A Random-Effects Model for Multiple Characteristics with Possibly Missing Data

Shah, Amrik; Laird, Nan M.; Schoenfeld, David

doi:10.1080/01621459.1997.10474030

Cited by 138 publications

(123 citation statements)

References 10 publications

Supporting

Mentioning

123

Contrasting

Order By: Relevance

“…Shah et al [2] discussed models for non-commensurate outcomes in which the measurement errors for the same source at any two occasions are uncorrelated, but measurement errors between two different sources at the same occasion are correlated. In addition to correlated random measurement error, a random subject effect was included.…”

Section: Discussionmentioning

confidence: 99%

“…On the other hand, the outcomes can be distinct but yet measured on the same scale. An example of the latter occurs in studies of AIDS, where CD4 and CD8 cell counts may be obtained longitudinally resulting in repeated measures of a bivariate outcome [2]. In this case, the two outcomes are both measured in terms of counts but are distinct markers of immune function (i.e., they are not commensurate).…”

Section: Introductionmentioning

confidence: 99%

“…Thus, each source provides a measure of the same underlying variable (family functioning) at each measurement occasion, and the analytic goal is to relate changes in family functioning to the intervention assignment. Much of the previous work on modelling multivariate longitudinal data, however, has focused on outcomes that are not commensurate (e.g., [1] and [2]). While there is some related work on longitudinal models for multiple source data, it has tended to make quite strong assumptions about the structure of the covariance among the outcomes (e.g., [3] and [5]).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Regression models for the analysis of longitudinal Gaussian data from multiple sources

O’Brien

Fitzmaurice

2005

Statistics in Medicine

View full text Add to dashboard Cite

We present a regression model for the joint analysis of longitudinal multiple source Gaussian data. Longitudinal multiple source data arise when repeated measurements are taken from two or more sources, and each source provides a measure of the same underlying variable and on the same scale. This type of data generally produces a relatively large number of observations per subject; thus estimation of an unstructured covariance matrix often may not be possible. We consider two methods by which parsimonious models for the covariance can be obtained for longitudinal multiple source data. The methods are illustrated with an example of multiple informant data arising from a longitudinal interventional trial in psychiatry.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Regression models for the analysis of longitudinal Gaussian data from multiple sources

O’Brien

Fitzmaurice

2005

Statistics in Medicine

View full text Add to dashboard Cite

show abstract

“…When assessing the approximated log-likelihood functions, we find that all approximation methods produce relative biases in log-likelihoods within ±0.12 (the range is not quite large). Because the simulated datasets are generated from a linear scenario, i.e., bivariate LMM specified in Equation (33), the pseudo-data model given in Equation (8) certainly satisfies the MLMM [1] framework. Therefore, the ML estimates of model parameters, as well as the maximized log-likelihood value obtained by the pseudo-ECM algorithm are exactly the same as those obtained by fitting the MLMM using the EM-based algorithm.…”

Section: Bivariate Linear Casementioning

confidence: 99%

“…The methodology of multivariate linear mixed-effects models (MLMM) [1] and multivariate nonlinear mixed-effects models (MNLMM) [2] has been developed for related work. A comprehensive study of the MLMM along with its applications can be found in [3][4][5][6][7], among others.…”

Section: Introductionmentioning

confidence: 99%

Approximate Methods for Maximum Likelihood Estimation of Multivariate Nonlinear Mixed-Effects Models

Wang

2015

Entropy

View full text Add to dashboard Cite

Multivariate nonlinear mixed-effects models (MNLMM) have received increasing use due to their flexibility for analyzing multi-outcome longitudinal data following possibly nonlinear profiles. This paper presents and compares five different iterative algorithms for maximum likelihood estimation of the MNLMM. These algorithmic schemes include the penalized nonlinear least squares coupled to the multivariate linear mixed-effects (PNLS-MLME) procedure, Laplacian approximation, the pseudo-data expectation conditional maximization (ECM) algorithm, the Monte Carlo EM algorithm and the importance sampling EM algorithm. When fitting the MNLMM, it is rather difficult to exactly evaluate the observed log-likelihood function in a closed-form expression, because it involves complicated multiple integrals. To address this issue, the corresponding approximations of the observed log-likelihood function under the five algorithms are presented. An expected information matrix of parameters is also provided to calculate the standard errors of model parameters. A comparison of computational performances is investigated through simulation and a real data example from an AIDS clinical study.

show abstract

Predicting Preclinical Disease by Using The Mixed‐Effects Regression Model

Shan

Brant

2010

Encyclopedia of Statistical Sciences

View full text Add to dashboard Cite

Most methods of disease prediction are generally restricted to logistic or Cox proportional hazards model regression using only baseline values of the risk factor, and where the individuals age and sex account for most of the predictive capacity of the method. This chapter presents a method of risk prediction appropriate for repeated measures or time‐dependent risk factors from longitudinal studies. The method is based on posterior probabilities calculated from mixed‐effects regression models to model intra‐ and inter‐individual differences in the serial risk factor measurements, to predict a disease‐related outcome or other future event according to age changes within gender groups. While aging is inevitable and the occurrence of disease increases with age, disease and aging are not synonymous. Disease is a selective event that occurs in only some members of the population; others maintain a greater degree of physiological function as they age and remain relatively disease free. Thus, longitudinal data that considers each individual as their own control and the use of an appropriate method of prediction can play an important role in promoting a healthy human life throughout the aging process.

show abstract

A Random-Effects Model for Multiple Characteristics with Possibly Missing Data

Cited by 138 publications

References 10 publications

Regression models for the analysis of longitudinal Gaussian data from multiple sources

Regression models for the analysis of longitudinal Gaussian data from multiple sources

Approximate Methods for Maximum Likelihood Estimation of Multivariate Nonlinear Mixed-Effects Models

Predicting Preclinical Disease by Using The Mixed‐Effects Regression Model

Contact Info

Product

Resources

About