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Abstract
Latent trajectory analysis is a form of latent class analysis, where the manifest variables are longitudinal measurements of a single outcome. The latent classes may correspond to either constant increasing or decreasing levels of the outcome over time and describe different severity or course of a disease. Extension to multiple outcomes at each time point allows more accurate determination of classes, with classes based on combination of the outcomes, however requiring models which account for both correlation between outcomes and periods. Three models are described for multiple binary outcomes, observed at each time point: a latent class model where all outcomes are considered independent at all time points, a model incorporating random effects for subject only and one incorporating random effects for subject and period. The methods are applied to data on asthma and allergy symptoms in infants, with symptoms recorded at four time points, and it is shown that the incorporation of subject and period heterogeneity results in lower estimates of the number of latent classes.
|
Beath K
(2008) RandomLCA. R package. Available at http://cran.r-project.org/package=randomLCA Google Scholar | |
|
Croudace T
,
Jarvelin M-R
,
Wadsworth M
and
Jones P
(2003)
Development typology of trajectories to nighttime bladder control: epidemiologic application of longitudinal latent class analysis
. American Journal of Epidemiology, 157,
834–42
. Google Scholar | Crossref | Medline | |
|
Fink P
,
Jensen J
,
Poulsen C 1993)
A study of hospital admissions over time, using longitudinal latent structure analysis
. Scandanavian Journal of Social Medicine, 21(3),
211–19
. Google Scholar | SAGE Journals | |
|
Follmann D
(1994)
Modelling transitional and joint marginal distributions in repeated categorical data
. Statistics in Medicine, 13,
467–77
. Google Scholar | Crossref | Medline | |
|
Fraley C Raftery A
(1998)
How many clusters? Which clustering method? Answers via model-based cluster analysis
. Computer Journal, 41(8),
578–88
. Google Scholar | Crossref | |
|
Grizzle JE
and
Allen DM
(1968)
Analysis of growth and dose response curves
. Biometrics, 25,
357–81
. Google Scholar | Crossref | |
|
Gueorguieva RV
and
Sanacora G
(2006)
Joint analysis of repeatedly observed continuous and ordinal measures of disease severity
. Statistics in Medicine, 25,
1307–322
. Google Scholar | Crossref | Medline | |
|
Keribin C
(2000)
Consistent estimation of the order of mixture models
. Sankhyä A, 62(1),
49–66
. Google Scholar | |
|
Langeheine R
and
van de Pol F
(1990)
A unifying framework for Markov modeling in discrete space and discrete time
. Sociological Methods and Research, 18(4),
416–41
. Google Scholar | SAGE Journals | |
|
Langeheine R
and
van de Pol F
(1993)
Multiple indicator Markov models
. In
R. Steyer
,
K. Wender
and
K. Widaman
, eds, ‘Psychometric Methodology. Proceedings of the 7th European Meeting of the Psychometric Society in Trier’, Gustav Fischer Verlag, Stuttgart, pp.
248–52
. Google Scholar | |
|
McHugh RB
(1956)
Efficient estimation and local identification in latent class analysis
. Psychometrika, 21(4),
331–47
. Google Scholar | Crossref | |
|
Mihrshahi S
,
Peat J
,
Webb K
,
Tovey R
, et al. (2001)
The Childhood Asthma Prevention Study (CAPS): design and research protocol of a randomized trial for the primary prevention of asthma
. Controlled Clinical Trials, 22,
333–54
. Google Scholar | Crossref | Medline | |
|
Muthèn B
and
Shedden K
(1999)
Finite mixture modeling with mixture outcomes using the EM algorithm
. Biometrics, 55(2),
463–69
. Google Scholar | Crossref | Medline | |
|
Nagin DS
and
Land KC
(1993)
Age, criminal careers, and population heterogeneity: specification and estimation of a nonparametric, mixed Poisson model
. Criminology, 31(3),
327–62
. Google Scholar | Crossref | |
|
Pickles A
and
Angold A
(2003)
Natural categories or fundamental dimensions: on carving nature at the joints and the rearticulation of psychopathology
. Development and Psychopathology, 15,
529–51
. Google Scholar | Crossref | Medline | ISI | |
|
Qu Y
,
Tan M
and
Kutner MH
(1996)
Random effects models in latent class analysis for evaluating accuracy of diagnostic tests
. Biometrics, 52(3),
797–810
. Google Scholar | Crossref | Medline | |
| R Development Core Team (2004) R: A language and environment for statistical computing.
Vienna, Austria: R Foundation for Statistical Computing
. Google Scholar | |
|
Rabe-Hesketh S
,
Skrondal A
,
Pickles A
(2005)
Maximum likelihood estimation of limited and discrete dependent variable models with nested random effects
. Journal of Econometrics, 128,
301–23
. Google Scholar | Crossref | ISI | |
|
Schwarz G
(1978)
Estimating the dimension of a model
. Annals of Statistics, 6,
461–64
. Google Scholar | Crossref | ISI | |
|
Skene AM
and
White SA
(1992)
A latent class model for repeated measurements experiments
. Statistics in Medicine, 11,
2111–122
. Google Scholar | Crossref | Medline | ISI | |
|
Uebersax JS
(1999)
Probit latent class analysis with dicotomous or ordered category measures: conditional independence/ dependence models
. Applied Psychological Measurement, 23(4),
283–97
. Google Scholar | SAGE Journals | |
|
Vermunt JK
(2007)
Multilevel mixture item response theory models: an application in education testing
, Proceedings of the 56th Session of the International Statistical Institute. Available at http://spitswww.uvt.nl/~vermunt/isi2007_vermunt.pdf Google Scholar | |
|
Vermunt JK
(2008)
Latent class and finite mixture models for multilevel data sets
. Statistical Methods in Medical Research, 17,
33–51
. Google Scholar | SAGE Journals | ISI | |
|
Vermunt JK
and
Magidson J
(2007) LG-Syntax User’s Guide: Manual for Latent Gold 4.5 Syntax Module.
Belmont, MA: Statistical Innovations Inc
. Google Scholar | |
|
Vermunt JK
and
van Dijk LA
(2001)
A non-parametric random coefficient approach: the latent class regression model
. Multilevel Modelling Newsletter, 13(2),
6–13
. Google Scholar | |
|
Ware JH
and
Lipsitz S
(1988)
Issues in the analysis of repeated categorical outcomes
. Statistics in Medicine, 7,
95–107
. Google Scholar | Crossref | Medline | ISI |
