Partial Likelihood Analysis of Within-Unit Variances in Repeated Measurement Experiments

Chinchilli, Vernon M.; Jd, Esinhart; Wg, Miller

doi:10.2307/2533326

Cited by 12 publications

(15 citation statements)

References 12 publications

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“…Hence, the magnitude of 6 measures the dispersion of the oQ around the o-20. Note that our parameterization of the distribution of 1/of is slightly different from that of Chinchilli et al (1995). We assume that the means oQ0 are related to a vector of subject-level covariates wi through log(oQo) ,w To,…”

Section: The Linear Mixed Model With Heterogeneousmentioning

confidence: 99%

Linear Mixed Models with Heterogeneous within-Cluster Variances

Lin¹,

Raz²,

Harlow³

1997

Biometrics

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This paper describes an extension of linear mixed models to allow for heterogeneous within-cluster variances in the analysis of clustered data. Unbiased estimating equations based on quasilikelihood/pseudolikelihood and method of moments are introduced and are shown to give consistent estimators of the regression coefficients, variance components, and heterogeneity parameter under regularity conditions. Cluster-specific random effects and variances are predicted by the posterior modes. The method is illustrated through an analysis of menstrual diary data and its properties are evaluated in a simulation study.

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Section: The Linear Mixed Model With Heterogeneousmentioning

confidence: 99%

Linear Mixed Models with Heterogeneous within-Cluster Variances

Lin¹,

Raz²,

Harlow³

1997

Biometrics

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“…The technical report by Cleveland, Denby, and Liu (2002) provides a detailed description of this general class of models and summarizes much of the relevant work. These models have been developed using both Bayesian (Lindley, 1971;Leonard, 1975;Myles et al, 2003) and frequentist approaches (James et al, 1994;Chinchilli, Esinhart, and Miller, 1995;Lin et al, 1997). Most of these authors take the random scale distribution to be square root inverse gamma, though some consider the log-normal distribution.…”

Section: Introductionmentioning

confidence: 99%

An Application of a Mixed‐Effects Location Scale Model for Analysis of Ecological Momentary Assessment (EMA) Data

2008

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SummaryFor longitudinal data, mixed models include random subject effects to indicate how subjects influence their responses over repeated assessments. The error variance and the variance of the random effects are usually considered to be homogeneous. These variance terms characterize the within-subjects (i.e., error variance) and between-subjects (i.e., random-effects variance) variation in the data. In studies using ecological momentary assessment (EMA), up to 30 or 40 observations are often obtained for each subject, and interest frequently centers around changes in the variances, both within and between subjects. In this article, we focus on an adolescent smoking study using EMA where interest is on characterizing changes in mood variation. We describe how covariates can influence the mood variances, and also extend the standard mixed model by adding a subject-level random effect to the within-subject variance specification. This permits subjects to have influence on the mean, or location, and variability, or (square of the) scale, of their mood responses. Additionally, we allow the location and scale random effects to be correlated. These mixed-effects location scale models have useful applications in many research areas where interest centers on the joint modeling of the mean and variance structure.

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“…Here, we will consider both formulations replicated k times over each subject with k greater than one. Chinchilli et al (1995) proposed a partial likelihood analysis in which the full log-likelihood was separated into a conditional log-likelihood depending on within-subject variances and individual means plus a marginal log-likelihood based on within-subject variances,…”

Section: Maximum Likelihood Estimatesmentioning

confidence: 99%

“…Assuming within-subject deviations are normally distributed, both within-subject variances follow a chi-square distribution with nðk À 1Þ degrees of freedom (Chinchilli et al, 1995). Therefore the within-subject variance ratio follows an F-distribution with nðk À 1Þ and nðk À 1Þ degrees of freedom.…”

Section: Ratio Of Within-subject Variancesmentioning

confidence: 99%

The Structural Error-in-Equation Model to Evaluate Individual Bioequivalence

Jover

2005

Biom. J.

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Individual bioequivalence is assessed using an extension of the classical structural equation model, known as the error-in-equation model. This procedure estimates the relationship between individual means, as well as the variance-covariance parameters, of the bioavailabilities measurement model, by considering individual means related through a straight line with a random term, whereas the classical structural equation considers a deterministic linear relationship. We discuss the implications of this approach in terms of the bioavailabilities measurement model and how to test the overall hypothesis of individual bioequivalence. Both models are compared in a simulation study and a case example is presented.

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Partial Likelihood Analysis of Within-Unit Variances in Repeated Measurement Experiments

Cited by 12 publications

References 12 publications

Linear Mixed Models with Heterogeneous within-Cluster Variances

Linear Mixed Models with Heterogeneous within-Cluster Variances

An Application of a Mixed‐Effects Location Scale Model for Analysis of Ecological Momentary Assessment (EMA) Data

The Structural Error-in-Equation Model to Evaluate Individual Bioequivalence

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