Grant Number:	2R01CA082370-07 Interpret this number
Primary Investigator:	Neuhaus, John
Organization:	University Of California, San Francisco
Project Title:	Assessing / Developing Methods for Complex Dependent Data
Fiscal Year:	2010

DESCRIPTION (provided by applicant): Dependent data methods are widely used in practice to conduct analyses for hierarchical and longitudinal studies. Formally, their use depends on a variety of assumptions, for example, parametric distributional assumptions or assumptions about the sampling mechanism. The overarching goal of our research program is to quantify sensitivity to these assumptions, identifying scenarios in which they are and are not important and developing new, more robust methods when necessary. Our focus is on assumptions surrounding the sampling mechanism, including missing data and outcome dependent sampling. Our research has four aims. Our first aim considers the performance of commonly used methods that are used to avoid cluster level confounding. However, they may be sensitive to data which are missing at random (MAR), but not missing completely at random (MCAR). Aims 2 through 4 consider outcome dependent sampling mechanisms that are frequently encountered in practice. In Aim 2 we consider the use of parametric mixed models in situations such as for outcome dependent family studies. Aim 3 considers the situation in which the timing of measurements may be dependent on previous results (e.g., more frequent exams in prostate cancer patients with rising prostate specific antigen (PSA) test results). Aim 4 considers a general and unifying context in which to imbed the work on Aims 2 and 3, derive optimality results, and develop a platform for creation of new statistical methods for outcome dependent sampling. Results from our research will directly inform current statistical practice for these commonly used methods. PUBLIC HEALTH RELEVANCE: Longitudinal and dependent data analysis methods are used in a wide variety of biomedical research investigations. If the assumptions on which the models are based are incorrect in important ways, it could give rise to misleading research conclusions. Knowing for which assumptions the results are sensitive and by how much is important to their proper use.




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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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