Incidence and mortality of Alzheimer's disease or dementia using an illness-death model
We present an illness‐death model for studying the incidence and the prevalence of Alzheimer's disease or dementia. We argue that the illness‐death model is better than a survival model for this purpose. In this model the best choice for the basic time‐scale is age. Then we present extensions o...
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Published in | Statistics in medicine Vol. 23; no. 2; pp. 199 - 210 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
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Chichester, UK
John Wiley & Sons, Ltd
30.01.2004
Wiley-Blackwell Wiley |
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Abstract | We present an illness‐death model for studying the incidence and the prevalence of Alzheimer's disease or dementia. We argue that the illness‐death model is better than a survival model for this purpose. In this model the best choice for the basic time‐scale is age. Then we present extensions of this model for incorporating covariates and taking account of a possible effect of calendar time. Calendar time is introduced via a proportional intensity model. We give the likelihood for a mixed discrete‐continuous observation pattern from this model: clinical status is observed at discrete visit‐times while the date of death is observed exactly or right‐censored. The penalized likelihood approach allows to non‐parametrically estimate the transition intensities. Application on the data of the Paquid study allows to produce estimates of the age‐specific incidence of dementia together with mortality rates of both demented and non‐demented subjects. Then the effect of calendar time and educational level are studied. Low educational level increases the risk of dementia. The risk of dementia increases with calendar time while the mortality of demented subjects decreases. The most likely explanation of this result seems to be in a shift in the diagnosis of dementia towards earlier stages of the disease prompted by a change in the perception of dementia and the arrival of new drugs. Copyright © 2004 John Wiley & Sons, Ltd. |
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AbstractList | We present an illness-death model for studying the incidence and the prevalence of Alzheimer's disease or dementia. We argue that the illness-death model is better than a survival model for this purpose. In this model the best choice for the basic time-scale is age. Then we present extensions of this model for incorporating covariates and taking account of a possible effect of calendar time. Calendar time is introduced via a proportional intensity model. We give the likelihood for a mixed discrete-continuous observation pattern from this model: clinical status is observed at discrete visit-times while the date of death is observed exactly or right-censored. The penalized likelihood approach allows to non-parametrically estimate the transition intensities. Application on the data of the Paquid study allows to produce estimates of the age-specific incidence of dementia together with mortality rates of both demented and non-demented subjects. Then the effect of calendar time and educational level are studied. Low educational level increases the risk of dementia. The risk of dementia increases with calendar time while the mortality of demented subjects decreases. The most likely explanation of this result seems to be in a shift in the diagnosis of dementia towards earlier stages of the disease prompted by a change in the perception of dementia and the arrival of new drugs. Abstract We present an illness‐death model for studying the incidence and the prevalence of Alzheimer's disease or dementia. We argue that the illness‐death model is better than a survival model for this purpose. In this model the best choice for the basic time‐scale is age. Then we present extensions of this model for incorporating covariates and taking account of a possible effect of calendar time. Calendar time is introduced via a proportional intensity model. We give the likelihood for a mixed discrete‐continuous observation pattern from this model: clinical status is observed at discrete visit‐times while the date of death is observed exactly or right‐censored. The penalized likelihood approach allows to non‐parametrically estimate the transition intensities. Application on the data of the Paquid study allows to produce estimates of the age‐specific incidence of dementia together with mortality rates of both demented and non‐demented subjects. Then the effect of calendar time and educational level are studied. Low educational level increases the risk of dementia. The risk of dementia increases with calendar time while the mortality of demented subjects decreases. The most likely explanation of this result seems to be in a shift in the diagnosis of dementia towards earlier stages of the disease prompted by a change in the perception of dementia and the arrival of new drugs. Copyright © 2004 John Wiley & Sons, Ltd. We present an illness-death model for studying the incidence and the prevalence of Alzheimer’s disease or dementia. We argue that the illness-death model is better than a survival model for this purpose. In this model the best choice for the basic time-scale is age. Then we present extensions of this model for incorporating covariates and taking account of a possible effect of calendar time. Calendar time is introduced via a proportional intensity model. We give the likelihood for a mixed discrete-continuous observation pattern from this model: clinical status is observed at discrete visit-times while the date of death is observed exactly or right-censored. The penalized likelihood approach allows to non-parametrically estimating the transition intensities. Application on the data of the Paquid study allows to produce estimates of the age-specific incidence of dementia