Competing regression models for longitudinal data

Alencar, Airlane Pereira; Singer, Júlio M.; Rocha, Francisco Marcelo Monteiro da

doi:10.1002/bimj.201100056

Cited by 12 publications

(11 citation statements)

References 41 publications

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“…These models are very flexible (they include the class of linearisable models, as considered in Alencar et al. (), for example), easily interpretable and may be fitted via a series of very efficient algorithms for which software is widely available. Furthermore, if both the fixed and random components are well specified, the results obtained in practical applications are usually very similar to those generated by other classes of models as evidenced in Pinheiro et al.…”

Section: Discussionmentioning

confidence: 99%

“…() or Alencar et al. (), for example. Such models are also convenient because in addition to the population parameters, they provide insight on the covariance structure as well as on the individual components.…”

Section: Discussionmentioning

confidence: 99%

“…These models are very flexible (they include the class of linearisable models, as considered in Alencar et al (2012), for example), easily interpretable and may be fitted via a series of very efficient algorithms for which software is widely available. Furthermore, if both the fixed and random components are well specified, the results obtained in practical applications are usually very similar to those generated by other classes of models as evidenced in Pinheiro et al (2001), Savalli et al (2006) or Alencar et al (2012), for example. Such models are also convenient because in addition to the population parameters, they provide insight on the covariance structure as well as on the individual components.…”

Section: Discussionmentioning

confidence: 99%

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Graphical Tools for Detecting Departures from Linear Mixed Model Assumptions and Some Remedial Measures

Singer

Rocha

Nobre

2016

Int Statistical Rev

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Summary We review some results on the analysis of longitudinal data or, more generally, of repeated measures via linear mixed models starting with some exploratory statistical tools that may be employed to specify a tentative model. We follow with a summary of inferential procedures under a Gaussian set‐up and then discuss different diagnostic methods focusing on residual analysis but also addressing global and local influence. Based on the interpretation of diagnostic plots related to three types of residuals (marginal, conditional and predicted random effects) as well as on other tools, we proceed to identify remedial measures for possible violations of the proposed model assumptions, ranging from fine‐tuning of the model to the use of elliptically symmetric or skew‐elliptical linear mixed models as well as of robust estimation methods. We specify many results available in the literature in a unified notation and highlight those with greater practical appeal. In each case, we discuss the availability of model diagnostics as well as of software and give general guidelines for model selection. We conclude with analyses of three practical examples and suggest further directions for research.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Graphical Tools for Detecting Departures from Linear Mixed Model Assumptions and Some Remedial Measures

Singer

Rocha

Nobre

2016

Int Statistical Rev

Self Cite

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“…To account for the longitudinal nature of the data and the accompanying intra-subject correlation, the analyses were based on the Generalized Estimation Equation (GEE) approach [ 26 , 27 ], which represents a marginal model with robust parameter estimates [ 28 ]. Contrary to mixed models, which would have been an alternative approach to account for intra-subject correlation, marginal models calculate population averages instead of subject-specific trajectories [ 28 , 29 ].…”

Section: Methodsmentioning

confidence: 99%

Health Care Service Utilization of Dementia Patients before and after Institutionalization: A Claims Data Analysis

Schwarzkopf

Hao

Holle

et al. 2014

Dement Geriatr Cogn Disord Extra

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Background: Community-based and institutional dementia care has been compared in cross-sectional studies, but longitudinal information on the effect of institutionalization on health care service utilization is sparse. Methods: We analyzed claims data from 651 dementia patients via Generalized Estimation Equations to assess health care service utilization profiles and corresponding expenditures from four quarters before to four quarters after institutionalization. Results: In all domains, utilization increased in the quarter of institutionalization. Afterwards, the use of drugs, medical aids, and non-physician services (e.g., occupational therapy and physiotherapy) remained elevated, but use of in- and outpatient treatment decreased. Cost of care showed corresponding profiles. Conclusion: Institutional dementia care seems to be associated with an increased demand for supportive services but not necessarily for specialized medical care.

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“…The notation 65-66 or 65-68 in the equations was used to indicate the first two or four years of Medicare coverage (equivalent to Medicare enrollees' 65 to 66 or 65 to 68 years of age). The models were specified as: [31] and generalized linear mixed model was one of the candidate models [32] . Generalized linear mixed model was selected for its support for both logit and ordered logit models to accommodate binary and ordinal outcomes evaluated in this study [33] .…”

Section: Functional Formsmentioning

confidence: 99%

Medicare spending, managed care and pre-Medicare insurance coverage and associated risks of mortality, deterioration of self-rated health and mental health after four years of Medicare coverage

Chao

2014

JHA

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National spending on Medicare keeps growing and managed care is reimbursed differently in the United States. Health returns from Medicare spending are not certain. This study aims to quantify the effects of Medicare spending in the first two years of Medicare coverage, managed care and insurance coverage before Medicare (pre-Medicare) on mortality, mental health and self-rated health status after first four years of Medicare coverage. Individuals, who were interviewed from age 65 to 68 years, without Medicare coverage before age 65 years, were included. Health spending (out-of-pocket, OOP) in the first two years of Medicare coverage, their pre-Medicare characteristics and Medicare managed care were used to predict associated risks of mortality, self-rated health status and mental health (Center for Epidemiologic Studies-Depression, CESD scale). Eligible Medicare enrollees (N = 3,503) in the Health and Retirement Study from 1992 to 2011 were chosen. Total health spending was associated with higher likelihood of worse mental health and self-rated health, but OOP spending was associated with risks of health deterioration (p < .05 for all). More OOP health spending in the first two years of Medicare coverage was associated with slightly higher chance of more mental problems, but the magnitude of this association became smaller over time. Medicare managed care did not seem to be beneficial for mortality, mental health or self-rated health status. Expanding pre-Medicare health coverage (through the Affordable Care Act) may not influence health status after first four years of Medicare coverage. Preventing pre-Medicare health conditions may be the priority.

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Competing regression models for longitudinal data

Cited by 12 publications

References 41 publications

Graphical Tools for Detecting Departures from Linear Mixed Model Assumptions and Some Remedial Measures

Graphical Tools for Detecting Departures from Linear Mixed Model Assumptions and Some Remedial Measures

Health Care Service Utilization of Dementia Patients before and after Institutionalization: A Claims Data Analysis

Medicare spending, managed care and pre-Medicare insurance coverage and associated risks of mortality, deterioration of self-rated health and mental health after four years of Medicare coverage

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