دورية أكاديمية
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Assessing Importance of Biomarkers: a Bayesian Joint Modeling Approach of Longitudinal and Survival Data with Semicompeting Risks.

التفاصيل البيبلوغرافية
العنوان: Assessing Importance of Biomarkers: a Bayesian Joint Modeling Approach of Longitudinal and Survival Data with Semicompeting Risks.
المؤلفون: Zhang F; Pfizer Inc., Groton, CT, USA., Chen MH; Department of Statistics, University of Connecticut, Storrs, CT, USA., Cong XJ; Everest Medicines, Shanghai, China., Chen Q; Department of Biostatistics, Vanderbilt University, Nashville, TN, USA.
المصدر: Statistical modelling [Stat Modelling] 2021 Feb 01; Vol. 21 (1-2), pp. 30-55. Date of Electronic Publication: 2020 Jul 27.
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Arnold Country of Publication: England NLM ID: 101314442 Publication Model: Print-Electronic Cited Medium: Print ISSN: 1471-082X (Print) Linking ISSN: 1471082X NLM ISO Abbreviation: Stat Modelling Subsets: PubMed not MEDLINE
أسماء مطبوعة: Original Publication: London : Arnold,
مستخلص: Longitudinal biomarkers such as patient-reported outcomes (PROs) and quality of life (QOL) are routinely collected in cancer clinical trials or other studies. Joint modeling of PRO/QOL and survival data can provide a comparative assessment of patient-reported changes in specific symptoms or global measures that correspond to changes in survival. Motivated by a head and neck cancer clinical trial, we develop a class of trajectory-based models for longitudinal and survival data with disease progression. Specifically, we propose a class of mixed effects regression models for longitudinal measures, a cure rate model for the disease progression time ( T P ), and a Cox proportional hazards model with time-varying covariates for the overall survival time ( T D ) to account for T P and treatment switching. Under the semi-competing risks framework, the disease progression is the nonterminal event, the occurrence of which is subject to a terminal event of death. The properties of the proposed models are examined in detail. Within the Bayesian paradigm, we derive the decompositions of the deviance information criterion (DIC) and the logarithm of the pseudo marginal likelihood (LPML) to assess the fit of the longitudinal component of the model and the fit of each survival component, separately. We further develop ΔDIC as well as ΔLPML to determine the importance and contribution of the longitudinal data to the model fit of the T P and T D data.
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معلومات مُعتمدة: P01 CA142538 United States CA NCI NIH HHS; R01 CA074015 United States CA NCI NIH HHS; R01 GM070335 United States GM NIGMS NIH HHS
فهرسة مساهمة: Keywords: Cure rate model; DIC Decomposition; Markov chain Monte Carlo; Patient-reported outcome; Shared parameter model; Time-varying covariates
تواريخ الأحداث: Date Created: 20210730 Latest Revision: 20220202
رمز التحديث: 20221213
مُعرف محوري في PubMed: PMC8315720
DOI: 10.1177/1471082x20933363
PMID: 34326706
قاعدة البيانات: MEDLINE
الوصف
تدمد:1471-082X
DOI:10.1177/1471082x20933363