Abstract
Dementia patients exhibit considerable heterogeneity in individual trajectories of cognitive decline, with some patients showing rapid decline following diagnoses while others exhibiting slower decline or remaining stable for several years. Dementia studies often collect longitudinal measures of multiple neuropsychological tests aimed to measure patients’ decline across a number of cognitive domains. We propose a multivariate finite mixture latent trajectory model to identify distinct longitudinal patterns of cognitive decline simultaneously in multiple cognitive domains, each of which is measured by multiple neuropsychological tests. EM algorithm is used for parameter estimation and posterior probabilities are used to predict latent class membership. We present results of a simulation study demonstrating adequate performance of our proposed approach and apply our model to the Uniform Data Set from the National Alzheimer's Coordinating Center to identify cognitive decline patterns among dementia patients.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1-21 |
| Number of pages | 21 |
| Journal | Journal of Applied Statistics |
| DOIs | |
| State | Accepted/In press - Feb 17 2016 |
Fingerprint
Keywords
- cognitive decline
- dementia
- Multivariate finite mixture latent trajectory
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty
Cite this
A multivariate finite mixture latent trajectory model with application to dementia studies. / Lai, Dongbing; Xu, Huiping; Koller, Daniel; Foroud, Tatiana; Gao, Sujuan.
In: Journal of Applied Statistics, 17.02.2016, p. 1-21.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - A multivariate finite mixture latent trajectory model with application to dementia studies
AU - Lai, Dongbing
AU - Xu, Huiping
AU - Koller, Daniel
AU - Foroud, Tatiana
AU - Gao, Sujuan
PY - 2016/2/17
Y1 - 2016/2/17
N2 - Dementia patients exhibit considerable heterogeneity in individual trajectories of cognitive decline, with some patients showing rapid decline following diagnoses while others exhibiting slower decline or remaining stable for several years. Dementia studies often collect longitudinal measures of multiple neuropsychological tests aimed to measure patients’ decline across a number of cognitive domains. We propose a multivariate finite mixture latent trajectory model to identify distinct longitudinal patterns of cognitive decline simultaneously in multiple cognitive domains, each of which is measured by multiple neuropsychological tests. EM algorithm is used for parameter estimation and posterior probabilities are used to predict latent class membership. We present results of a simulation study demonstrating adequate performance of our proposed approach and apply our model to the Uniform Data Set from the National Alzheimer's Coordinating Center to identify cognitive decline patterns among dementia patients.
AB - Dementia patients exhibit considerable heterogeneity in individual trajectories of cognitive decline, with some patients showing rapid decline following diagnoses while others exhibiting slower decline or remaining stable for several years. Dementia studies often collect longitudinal measures of multiple neuropsychological tests aimed to measure patients’ decline across a number of cognitive domains. We propose a multivariate finite mixture latent trajectory model to identify distinct longitudinal patterns of cognitive decline simultaneously in multiple cognitive domains, each of which is measured by multiple neuropsychological tests. EM algorithm is used for parameter estimation and posterior probabilities are used to predict latent class membership. We present results of a simulation study demonstrating adequate performance of our proposed approach and apply our model to the Uniform Data Set from the National Alzheimer's Coordinating Center to identify cognitive decline patterns among dementia patients.
KW - cognitive decline
KW - dementia
KW - Multivariate finite mixture latent trajectory
UR - http://www.scopus.com/inward/record.url?scp=84959054034&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84959054034&partnerID=8YFLogxK
U2 - 10.1080/02664763.2016.1141181
DO - 10.1080/02664763.2016.1141181
M3 - Article
AN - SCOPUS:84959054034
SP - 1
EP - 21
JO - Journal of Applied Statistics
JF - Journal of Applied Statistics
SN - 0266-4763
ER -