Literature DB >> 22139873

Intermittent observation of time-dependent explanatory variables: a multistate modelling approach.

Brian D M Tom1, Vernon T Farewell.   

Abstract

Motivated by investigations of factors related to various patient-reported outcome measures in psoriatic arthritis patients, after controlling for the effect of disease activity on these outcomes, we outline an approach for dealing with a rapidly fluctuating explanatory variable in a multistate model. On the basis of a representation of this variable as an ordinal classification, we suggest the use of an expanded multistate model. We examine the bias in estimating effects associated with other variables via simulation for different modelling choices. We present an analysis of a motivating data set on physical functional disability in psoriatic arthritis patients.
Copyright © 2011 John Wiley & Sons, Ltd.

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Year:  2011        PMID: 22139873     DOI: 10.1002/sim.4429

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  8 in total

1.  Joint modelling of longitudinal and multi-state processes: application to clinical progressions in prostate cancer.

Authors:  Loïc Ferrer; Virginie Rondeau; James Dignam; Tom Pickles; Hélène Jacqmin-Gadda; Cécile Proust-Lima
Journal:  Stat Med       Date:  2016-04-18       Impact factor: 2.373

2.  Recurrent event data analysis with intermittently observed time-varying covariates.

Authors:  Shanshan Li; Yifei Sun; Chiung-Yu Huang; Dean A Follmann; Richard Krause
Journal:  Stat Med       Date:  2016-02-16       Impact factor: 2.373

3.  State selection in Markov models for panel data with application to psoriatic arthritis.

Authors:  Howard H Z Thom; Christopher H Jackson; Daniel Commenges; Linda D Sharples
Journal:  Stat Med       Date:  2015-03-05       Impact factor: 2.373

4.  The versatility of multi-state models for the analysis of longitudinal data with unobservable features.

Authors:  Vernon T Farewell; Brian D M Tom
Journal:  Lifetime Data Anal       Date:  2012-12-06       Impact factor: 1.588

5.  Application of seemingly unrelated regression in medical data with intermittently observed time-dependent covariates.

Authors:  Sareh Keshavarzi; Seyyed Mohammad Taghi Ayatollahi; Najaf Zare; Maryam Pakfetrat
Journal:  Comput Math Methods Med       Date:  2012-12-18       Impact factor: 2.238

6.  A joint modelling approach for multistate processes subject to resolution and under intermittent observations.

Authors:  Sean Yiu; Brian Tom
Journal:  Stat Med       Date:  2016-10-17       Impact factor: 2.373

7.  Clustered multistate models with observation level random effects, mover-stayer effects and dynamic covariates: modelling transition intensities and sojourn times in a study of psoriatic arthritis.

Authors:  Sean Yiu; Vernon T Farewell; Brian D M Tom
Journal:  J R Stat Soc Ser C Appl Stat       Date:  2017-07-25       Impact factor: 1.864

8.  Partially hidden multi-state modelling of a prolonged disease state defined by a composite outcome.

Authors:  Vernon T Farewell; Li Su; Christopher Jackson
Journal:  Lifetime Data Anal       Date:  2019-01-19       Impact factor: 1.588

  8 in total

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