the study. An example on alcohol use during middle and high school shows how to perform a power analysis. The methodology has been implemented in a Shiny app to facilitate power calculations for future studies. |
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AbstractList | In longitudinal research, the development of some outcome variable(s) over time (or age) is studied. Such relations are not necessarily smooth, and piecewise growth models may be used to account for differential growth rates before and after a turning point in time. Such models have been well developed, but the literature on power analysis for these models is scarce. This study investigates the power needed to detect differential growth for linear–linear piecewise growth models in further detail while taking into account the possibility of attrition. Attrition is modeled using the Weibull survival function, which allows for increasing, decreasing or constant attrition across time. Furthermore, this work takes into account the realistic situation where subjects do not necessarily have the same turning point. A multilevel mixed model is used to model the relation between time and outcome, and to derive the relation between sample size and power. The required sample size to achieve a desired power is smallest when the turning points are located halfway through the study and when all subjects have the same turning point. Attrition has a diminishing effect on power, especially when the probability of attrition is largest at the beginning of the study. An example on alcohol use during middle and high school shows how to perform a power analysis. The methodology has been implemented in a Shiny app to facilitate power calculations for future studies. In longitudinal research, the development of some outcome variable(s) over time (or age) is studied. Such relations are not necessarily smooth, and piecewise growth models may be used to account for differential growth rates before and after a turning point in time. Such models have been well developed, but the literature on power analysis for these models is scarce. This study investigates the power needed to detect differential growth for linear-linear piecewise growth models in further detail while taking into account the possibility of attrition. Attrition is modeled using the Weibull survival function, which allows for increasing, decreasing or constant attrition across time. Furthermore, this work takes into account the realistic situation where subjects do not necessarily have the same turning point. A multilevel mixed model is used to model the relation between time and outcome, and to derive the relation between sample size and power. The required sample size to achieve a desired power is smallest when the turning points are located halfway through the study and when all subjects have the same turning point. Attrition has a diminishing effect on power, especially when the probability of attrition is largest at the beginning of the study. An example on alcohol use during middle and high school shows how to perform a power analysis. The methodology has been implemented in a Shiny app to facilitate power calculations for future studies.In longitudinal research, the development of some outcome variable(s) over time (or age) is studied. Such relations are not necessarily smooth, and piecewise growth models may be used to account for differential growth rates before and after a turning point in time. Such models have been well developed, but the literature on power analysis for these models is scarce. This study investigates the power needed to detect differential growth for linear-linear piecewise growth models in further detail while taking into account the possibility of attrition. Attrition is modeled using the Weibull survival function, which allows for increasing, decreasing or constant attrition across time. Furthermore, this work takes into account the realistic situation where subjects do not necessarily have the same turning point. A multilevel mixed model is used to model the relation between time and outcome, and to derive the relation between sample size and power. The required sample size to achieve a desired power is smallest when the turning points are located halfway through the study and when all subjects have the same turning point. Attrition has a diminishing effect on power, especially when the probability of attrition is largest at the beginning of the study. An example on alcohol use during middle and high school shows how to perform a power analysis. The methodology has been implemented in a Shiny app to facilitate power calculations for future studies. |
Author | Moerbeek, Mirjam |
Author_xml | – sequence: 1 givenname: Mirjam orcidid: 0000-0001-5537-1237 surname: Moerbeek fullname: Moerbeek, Mirjam email: m.moerbeek@uu.nl organization: Department of Methodology and Statistics, Utrecht University |
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Cites_doi | 10.1207/S15328007SEM1003_3 10.1177/1471082X13504721 10.1146/annurev.psych.58.110405.085520 10.1080/10705511.2014.935678 10.1086/686304 10.3102/10769986024001070 10.1080/10705510802154349 10.3102/1076998607302630 10.1080/10503300903376320 10.1023/B:JOBA.0000045342.32739.2f 10.1002/sim.8195 10.15288/jsa.2001.62.199 10.3758/BRM.41.4.1083 10.1016/s0197-2456(02)00205-2 10.3758/s13428-020-01420-5 10.1080/10705511.2018.1424548 10.1016/j.jclinepi.2010.12.003 10.1037/1082-989X.6.4.387 10.1111/1467-9876.00158 10.1027/1614-2241/a000019 10.1080/02664763.2015.1014884 10.1177/0165025415580806 |
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Keywords | Piecewise growth model Multiphase Shiny app Power Multilevel model |
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Snippet | In longitudinal research, the development of some outcome variable(s) over time (or age) is studied. Such relations are not necessarily smooth, and piecewise... |
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SubjectTerms | Behavioral Science and Psychology Cognitive Psychology Growth models Growth rate Humans Longitudinal Studies Psychology Schools |
Title | Power analysis of longitudinal studies with piecewise linear growth and attrition |
URI | https://link.springer.com/article/10.3758/s13428-022-01791-x https://www.ncbi.nlm.nih.gov/pubmed/35132584 https://www.proquest.com/docview/2747917494 https://www.proquest.com/docview/2626892124 https://pubmed.ncbi.nlm.nih.gov/PMC9729151 |
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