An extension of the mixed‐effects growth model that considers between‐person differences in the within‐subject variance and the autocorrelation
Experience sampling methods have led to a significant increase in the availability of intensive longitudinal data. Typically, this type of data is analyzed with a mixed‐effects model that allows to examine hypotheses concerning between‐person differences in the mean structure by including multiple r...
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| Vydané v: | Statistics in medicine Ročník 41; číslo 3; s. 471 - 482 |
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| Médium: | Journal Article |
| Jazyk: | English |
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England
Wiley Subscription Services, Inc
10.02.2022
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| ISSN: | 0277-6715, 1097-0258, 1097-0258 |
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| Abstract | Experience sampling methods have led to a significant increase in the availability of intensive longitudinal data. Typically, this type of data is analyzed with a mixed‐effects model that allows to examine hypotheses concerning between‐person differences in the mean structure by including multiple random effects per individual (eg, random intercept and random slopes). Here, we describe an extension of this model that—in addition to the random effects for the mean structure—also includes a random effect for the within‐subject variance and a random effect for the autocorrelation. After the description of the model, we show how its parameters can be efficiently estimated using a marginal maximum likelihood (ML) approach. We then illustrate the model using a real data example. We also present the results of a small simulation study in which we compare the ML approach with a Bayesian estimation approach. |
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| AbstractList | Experience sampling methods have led to a significant increase in the availability of intensive longitudinal data. Typically, this type of data is analyzed with a mixed‐effects model that allows to examine hypotheses concerning between‐person differences in the mean structure by including multiple random effects per individual (eg, random intercept and random slopes). Here, we describe an extension of this model that—in addition to the random effects for the mean structure—also includes a random effect for the within‐subject variance and a random effect for the autocorrelation. After the description of the model, we show how its parameters can be efficiently estimated using a marginal maximum likelihood (ML) approach. We then illustrate the model using a real data example. We also present the results of a small simulation study in which we compare the ML approach with a Bayesian estimation approach. Experience sampling methods have led to a significant increase in the availability of intensive longitudinal data. Typically, this type of data is analyzed with a mixed-effects model that allows to examine hypotheses concerning between-person differences in the mean structure by including multiple random effects per individual (eg, random intercept and random slopes). Here, we describe an extension of this model that-in addition to the random effects for the mean structure-also includes a random effect for the within-subject variance and a random effect for the autocorrelation. After the description of the model, we show how its parameters can be efficiently estimated using a marginal maximum likelihood (ML) approach. We then illustrate the model using a real data example. We also present the results of a small simulation study in which we compare the ML approach with a Bayesian estimation approach.Experience sampling methods have led to a significant increase in the availability of intensive longitudinal data. Typically, this type of data is analyzed with a mixed-effects model that allows to examine hypotheses concerning between-person differences in the mean structure by including multiple random effects per individual (eg, random intercept and random slopes). Here, we describe an extension of this model that-in addition to the random effects for the mean structure-also includes a random effect for the within-subject variance and a random effect for the autocorrelation. After the description of the model, we show how its parameters can be efficiently estimated using a marginal maximum likelihood (ML) approach. We then illustrate the model using a real data example. We also present the results of a small simulation study in which we compare the ML approach with a Bayesian estimation approach. |
| Author | Nestler, Steffen |
| Author_xml | – sequence: 1 givenname: Steffen orcidid: 0000-0001-9724-2441 surname: Nestler fullname: Nestler, Steffen email: steffen.nestler@uni-muenster.de organization: Universität Münster |
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| Title | An extension of the mixed‐effects growth model that considers between‐person differences in the within‐subject variance and the autocorrelation |
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