Identification of Multivariate Responders/Non-Responders Using Bayesian Growth Curve Latent Class Models. - Wikidata - wikimedia - TriplyDB

Identification of Multivariate Responders/Non-Responders Using Bayesian Growth Curve Latent Class Models.

Identification of Multivariate Responders/Non-Responders Using Bayesian Growth Curve Latent Class Models.

http://www.wikidata.org/entity/Q35014266

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Methods for analyzing observational longitudinal prognosis studies for rheumatic diseases: a review & worked example using a clinic-based cohort of juvenile dermatomyositis patients.A bayesian two-part latent class model for longitudinal medical expenditure data: assessing the impact of mental health and substance abuse parity.Bayesian multivariate growth curve latent class models for mixed outcomes.Estimators for longitudinal latent exposure models: examining measurement model assumptions.Identification of treatment responders based on multiple longitudinal outcomes with applications to multiple sclerosis patients.Growth curve mixture models.Methods for assessing and controlling placebo effects.A Bayesian growth mixture model to examine maternal hypertension and birth outcomes.

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P2860

Identification of Multivariate Responders/Non-Responders Using Bayesian Growth Curve Latent Class Models.

http://www.wikidata.org/entity/Q35014266

description

2009 nî lūn-bûn

@nan

2009 թուականի Սեպտեմբերին հրատարակուած գիտական յօդուած

@hyw

2009 թվականի սեպտեմբերին հրատարակված գիտական հոդված

@hy

2009年の論文

@ja

2009年論文

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2009年論文

@zh-hant

2009年論文

@zh-hk

2009年論文

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2009年論文

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2009年论文

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Identification of Multivariate ...... wth Curve Latent Class Models.

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Identification of Multivariate ...... wth Curve Latent Class Models.

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J.1467-9876.2009.00663.X

P1433

Journal of the Royal Statistical Society Series C: Applied Statistics

P1476

Identification of Multivariate ...... wth Curve Latent Class Models.

@en

P2093

Benjamin E Leiby

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Kevin G Lynch

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Mary D Sammel

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Thomas R Ten Have

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P2860

Random-effects models for longitudinal data

Latent variables, measurement error and methods for analysing longitudinal binary and ordinal data.

Latent variable models for longitudinal data with multiple continuous outcomes.

Using a Bayesian latent growth curve model to identify trajectories of positive affect and negative events following myocardial infarction

The integration of continuous and discrete latent variable models: potential problems and promising opportunities.

Finite mixture modeling with mixture outcomes using the EM algorithm.

General growth mixture modeling for randomized preventive interventions.

Pairwise fitting of mixed models for the joint modeling of multivariate longitudinal profiles.

Discovery of morphological subgroups that correlate with severity of symptoms in interstitial cystitis: a proposed biopsy classification system.

Latent class model diagnosis.

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505-524

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10.1111/J.1467-9876.2009.00663.X

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