Random Changepoint Model for Joint Modeling of Cognitive Decline and Dementia

We propose a joint model for cognitive decline and risk of dementia to describe the pre-diagnosis phase of dementia. We aim to estimate the time when the cognitive evolution of subjects in the pre-dementia phase becomes distinguishable from normal evolution and to study whether the shape of cognitive decline depends on educational level. The model combines a piecewise polynomial mixed model with a random change point for the evolution of the cognitive test and a log-normal model depending on the random change point for the time to dementia. Parameters are estimated by maximum likelihood using a Newton-Raphson-like algorithm. The expected cognitive evolution given age to dementia is then derived and the marginal distribution of dementia is estimated to check the log-normal assumption.

[1]  D. Marquardt An Algorithm for Least-Squares Estimation of Nonlinear Parameters , 1963 .

[2]  D Commenges,et al.  A penalized likelihood approach for arbitrarily censored and truncated data: application to age-specific incidence of dementia. , 1998, Biometrics.

[3]  Jean-François Dartigues,et al.  The 9 year cognitive decline before dementia of the Alzheimer type: a prospective population-based study. , 2005, Brain : a journal of neurology.

[4]  M. Wulfsohn,et al.  A joint model for survival and longitudinal data measured with error. , 1997, Biometrics.

[5]  R. Mayeux,et al.  Influence of education and occupation on the incidence of Alzheimer's disease. , 1994, JAMA.

[6]  C B Hall,et al.  A change point model for estimating the onset of cognitive decline in preclinical Alzheimer's disease. , 2000, Statistics in medicine.

[7]  M. Sliwinski,et al.  Neuropsychological prediction of dementia and the absence of dementia in healthy elderly persons , 1994, Neurology.

[8]  R. Hashemi,et al.  A Latent Process Model for Joint Modeling of Events and Marker , 2003, Lifetime data analysis.

[9]  Jeremy M G Taylor,et al.  The joint modeling of a longitudinal disease progression marker and the failure time process in the presence of cure. , 2002, Biostatistics.

[10]  V. De Gruttola,et al.  Modelling progression of CD4-lymphocyte count and its relationship to survival time. , 1994, Biometrics.

[11]  D Commenges,et al.  Incidence of dementia and Alzheimer's disease in elderly community residents of south-western France. , 1994, International journal of epidemiology.

[12]  J. M. Taylor,et al.  Survival Analysis Using Auxiliary Variables Via Multiple Imputation, with Application to AIDS Clinical Trial Data , 2002, Biometrics.

[13]  Daniel Commenges,et al.  A penalized likelihood approach for an illness-death model with interval-censored data: application to age-specific incidence of dementia. , 2002, Biostatistics.

[14]  G. Seber,et al.  Nonlinear Regression: Seber/Nonlinear Regression , 2005 .

[15]  D Commenges,et al.  A 5-year longitudinal study of the Mini-Mental State Examination in normal aging. , 1997, American journal of epidemiology.

[16]  R Henderson,et al.  Joint modelling of longitudinal measurements and event time data. , 2000, Biostatistics.

[17]  C. Fabrigoule,et al.  221 A five-year longitudinal study of mini-mental state examination in normal aging , 1996, Neurobiology of Aging.

[18]  Lynn Kuo,et al.  Bayesian and profile likelihood change point methods for modeling cognitive function over time , 2003, Comput. Stat. Data Anal..

[19]  D Commenges,et al.  Cognitive predictors of dementia in elderly community residents. , 1997, Neuroepidemiology.

[20]  D. Pauler,et al.  Predicting time to prostate cancer recurrence based on joint models for non‐linear longitudinal biomarkers and event time outcomes , 2002, Statistics in medicine.