[Prediction of Future Observations in Growth Curve Models]: Comment

Laird, Nan M.; Lange, Nick

doi:10.1214/ss/1177013121

Cited by 5 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The estimator (9) might lead to a non-positive-definite estimate of D. Laird and Lange (1987) and Rousseeuw and Molenberghs (1993) proposed various methods to find the nearest positive-definite matrix, the latter in the context of correlation matrices. The adjustments' purpose is to find the closest positive-definite matrix of the one that is found at first.…”

Section: Adjustment For Non-positive-definite Dmentioning

confidence: 99%

A closed-form estimator for meta-analysis and surrogate markers evaluation

Flórez

Molenberghs

Verbeke

et al. 2018

Journal of Biopharmaceutical Statistics

View full text Add to dashboard Cite

Estimating complex linear mixed models using an iterative full maximum likelihood estimator can be cumbersome in some cases. With small and unbalanced datasets, convergence problems are common. Also, for large datasets, iterative procedures can be computationally prohibitive. To overcome these computational issues, an unbiased two-stage closed-form estimator for the multivariate linear mixed model is proposed. It is rooted in pseudo-likelihood-based split-sample methodology, and useful, for example, when evaluating normally distributed endpoints in a meta-analytic context. However, applications go well beyond this framework. Its statistical and computational performance is assessed via simulation. The method is applied to a study in schizophrenia.

show abstract

Section: Adjustment For Non-positive-definite Dmentioning

confidence: 99%

A closed-form estimator for meta-analysis and surrogate markers evaluation

Flórez

Molenberghs

Verbeke

et al. 2018

Journal of Biopharmaceutical Statistics

View full text Add to dashboard Cite

show abstract

“…Ra0 9 discusses prediction in growth curve models for several illustrative data sets. Two discussants 16,17 present parallel plots of the raw data. Other examples of parallel plots exist in the literature, but they do not seem to have received much study.…”

Section: Parallel Plotsmentioning

confidence: 99%

Residual plots for repeated measures

Weiss¹,

Lázaro²

1992

Statistics in Medicine

View full text Add to dashboard Cite

Multivariate data are difficult to analyse partly because of difficulty in looking at the data. Repeated measures data have a special structure that makes the parallel plot particularly effective for viewing the data. In this paper we use parallel plots to display not just raw data but also residuals from standard models fit to repeated measures data. The plots are useful for determining how well a particular model fits the data, identifying outlying observations and suggesting terms missing from the linear predictor.

show abstract

“…In this paper we utilize population information in nonparametric curve estimation by applying EB techniques to the fitted values. This is in contrast to the use of EB estimation to improve parameter estimates (rather than fitted values) for parametric growth curve models, (Berkey, 1982;Hui and Berger, 1982;Laird and Lange, 1987;Rao, 1987;Strenio, Weisberg and Bryk, 1983) .…”

Section: Introductionmentioning

confidence: 99%