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Likelihood-based analysis of longitudinal data from outcome-related sampling designs
2013
Biometrics
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In this paper we develop two likelihood-based approaches for fitting generalized linear mixed models (GLMMs) to longitudinal data from a wide variety of outcome-related sampling designs. ...
The second approach is an adaptation of standard conditional likelihood methods and is limited to random intercept models with a canonical link. ...
Paul Rathouz of the University of Wisconsin for sharing the Attention Deficit Hyperactivity Disorder study data and the software they developed to fit their methods. ...
doi:10.1111/biom.12108
pmid:24571396
pmcid:PMC3954410
fatcat:tavqgn43tjcellu3xwhoq5tgp4
Modern statistical techniques for the analysis of longitudinal data in biomedical research
2000
Pediatric Pulmonology
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Longitudinal study designs in biomedical research are motivated by the need or desire of a researcher to assess the change over time of an outcome and what risk factors may be associated with the outcome ...
data: the general linear mixed model, and generalized estimating equations. ...
Figure 4 is a plot of the linear regression line resulting from a cross-sectional linear regression analysis for the 23 female and 24 male patients. ...
doi:10.1002/1099-0496(200010)30:4<330::aid-ppul10>3.0.co;2-d
pmid:11015135
fatcat:ls52tejxdzd4pau2d22n4wwelq
Comparison of Population-Averaged and Subject-Specific Approaches for Analyzing Repeated Binary Outcomes
1998
American Journal of Epidemiology
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Am J Epidemiol 1998; 147:694-703. generalized estimating equations; longitudinal studies; random-effects regression models; repeated measurement ...
In particular, the authors compare results from stratified analysis, standard logistic models, conditional logistic models, the GEE models, and random-effects models by analyzing a binary outcome from ...
Dick Campbell and Paul Levy for many helpful suggestions. They also thank two anonymous referees for helpful comments. ...
doi:10.1093/oxfordjournals.aje.a009511
pmid:9554609
fatcat:cfowt5fcjrga7muiat2q4ofc2m
Modelling correlated data: Multilevel models and generalized estimating equations and their use with data from research in developmental disabilities
2018
Research in Developmental Disabilities
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Studies on sample size seem to suggest that Level 1 coefficients are robust to small samples/clusters, with any higher-level coefficients less so. ...
The paper discusses some of the core features of MLMs and GEEs for researchers who are considering how to analyse their longitudinal or clustered data. ...
If this is a simple linear regression with an intercept, for each individual we have estimated two parameters: (i) an intercept and (ii) a slope for time. ...
doi:10.1016/j.ridd.2018.04.010
pmid:29786528
fatcat:225vkqxogfd5hb5wqhv4b6i4iq
Modelling subject-specific childhood growth using linear mixed-effect models with cubic regression splines
2016
Emerging Themes in Epidemiology
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We start with standard cubic splines regression models and build up to a model that includes subject-specific random intercepts and slopes and residual autocorrelation. ...
We show that cubic regression splines are superior to linear regression splines for the case of a small number of knots in both estimation and prediction with the full linear mixed effect model (AIC 19,352 ...
We are grateful to the community of Pampas de San Juan de Miraflores for their long-term collaboration.
Competing interests The authors declare that they have no competing interests. ...
doi:10.1186/s12982-015-0038-3
pmid:26752996
pmcid:PMC4705630
fatcat:goh2so2bqjaypidzrm367uejkm
Analytic Considerations for Repeated Measures of eGFR in Cohort Studies of CKD
2017
American Society of Nephrology. Clinical Journal
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More specifically, we model longitudinal kidney outcomes over annual clinical visits and assess the association with both baseline and longitudinal risk factors. ...
We provide a general overview of the characteristics of data collected in cohort studies and compare appropriate statistical methods for the analysis of longitudinal exposures and outcomes. ...
Acknowledgments Funding for the CRIC Study was obtained under a cooperative agreement from National Institute of Diabetes ...
doi:10.2215/cjn.11311116
pmid:28751576
pmcid:PMC5544518
fatcat:qonwyswyvjaijiw7dbuphwi3ky
Multivariate Longitudinal Modeling of Cognitive Aging
2012
GeroPsych: the journal of gerontopsychology and geriatric psychiatry
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We found a significant intercept-intercept and slope-slope association between processing speed and visuospatial ability. ...
Random and fixed effects for visuospatial ability are reduced when we include structural parameters (directional growth curve model) providing information about changes in visuospatial abilities after ...
AG08861), The Swedish Council for Working Life and Social Research, The Adlerbertska Foundation, The Hjalmar Svensson Foundation, The Knut and Alice Wallenberg Foundation, The Wenner-Gren Foundations, ...
doi:10.1024/1662-9647/a000051
pmid:23589712
pmcid:PMC3625423
fatcat:y6xpexs5wjec3m3up67ex7ds6y
Correction of Retransformation Bias in Nonlinear Predictions on Longitudinal Data with Generalized Linear Mixed Models
2015
Journal of Biometrics & Biostatistics
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Generalized linear mixed models are generally applied to account for potential lack of independence inherent in longitudinally data. ...
