scholarly journals Assessing importance of biomarkers: A Bayesian joint modelling approach of longitudinal and survival data with semi-competing risks

2020 ◽  
pp. 1471082X2093336
Author(s):  
Fan Zhang ◽  
Ming-Hui Chen ◽  
Xiuyu Julie Cong ◽  
Qingxia Chen
Keyword(s):  
Disease Progression ◽  
Competing Risks ◽  
Survival Data ◽  
Marginal Likelihood ◽  
Cox Proportional Hazards Model ◽  
Terminal Event ◽  
Joint Modelling ◽  
Patient Reported ◽  
Longitudinal And Survival Data ◽  
Time Varying Covariates

Longitudinal biomarkers such as patient-reported outcomes (PROs) and quality of life (QOL) are routinely collected in cancer clinical trials or other studies. Joint modelling 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 ([Formula: see text]) and a Cox proportional hazards model with time-varying covariates for the overall survival time ([Formula: see text]) to account for [Formula: see text] and treatment switching. Under the semi-competing risks framework, the disease progression is the non-terminal 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 [Formula: see text]DIC as well as [Formula: see text]LPML to determine the importance and contribution of the longitudinal data to the model fit of the [Formula: see text] and [Formula: see text] data.

scholarly journals Flexible parametric joint modelling of longitudinal and survival data

2012 ◽  
Vol 31 (30) ◽  
pp. 4456-4471 ◽  
Author(s):  
Michael J. Crowther ◽  
Keith R. Abrams ◽  
Paul C. Lambert
Keyword(s):  
Survival Data ◽  
Joint Modelling ◽  
Longitudinal And Survival Data

scholarly journals Propensity score matching for treatment delay effects with observational survival data

2019 ◽  
Vol 29 (3) ◽  
pp. 695-708 ◽  
Author(s):  
Erinn M Hade ◽  
Giovanni Nattino ◽  
Heather A Frey ◽  
Bo Lu
Keyword(s):  
Propensity Score ◽  
Propensity Score Matching ◽  
Survival Data ◽  
Causal Effect ◽  
Treatment Delay ◽  
Cox Proportional Hazards Model ◽  
Time Varying ◽  
Delay Effect ◽  
Delay Effects ◽  
Time Varying Covariates

In observational studies with a survival outcome, treatment initiation may be time dependent, which is likely to be affected by both time-invariant and time-varying covariates. In situations where the treatment is necessary for the study population, all or most subjects may be exposed to the treatment sooner or later. In this scenario, the causal effect of interest is the delay in treatment reception. A simple comparison of those receiving treatment early vs. those receiving treatment late might not be appropriate, as the timing of the treatment reception is not randomized. Extending Lu’s matching design with time-varying covariates, we propose a propensity score matching strategy to estimate the treatment delay effect. The goal is to balance the covariate distribution between on-time treatment and delayed treatment groups at each time point using risk set matching. Our simulation study shows that, in the presence of treatment delay effects, the matching-based analyses clearly outperform the conventional regression analysis using the naive Cox proportional hazards model. We apply this method to study the treatment delay effect of 17 alpha-hydroxyprogesterone caproate (17P) for patients with recurrent preterm birth.

scholarly journals Joint modeling of longitudinal zero-inflated count and time-to-event data: A Bayesian perspective

2016 ◽  
Vol 27 (4) ◽  
pp. 1258-1270 ◽  
Author(s):  
Huirong Zhu ◽  
Stacia M DeSantis ◽  
Sheng Luo
Keyword(s):  
Survival Data ◽  
Proportional Hazards Model ◽  
Joint Modeling ◽  
Cox Proportional Hazards Model ◽  
Alpha Tocopherol ◽  
Repeated Measure ◽  
Terminal Event ◽  
Event Data ◽  
Time To Event ◽  
Time To Event Data

Longitudinal zero-inflated count data are encountered frequently in substance-use research when assessing the effects of covariates and risk factors on outcomes. Often, both the time to a terminal event such as death or dropout and repeated measure count responses are collected for each subject. In this setting, the longitudinal counts are censored by the terminal event, and the time to the terminal event may depend on the longitudinal outcomes. In the study described herein, we expand the class of joint models for longitudinal and survival data to accommodate zero-inflated counts and time-to-event data by using a Cox proportional hazards model with piecewise constant baseline hazard. We use a Bayesian framework via Markov chain Monte Carlo simulations implemented in the BUGS programming language. Via an extensive simulation study, we apply the joint model and obtain estimates that are more accurate than those of the corresponding independence model. We apply the proposed method to an alpha-tocopherol, beta-carotene lung cancer prevention study.

scholarly journals Generating Survival Times Using Cox Proportional Hazards Models with Cyclic and Piecewise Time-Varying Covariates

2020 ◽  
Vol 12 (3) ◽  
pp. 324-339 ◽  
Author(s):  
Yunda Huang ◽  
Yuanyuan Zhang ◽  
Zong Zhang ◽  
Peter B. Gilbert
Keyword(s):  
Survival Data ◽  
Dose Regimen ◽  
Hazard Function ◽  
Proportional Hazards ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Time Varying ◽  
Proportional Hazards Models ◽  
Cox Proportional Hazards Models ◽  
Time Varying Covariates

Abstract Time-to-event outcomes with cyclic time-varying covariates are frequently encountered in biomedical studies that involve multiple or repeated administrations of an intervention. In this paper, we propose approaches to generating event times for Cox proportional hazards models with both time-invariant covariates and a continuous cyclic and piecewise time-varying covariate. Values of the latter covariate change over time through cycles of interventions and its relationship with hazard differs before and after a threshold within each cycle. The simulations of data are based on inverting the cumulative hazard function and a log link function for relating the hazard function to the covariates. We consider closed-form derivations with the baseline hazard following the exponential, Weibull, or Gompertz distribution. We propose two simulation approaches: one based on simulating survival data under a single-dose regimen first before data are aggregated over multiple-dosing cycles and another based on simulating survival data directly under a multiple-dose regimen. We consider both fixed intervals and varying intervals of the drug administration schedule. The method’s validity is assessed in simulation experiments. The results indicate that the proposed procedures perform well in generating data that conform to their cyclic nature and assumptions of the Cox proportional hazards model.

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