together with mortality rates of both demented and non-demented subjects. Then the effect of calendar time and educational level are studied. Low educational level increases the risk of dementia. The risk of dementia increases with calendar time while the mortality of demented decreases. The most likely explanation of this result seems to be in a shift in the diagnosis of dementia towards earlier stages of the disease prompted by a change in the perception of dementia and the arrival of new drugs. We present an illness‐death model for studying the incidence and the prevalence of Alzheimer's disease or dementia. We argue that the illness‐death model is better than a survival model for this purpose. In this model the best choice for the basic time‐scale is age. Then we present extensions of this model for incorporating covariates and taking account of a possible effect of calendar time. Calendar time is introduced via a proportional intensity model. We give the likelihood for a mixed discrete‐continuous observation pattern from this model: clinical status is observed at discrete visit‐times while the date of death is observed exactly or right‐censored. The penalized likelihood approach allows to non‐parametrically estimate the transition intensities. Application on the data of the Paquid study allows to produce estimates of the age‐specific incidence of dementia together with mortality rates of both demented and non‐demented subjects. Then the effect of calendar time and educational level are studied. Low educational level increases the risk of dementia. The risk of dementia increases with calendar time while the mortality of demented subjects decreases. The most likely explanation of this result seems to be in a shift in the diagnosis of dementia towards earlier stages of the disease prompted by a change in the perception of dementia and the arrival of new drugs. Copyright © 2004 John Wiley & Sons, Ltd. |
Author | Letenneur, L. Commenges, D. Dartigues, JF Joly, P. |
AuthorAffiliation | 1 Equipe de Biostatistique INSERM Université Bordeaux Segalen - Bordeaux 2 IFR99 ISPED Universite Victor Segalen 146, Rue Leo Saignat 33076 BORDEAUX CEDEX |
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Incidence data from the PAQUID project publication-title: Journal of Neurology Neurosurgery and Psychiatry – volume: 42 start-page: 845 year: 1986 end-page: 854 article-title: A proportional hazards model for interval‐censored failure time data publication-title: Biometrics – year: 1993 – volume: 54 start-page: 853 year: 1992 end-page: 866 article-title: A non‐parametric estimation procedure for a periodically observed three‐state Markov process, with application to AIDS publication-title: Journal of the Royal Statistical Society, Series B – ident: e_1_2_1_11_2 doi: 10.2307/2532030 – ident: e_1_2_1_2_2 doi: 10.2307/2346307 – ident: e_1_2_1_5_2 doi: 10.2307/2532891 – ident: e_1_2_1_6_2 doi: 10.2307/2534006 – ident: e_1_2_1_4_2 doi: 10.2307/2530698 – volume: 6 start-page: 861 year: 1996 ident: e_1_2_1_20_2 article-title: Penalized likelihood hazard estimation: a general procedure publication-title: Statistica Sinica contributor: fullname: Gu C – volume: 54 start-page: 853 year: 1992 ident: e_1_2_1_12_2 article-title: A non‐parametric estimation procedure for a periodically observed three‐state Markov process, with application to AIDS publication-title: Journal of the Royal Statistical Society, Series B contributor: fullname: Frydman H – ident: e_1_2_1_10_2 doi: 10.1007/978-1-4612-4348-9 – ident: e_1_2_1_18_2 doi: 10.1191/0962280202sm279ra – ident: e_1_2_1_9_2 doi: 10.2307/2983150 – volume: 82 start-page: 773 year: 1995 ident: e_1_2_1_14_2 article-title: Non‐parametric estimation of a Markov ‘illness‐death model’ process from interval‐censored observations, with application to diabetes survival data publication-title: Biometrika contributor: fullname: Frydman H – ident: e_1_2_1_23_2 doi: 10.1093/aje/154.7.642 – ident: e_1_2_1_17_2 doi: 10.1023/A:1009636125294 – ident: e_1_2_1_7_2 doi: 10.1002/(SICI)1097-0258(19980915)17:17<1973::AID-SIM892>3.0.CO;2-5 – ident: e_1_2_1_19_2 – volume: 38 start-page: 290 year: 1976 ident: e_1_2_1_3_2 article-title: The empirical distribution function with arbitrarily grouped, censored and truncated data publication-title: Journal of the Royal Statistical Society, Series B contributor: fullname: Turnbull BW – ident: e_1_2_1_8_2 doi: 10.1002/sim.4780070605 – ident: e_1_2_1_21_2 doi: 10.1111/1541-0420.00020 – ident: e_1_2_1_22_2 doi: 10.1136/jnnp.66.2.177 – ident: e_1_2_1_15_2 doi: 10.1111/j.0006-341X.1999.00887.x – ident: e_1_2_1_13_2 doi: 10.2307/2532938 – ident: e_1_2_1_16_2 doi: 10.1093/biostatistics/3.3.433 |
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Snippet | We present an illness‐death model for studying the incidence and the prevalence of Alzheimer's disease or dementia. We argue that the illness‐death model is... We present an illness-death model for studying the incidence and the prevalence of Alzheimer's disease or dementia. We argue that the illness-death model is... Abstract We present an illness‐death model for studying the incidence and the prevalence of Alzheimer's disease or dementia. We argue that the illness‐death... We present an illness-death model for studying the incidence and the prevalence of Alzheimer’s disease or dementia. We argue that the illness-death model is... |
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SubjectTerms | Alzheimer Disease Alzheimer Disease - epidemiology Alzheimer Disease - mortality Alzheimer's disease Dementia Dementia - epidemiology Dementia - mortality France France - epidemiology Humans incidence interval-censored data Life Sciences Markov Chains Markov models Models, Statistical multistate models Santé publique et épidémiologie |
Title | Incidence and mortality of Alzheimer's disease or dementia using an illness-death model |
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