Literature DB >> 22468033

Joint modelling of longitudinal outcome and interval-censored competing risk dropout in a schizophrenia clinical trial.

Ralitza Gueorguieva1, Robert Rosenheck, Haiqun Lin.   

Abstract

The 'Clinical antipsychotic trials in intervention effectiveness' study, was designed to evaluate whether there were significant differences between several antipsychotic medications in effectiveness, tolerability, cost and quality of life of subjects with schizophrenia. Overall, 74 % of patients discontinued the study medication for various reasons before the end of 18 months in phase I of the study. When such a large percentage of study participants fail to complete the study schedule, it is not clear whether the apparent profile in effectiveness reflects genuine changes over time or is influenced by selection bias, with participants with worse (or better) outcome values being more likely to drop out or to discontinue. To assess the effect of dropouts for different reasons on inferences, we construct a joint model for the longitudinal outcome and cause-specific dropouts that allows for interval-censored dropout times. Incorporating the information regarding the cause of dropout improves inferences and provides better understanding of the association between cause-specific dropout and the outcome process. We use simulations to demonstrate the advantages of the joint modelling approach in terms of bias and efficiency.

Entities:  

Year:  2011        PMID: 22468033      PMCID: PMC3315284          DOI: 10.1111/j.1467-985X.2011.00719.x

Source DB:  PubMed          Journal:  J R Stat Soc Ser A Stat Soc        ISSN: 0964-1998            Impact factor:   2.483


  20 in total

1.  Diagnostics for joint longitudinal and dropout time modeling.

Authors:  Angela Dobson; Robin Henderson
Journal:  Biometrics       Date:  2003-12       Impact factor: 2.571

2.  Methods for the analysis of informatively censored longitudinal data.

Authors:  M D Schluchter
Journal:  Stat Med       Date:  1992 Oct-Nov       Impact factor: 2.373

3.  Joint modelling of multivariate longitudinal profiles: pitfalls of the random-effects approach.

Authors:  Steffen Fieuws; Geert Verbeke
Journal:  Stat Med       Date:  2004-10-30       Impact factor: 2.373

4.  Comments about Joint Modeling of Cluster Size and Binary and Continuous Subunit-Specific Outcomes.

Authors:  Ralitza V Gueorguieva
Journal:  Biometrics       Date:  2005-09       Impact factor: 2.571

5.  Simultaneously modelling censored survival data and repeatedly measured covariates: a Gibbs sampling approach.

Authors:  C L Faucett; D C Thomas
Journal:  Stat Med       Date:  1996-08-15       Impact factor: 2.373

6.  Model-based approaches to analysing incomplete longitudinal and failure time data.

Authors:  J W Hogan; N M Laird
Journal:  Stat Med       Date:  1997 Jan 15-Feb 15       Impact factor: 2.373

7.  A joint model for survival and longitudinal data measured with error.

Authors:  M S Wulfsohn; A A Tsiatis
Journal:  Biometrics       Date:  1997-03       Impact factor: 2.571

8.  Effectiveness of antipsychotic drugs in patients with chronic schizophrenia.

Authors:  Jeffrey A Lieberman; T Scott Stroup; Joseph P McEvoy; Marvin S Swartz; Robert A Rosenheck; Diana O Perkins; Richard S E Keefe; Sonia M Davis; Clarence E Davis; Barry D Lebowitz; Joanne Severe; John K Hsiao
Journal:  N Engl J Med       Date:  2005-09-19       Impact factor: 91.245

9.  Cost-effectiveness of second-generation antipsychotics and perphenazine in a randomized trial of treatment for chronic schizophrenia.

Authors:  Robert A Rosenheck; Douglas L Leslie; Jody Sindelar; Edward A Miller; Haiqun Lin; T Scott Stroup; Joseph McEvoy; Sonia M Davis; Richard S E Keefe; Marvin Swartz; Diana O Perkins; John K Hsiao; Jeffrey Lieberman
Journal:  Am J Psychiatry       Date:  2006-12       Impact factor: 18.112

10.  Robust joint modeling of longitudinal measurements and competing risks failure time data.

Authors:  Ning Li; Robert M Elashoff; Gang Li
Journal:  Biom J       Date:  2009-02       Impact factor: 2.207

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  4 in total

1.  Joint modelling of repeated measurements and time-to-event outcomes: flexible model specification and exact likelihood inference.

Authors:  Jessica Barrett; Peter Diggle; Robin Henderson; David Taylor-Robinson
Journal:  J R Stat Soc Series B Stat Methodol       Date:  2014-04-08       Impact factor: 4.488

Review 2.  Joint Analyses of Longitudinal and Time-to-Event Data in Research on Aging: Implications for Predicting Health and Survival.

Authors:  Konstantin G Arbeev; Igor Akushevich; Alexander M Kulminski; Svetlana V Ukraintseva; Anatoliy I Yashin
Journal:  Front Public Health       Date:  2014-11-06

3.  Comparison of a time-varying covariate model and a joint model of time-to-event outcomes in the presence of measurement error and interval censoring: application to kidney transplantation.

Authors:  Kristen R Campbell; Elizabeth Juarez-Colunga; Gary K Grunwald; James Cooper; Scott Davis; Jane Gralla
Journal:  BMC Med Res Methodol       Date:  2019-06-26       Impact factor: 4.615

4.  Predicting the Survival of AIDS Patients Using Two Frameworks of Statistical Joint Modeling and Comparing Their Predictive Accuracy.

Authors:  Fatemeh Khorashadizadeh; Hamed Tabesh; Mahboubeh Parsaeian; Habibollah Esmaily; Abbas Rahimi Foroushani
Journal:  Iran J Public Health       Date:  2020-05       Impact factor: 1.429

  4 in total

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