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Tytuł pozycji:

Joint model robustness compared with the time-varying covariate Cox model to evaluate the association between a longitudinal marker and a time-to-event endpoint.

Tytuł :
Joint model robustness compared with the time-varying covariate Cox model to evaluate the association between a longitudinal marker and a time-to-event endpoint.
Autorzy :
Arisido MW; Center of Biostatistics for Clinical Epidemiology, School of Medicine and Surgery, University of Milano-Bicocca, Via Cadore 48, Monza, 20052, Italy.
Antolini L; Center of Biostatistics for Clinical Epidemiology, School of Medicine and Surgery, University of Milano-Bicocca, Via Cadore 48, Monza, 20052, Italy.
Bernasconi DP; Center of Biostatistics for Clinical Epidemiology, School of Medicine and Surgery, University of Milano-Bicocca, Via Cadore 48, Monza, 20052, Italy.
Valsecchi MG; Center of Biostatistics for Clinical Epidemiology, School of Medicine and Surgery, University of Milano-Bicocca, Via Cadore 48, Monza, 20052, Italy.
Rebora P; Center of Biostatistics for Clinical Epidemiology, School of Medicine and Surgery, University of Milano-Bicocca, Via Cadore 48, Monza, 20052, Italy. .
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Źródło :
BMC medical research methodology [BMC Med Res Methodol] 2019 Dec 03; Vol. 19 (1), pp. 222. Date of Electronic Publication: 2019 Dec 03.
Typ publikacji :
Journal Article; Research Support, Non-U.S. Gov't
Język :
English
Imprint Name(s) :
Original Publication: London : BioMed Central, [2001-
MeSH Terms :
Proportional Hazards Models*
C-Reactive Protein/*metabolism
Graft vs Host Disease/*etiology
Hematopoietic Stem Cell Transplantation/*adverse effects
Serum Amyloid P-Component/*metabolism
Bias ; Biomarkers/metabolism ; Computer Simulation ; Graft vs Host Disease/metabolism ; Graft vs Host Disease/mortality ; Humans ; Longitudinal Studies ; Prognosis ; Survival Analysis ; Time Factors
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Contributed Indexing :
Keywords: Cox model*; Joint model simulation*; Longitudinal biomarker*; Random effects model*; Time-varying covariate*
Substance Nomenclature :
0 (Biomarkers)
0 (Serum Amyloid P-Component)
148591-49-5 (PTX3 protein)
9007-41-4 (C-Reactive Protein)
Entry Date(s) :
Date Created: 20191205 Date Completed: 20201005 Latest Revision: 20201005
Update Code :
20201023
PubMed Central ID :
PMC6888912
DOI :
10.1186/s12874-019-0873-y
PMID :
31795933
Czasopismo naukowe
Background: The recent progress in medical research generates an increasing interest in the use of longitudinal biomarkers for characterizing the occurrence of an outcome. The present work is motivated by a study, where the objective was to explore the potential of the long pentraxin 3 (PTX3) as a prognostic marker of Acute Graft-versus-Host Disease (GvHD) after haematopoietic stem cell transplantation. Time-varying covariate Cox model was commonly used, despite its limiting assumptions that marker values are constant in time and measured without error. A joint model has been developed as a viable alternative; however, the approach is computationally intensive and requires additional strong assumptions, in which the impacts of their misspecification were not sufficiently studied.
Methods: We conduct an extensive simulation to clarify relevant assumptions for the understanding of joint models and assessment of its robustness under key model misspecifications. Further, we characterize the extent of bias introduced by the limiting assumptions of the time-varying covariate Cox model and compare its performance with a joint model in various contexts. We then present results of the two approaches to evaluate the potential of PTX3 as a prognostic marker of GvHD after haematopoietic stem cell transplantation.
Results: Overall, we illustrate that a joint model provides an unbiased estimate of the association between a longitudinal marker and the hazard of an event in the presence of measurement error, showing improvement over the time-varying Cox model. However, a joint model is severely biased when the baseline hazard or the shape of the longitudinal trajectories are misspecified. Both the Cox model and the joint model correctly specified indicated PTX3 as a potential prognostic marker of GvHD, with the joint model providing a higher hazard ratio estimate.
Conclusions: Joint models are beneficial to investigate the capability of the longitudinal marker to characterize time-to-event endpoint. However, the benefits are strictly linked to the correct specification of the longitudinal marker trajectory and the baseline hazard function, indicating a careful consideration of assumptions to avoid biased estimates.
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