This study attempts to go beyond existing work by developing a retransformation method deriving statistically robust longitudinal trajectory of nonlinear predictions. ...
With specification of the subject-specific random effects, covariates' regression coefficients in generalized linear mixed models do not necessarily describe changes in the mean response in the study population ...
doi:10.4172/2155-6180.1000235
fatcat:6u4ihe7govcu5hgxw5gxlqbkdy
Advances in Analysis of Longitudinal Data
2010
Annual Review of Clinical Psychology
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In this review, we explore recent developments in the area of linear and nonlinear generalized mixed-effects regression models and various alternatives, including generalized estimating equations for analysis ...
Linear and nonlinear models are illustrated using an example involving a study of the relationship between mood and smoking. 3.1 ...
Random Intercept and Trend Model For longitudinal data, the random intercept model is often too simplistic for a number of reasons. ...
doi:10.1146/annurev.clinpsy.032408.153550
pmid:20192796
pmcid:PMC2971698
fatcat:y4jp4nf7efh7temowrqh2yciyq
Subject specific and population average models for binary longitudinal data: a tutorial
2013
Longitudinal and life course studies
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We show how the autocorrelation found in longitudinal data is accounted for by both approaches, and why, in contrast to linear models for continuous outcomes, the parameters of population average and subject ...
Using data from the British Household Panel Survey, we illustrate how longitudinal repeated measures of binary outcomes are analysed using population average and subject specific logistic regression models ...
However, as we use BHPS data, anyone making such a request must also provide evidence that they are registered with the ESRC Data Archive. ...
doi:10.14301/llcs.v4i2.249
fatcat:rssr4mwuxvggzjre6ajikp6bbm
Longitudinal beta regression models for analyzing health-related quality of life scores over time
2012
BMC Medical Research Methodology
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This study examined the use of beta regression models for analyzing longitudinal HRQL data using two empirical examples with distributional features typically encountered in practice. ...
Methods: We used SF-6D utility data from a German older age cohort study and stroke-specific HRQL data from a randomized controlled trial. ...
We acknowledge with thanks PD Dr. Alarcos Cieza and Dr. ...
doi:10.1186/1471-2288-12-144
pmid:22984825
pmcid:PMC3528618
fatcat:6s6t4mtizbbehcne7lbgbm7ple
Translational methods in biostatistics: linear mixed effect regression models of alcohol consumption and HIV disease progression over time
2007
Epidemiologic Perspectives & Innovations
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Fitting a linear mixed effects multiple regression model with a random intercept and random slope for each subject accounts for the association of observations within subjects and yields parameters interpretable ...
We describe a linear mixed effects regression framework that accounts for the clustering of longitudinal data and that can be fit using standard statistical software. ...
We thank the reviewers as well as Seville Meli, Richard Saitz and Kristin Tyler for helpful comments on an earlier draft. ...
doi:10.1186/1742-5573-4-8
pmid:17880699
pmcid:PMC2147003
fatcat:ru5miqeh35g3douic3eh7zxoim
Linear quantile regression models for longitudinal experiments: an overview
2015
Metron
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We provide an overview of linear quantile regression models for continuous responses repeatedly measured over time. ...
The paper is concluded by an overview of open issues in longitudinal quantile regression. ...
Acknowledgments The authors are grateful to two referees for several suggestions. ...
doi:10.1007/s40300-015-0072-5
fatcat:sptuh3a2rbd4nmy5rfa46gtmg4
Longitudinal analysis of the strengths and difficulties questionnaire scores of the Millennium Cohort Study children in England usingM-quantile random-effects regression
2015
Journal of the Royal Statistical Society: Series A (Statistics in Society)
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We present a new approach, M -quantile random-effects regression, for modelling multilevel data. ...
Multilevel modelling is a popular approach for longitudinal data analysis. Statistical models conventionally target a parameter at the centre of a distribution. ...
Editor and two referees for comments that significantly improved the paper. ...
doi:10.1111/rssa.12126
pmid:27546997
pmcid:PMC4975608
fatcat:boal6dt4n5bedn3kibqjgcajr4
Avoiding bias in mixed model inference for fixed effects
2011
Statistics in Medicine
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One underspecification common for longitudinal data assumes a simple random intercept and conditional independence of the within-subject errors (i.e., compound symmetry). ...
The results illustrate how accurate inference for fixed effects in a general linear mixed model depends on the covariance model selected for the data. ...
The stacked-data structure creates parallels with univariate linear regression. ...
doi:10.1002/sim.4293
pmid:21751227
pmcid:PMC3396027
fatcat:svt5h2ginrddzby4a2hmpegvou
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