Grant Number:	2R01CA082370-04A1 Interpret this number
Primary Investigator:	Neuhaus, John
Organization:	University Of California, San Francisco
Project Title:	Assessing / Developing Methods for Complex Dependent Data
Fiscal Year:	2006

DESCRIPTION (provided by applicant): Complex dependent data involving cluster sampling, longitudinal designs and other forms of correlated data arise frequently in studies of health. For example, longitudinal data are essential to assess changes in health status over time and determinants of those changes. Cluster designs arise naturally in sample surveys, in group randomized trials, in health care profiling of institutions or physicians and in studies of familial aggregation of disease. Models incorporating random effects such as generalized linear mixed models and non-linear mixed effects models provide effective analyses of such complex dependent data but typically require the specification of the distribution of the random effects. The consequence of misspecifying aspects of the distribution of the random effects is of considerable debate in the literature. Some research has shown that such misspecification can produce biased estimates of parameters of interest and potentially misleading inference while other research has demonstrated that there is little impact. Through theory, simulation and application to real datasets, this application will investigate, in a systematic and unified way, which aspects of the specification of a random effects distribution are innocuous as opposed to important, how to diagnosis specification errors, and develop possible remedies to those errors. The proposed work will investigate the effects of misspecifying random effects distributions in nonlinear mixed models by 1) conducting a comprehensive assessment of the bias and efficient loss due to misspecification of random effects distributions; 2) developing and evaluating the performance of diagnostic methods to detect misspecification of random effects distribution; 3) investigating the performance of methods that may minimize the effects of misspecification of random effects distribution in nonlinear mixed models. This research extends our previous work and addresses many of the issues raised by the 1999 NSF-CBMS Regional Conference on generalized linear mixed models. We will produce illustrative, comparative analyses of data from several longitudinal and clustered studies of chronic disease. The results of this research will explain seemingly contradictory findings in the literature, provide a thorough assessment to the consequences of misspecifying random effects distributions and provide new methods to detect such misspecification. The results will provide biomedical investigators with effective methods to analyze data gathered using longitudinal and cluster study designs and guidelines for avoiding inappropriate analyses. The proposed research will comprehensively assess the consequences of misspecifying features of methods commonly used to analyze data in longitudinal studies of health. The research will develop new statistical methods to help investigators avoid misleading and inappropriate analyses and will provide them with effective methods to study important issues in studies of health.




Biased And Unbiased Estimation In Longitudinal Studies With Informative Visit Processes
Authors: McCulloch C.E. , Neuhaus J.M. , Olin R.L. .
Source: Biometrics, 2016-03-17 00:00:00.0; , .
PMID: 26990830
Related Citations

A Prospective Comparison Of Informant-based And Performance-based Dementia Screening Tools To Predict In-hospital Delirium
Authors: Zeng L. , Josephson S.A. , Fukuda K.A. , Neuhaus J. , Douglas V.C. .
Source: Alzheimer Disease And Associated Disorders, 2015 Oct-Dec; 29(4), p. 312-6.
PMID: 25350550
Related Citations

Covariate Decomposition Methods For Longitudinal Missing-at-random Data And Predictors Associated With Subject-specific Effects
Authors: Neuhaus J.M. , McCulloch C.E. .
Source: Australian & New Zealand Journal Of Statistics, 2014 Dec; 56(4), p. 331-345.
PMID: 26052246
Related Citations

Likelihood-based Analysis Of Longitudinal Data From Outcome-related Sampling Designs
Authors: Neuhaus J.M. , Scott A.J. , Wild C.J. , Jiang Y. , McCulloch C.E. , Boylan R. .
Source: Biometrics, 2014 Mar; 70(1), p. 44-52.
PMID: 24571396
Related Citations

Impact Of Gender And Blood Pressure On Poststroke Cognitive Decline Among Older Latinos
Authors: Levine D.A. , Haan M.N. , Langa K.M. , Morgenstern L.B. , Neuhaus J. , Lee A. , Lisabeth L.D. .
Source: Journal Of Stroke And Cerebrovascular Diseases : The Official Journal Of National Stroke Association, 2013 Oct; 22(7), p. 1038-45.
PMID: 22748715
Related Citations

Medication Adherence: Tailoring The Analysis To The Data
Authors: Saberi P. , Johnson M.O. , McCulloch C.E. , Vittinghoff E. , Neilands T.B. .
Source: Aids And Behavior, 2011 Oct; 15(7), p. 1447-53.
PMID: 21833689
Related Citations

The Effect Of Misspecification Of Random Effects Distributions In Clustered Data Settings With Outcome-dependent Sampling
Authors: Neuhaus J.M. , McCulloch C.E. .
Source: The Canadian Journal Of Statistics = Revue Canadienne De Statistique, 2011-09-01 00:00:00.0; 39(3), p. 488-497.
PMID: 23204632
Related Citations

A Note On Type Ii Error Under Random Effects Misspecification In Generalized Linear Mixed Models
Authors: Neuhaus,J.M. , McCulloch,C.E. , Boylan,R. .
Source: Biometrics, 2011 Jun; 67(2), p. 654-6; disucssion 656-60.
PMID: 21689077
Related Citations

Prediction Of Random Effects In Linear And Generalized Linear Models Under Model Misspecification
Authors: McCulloch,C.E. , Neuhaus,J.M. .
Source: Biometrics, 2011 Mar; 67(1), p. 270-9.
PMID: 20528860
Related Citations

An Open-label Study Of Memantine Treatment In 3 Subtypes Of Frontotemporal Lobar Degeneration
Authors: Boxer,A.L. , Lipton,A.M. , Womack,K. , Merrilees,J. , Neuhaus,J. , Pavlic,D. , Gandhi,A. , Red,D. , Martin-Cook,K. , Svetlik,D. , et al. .
Source: Alzheimer Disease And Associated Disorders, 2009 Jul-Sep; 23(3), p. 211-7.
PMID: 19812461
Related Citations

Clinical-neuroimaging Characteristics Of Dysexecutive Mild Cognitive Impairment
Authors: Pa,J. , Boxer,A. , Chao,L.L. , Gazzaley,A. , Freeman,K. , Kramer,J. , Miller,B.L. , Weiner,M.W. , Neuhaus,J. , Johnson,J.K. .
Source: Annals Of Neurology, 2009 Apr; 65(4), p. 414-23.
PMID: 19399879
Related Citations