Relapse-remission and remission-relapse switches in rheumatoid arthritis patients were modeled by random effects

J. Berkhof, D.L. Knol, F.P.J. Rijmen, J.W.R. Twisk, B.M.J. Uitdehaag, M. Boers

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Objective: To compare statistical models for the analysis of two-state disease processes. Study Design and Setting: A two-armed randomized trial of patients with early rheumatoid arthritis (RA) treated by either combination therapy (sulfasazaline, methotrextate, prednisolone) or monotherapy (sulfasazaline). Disease activity (remission or relapse) was analyzed with the logistic regression model, the proportional hazards regression model, and the continuous-time Markov process model for panel data. The dependence among the switching times was studied by (1) including correlated normal random patient effects for the relapse-remission and remission-relapse switching probabilities; (2) assuming the population to be a mixture of patients responsive and nonresponsive to therapy; (3) including separate parameters for the first and subsequent relapse-remission switch; and (4) combining (1) and (3). The four approaches were compared using parametric bootstrap checks. Results: The logistic regression model, the proportional hazards regression model, and the continuous-time Markov process model for panel data yielded similar combination therapy effects. The inclusion of random patient effects (approaches I and 4) gave the best fit to the observed disease activity pattern. Conclusion: Models with correlated random effects can provide a satisfactory fit to two-state disease patterns. (C) 2009 Elsevier Inc. All rights reserved
Original languageUndefined/Unknown
Pages (from-to)1085-1094
JournalJournal of Clinical Epidemiology
Volume62
Issue number10
DOIs
Publication statusPublished - 2009

Cite this

@article{cfa03b19e16149dda6e5b255b45e3f4e,
title = "Relapse-remission and remission-relapse switches in rheumatoid arthritis patients were modeled by random effects",
abstract = "Objective: To compare statistical models for the analysis of two-state disease processes. Study Design and Setting: A two-armed randomized trial of patients with early rheumatoid arthritis (RA) treated by either combination therapy (sulfasazaline, methotrextate, prednisolone) or monotherapy (sulfasazaline). Disease activity (remission or relapse) was analyzed with the logistic regression model, the proportional hazards regression model, and the continuous-time Markov process model for panel data. The dependence among the switching times was studied by (1) including correlated normal random patient effects for the relapse-remission and remission-relapse switching probabilities; (2) assuming the population to be a mixture of patients responsive and nonresponsive to therapy; (3) including separate parameters for the first and subsequent relapse-remission switch; and (4) combining (1) and (3). The four approaches were compared using parametric bootstrap checks. Results: The logistic regression model, the proportional hazards regression model, and the continuous-time Markov process model for panel data yielded similar combination therapy effects. The inclusion of random patient effects (approaches I and 4) gave the best fit to the observed disease activity pattern. Conclusion: Models with correlated random effects can provide a satisfactory fit to two-state disease patterns. (C) 2009 Elsevier Inc. All rights reserved",
author = "J. Berkhof and D.L. Knol and F.P.J. Rijmen and J.W.R. Twisk and B.M.J. Uitdehaag and M. Boers",
year = "2009",
doi = "10.1016/j.jclinepi.2008.11.013",
language = "Undefined/Unknown",
volume = "62",
pages = "1085--1094",
journal = "Journal of Clinical Epidemiology",
issn = "0895-4356",
publisher = "Elsevier USA",
number = "10",

}

Relapse-remission and remission-relapse switches in rheumatoid arthritis patients were modeled by random effects. / Berkhof, J.; Knol, D.L.; Rijmen, F.P.J.; Twisk, J.W.R.; Uitdehaag, B.M.J.; Boers, M.

In: Journal of Clinical Epidemiology, Vol. 62, No. 10, 2009, p. 1085-1094.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Relapse-remission and remission-relapse switches in rheumatoid arthritis patients were modeled by random effects

AU - Berkhof, J.

AU - Knol, D.L.

AU - Rijmen, F.P.J.

AU - Twisk, J.W.R.

AU - Uitdehaag, B.M.J.

AU - Boers, M.

PY - 2009

Y1 - 2009

N2 - Objective: To compare statistical models for the analysis of two-state disease processes. Study Design and Setting: A two-armed randomized trial of patients with early rheumatoid arthritis (RA) treated by either combination therapy (sulfasazaline, methotrextate, prednisolone) or monotherapy (sulfasazaline). Disease activity (remission or relapse) was analyzed with the logistic regression model, the proportional hazards regression model, and the continuous-time Markov process model for panel data. The dependence among the switching times was studied by (1) including correlated normal random patient effects for the relapse-remission and remission-relapse switching probabilities; (2) assuming the population to be a mixture of patients responsive and nonresponsive to therapy; (3) including separate parameters for the first and subsequent relapse-remission switch; and (4) combining (1) and (3). The four approaches were compared using parametric bootstrap checks. Results: The logistic regression model, the proportional hazards regression model, and the continuous-time Markov process model for panel data yielded similar combination therapy effects. The inclusion of random patient effects (approaches I and 4) gave the best fit to the observed disease activity pattern. Conclusion: Models with correlated random effects can provide a satisfactory fit to two-state disease patterns. (C) 2009 Elsevier Inc. All rights reserved

AB - Objective: To compare statistical models for the analysis of two-state disease processes. Study Design and Setting: A two-armed randomized trial of patients with early rheumatoid arthritis (RA) treated by either combination therapy (sulfasazaline, methotrextate, prednisolone) or monotherapy (sulfasazaline). Disease activity (remission or relapse) was analyzed with the logistic regression model, the proportional hazards regression model, and the continuous-time Markov process model for panel data. The dependence among the switching times was studied by (1) including correlated normal random patient effects for the relapse-remission and remission-relapse switching probabilities; (2) assuming the population to be a mixture of patients responsive and nonresponsive to therapy; (3) including separate parameters for the first and subsequent relapse-remission switch; and (4) combining (1) and (3). The four approaches were compared using parametric bootstrap checks. Results: The logistic regression model, the proportional hazards regression model, and the continuous-time Markov process model for panel data yielded similar combination therapy effects. The inclusion of random patient effects (approaches I and 4) gave the best fit to the observed disease activity pattern. Conclusion: Models with correlated random effects can provide a satisfactory fit to two-state disease patterns. (C) 2009 Elsevier Inc. All rights reserved

U2 - 10.1016/j.jclinepi.2008.11.013

DO - 10.1016/j.jclinepi.2008.11.013

M3 - Article

VL - 62

SP - 1085

EP - 1094

JO - Journal of Clinical Epidemiology

JF - Journal of Clinical Epidemiology

SN - 0895-4356

IS - 10

ER -