Joint analysis of longitudinal measurements and survival times with a cure fraction based on partly linear mixed and semiparametric cure models

2020 ◽  
Author(s):  
Lu Yang ◽  
Hui Song ◽  
Yingwei Peng ◽  
Dongsheng Tu
Keyword(s):  
Joint Analysis ◽  
Survival Times ◽  
Longitudinal Measurements ◽  
Cure Fraction ◽  
Cure Models

Joint longitudinal and time-to-event cure models for the assessment of being cured

2019 ◽  
Vol 29 (4) ◽  
pp. 1256-1270
Author(s):  
Antoine Barbieri ◽  
Catherine Legrand
Keyword(s):  
Survival Data ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Medical Intervention ◽  
Time To Event ◽  
Mixture Cure Model ◽  
Longitudinal Measurements ◽  
Cure Fraction ◽  
Cure Models ◽  
Latent Structures

Medical time-to-event studies frequently include two groups of patients: those who will not experience the event of interest and are said to be “cured” and those who will develop the event and are said to be “susceptible”. However, the cure status is unobserved in (right-)censored patients. While most of the work on cure models focuses on the time-to-event for the uncured patients (latency) or on the baseline probability of being cured or not (incidence), we focus in this research on the conditional probability of being cured after a medical intervention given survival until a certain time. Assuming the availability of longitudinal measurements collected over time and being informative on the risk to develop the event, we consider joint models for longitudinal and survival data given a cure fraction. These models include a linear mixed model to fit the trajectory of longitudinal measurements and a mixture cure model. In simulation studies, different shared latent structures linking both submodels are compared in order to assess their predictive performance. Finally, an illustration on HIV patient data completes the comparison.

scholarly journals An approach to joint analysis of longitudinal measurements and competing risks failure time data

2007 ◽  
Vol 26 (14) ◽  
pp. 2813-2835 ◽  
Author(s):  
Robert M. Elashoff ◽  
Gang Li ◽  
Ning Li
Keyword(s):  
Competing Risks ◽  
Failure Time ◽  
Joint Analysis ◽  
Failure Time Data ◽  
Time Data ◽  
Longitudinal Measurements

Cure fraction estimation from the mixture cure models for grouped survival data

2004 ◽  
Vol 23 (11) ◽  
pp. 1733-1747 ◽  
Author(s):  
Binbing Yu ◽  
Ram C. Tiwari ◽  
Kathleen A. Cronin ◽  
Eric J. Feuer
Keyword(s):  
Survival Data ◽  
Cure Fraction ◽  
Cure Models

scholarly journals Survival Analysis in The Presence of a Cure Fraction Using Data of Dialysis Patients: A Bayesian Approach

2021 ◽  
Author(s):  
Shideh Rafati ◽  
Mohammad Reza Baneshi ◽  
Laleh Hassani ◽  
Abbas Bahrampour
Keyword(s):  
Goodness Of Fit ◽  
Significant Proportion ◽  
Dialysis Patients ◽  
Term Survival ◽  
Long Term Survival ◽  
Mixture Cure Model ◽  
Cure Fraction ◽  
Dagum Distribution ◽  
Cure Models

Abstract Background: The aim of this study was to evaluate the goodness of fit of Bayesian mixture and non-mixture cure models to find the factors affecting dialysis patient’s survival time where a significant proportion of the population has a long-term survival.Study Design: A retrospective cohort study. Methods: The data of 252 dialysis patients were used among whom 35 cases died. Since in this study a part of the population had long-term survival, Bayesian cure models were used and evaluated using DIC index. The data were analyzed by R and Openbugs Softwares. Results: Of the 252 dialysis patients, 136(54%) were males and the mean (SD) age was 53.39 (18.09) years. The patient’s follow-up time mean (SD) was 10.93(7.82) years. The 10 and 20-year survival rate of these patients were 87% and 73%, respectively. The findings show that the best fitting belonged to the Bayesian Non-mixture Cure Model (BNCM) with Dagum distribution. The variables of age, Body Mass Index, dialysis duration, frequency of dialysis, age of onset of dialysis, and occupation affected patients' survival based on BNCM with Dagum distribution.Conclusions: The results demonstrated that the BNCM with Dagum distribution can be a good selection model to analyze survival data, where there is the possibility of a fraction of cure.

On a Class of Transmuted Distributions to Model Survival Data with a Cure Fraction

2021 ◽  
Vol 73 (2) ◽  
pp. 106-126
Author(s):  
G. Asha ◽  
C. S. Soorya
Keyword(s):  
Survival Data ◽  
Likelihood Function ◽  
Real Life ◽  
Estimation Of Parameters ◽  
Cure Rate ◽  
Data Set ◽  
Cure Rate Models ◽  
Subject Classifications ◽  
Cure Fraction ◽  
Cure Models

Modelling time to event data, when there is always a proportion of the individuals, commonly referred to as immunes who do not experience the event of interest, is of importance in many biomedical studies. Improper distributions are used to model these situations and they are generally referred to as cure rate models. In the literature, two main families of cure rate models have been proposed, namely the mixture cure models and promotion time cure models. Here we propose a new model by extending the mixture model via a generating function by considering a shifted Bernoulli distribution. This gives rise to a new class of popular distributions called the transmuted class of distributions to model survival data with a cure fraction. The properties of the proposed model are investigated and parameters estimated. The Bayesian approach to the estimation of parameters is also adopted. The complexity of the likelihood function is handled through the Metropolis-Hasting algorithm. The proposed method is illustrated with few examples using different baseline distributions. A real life data set is also analysed. AMS subject classifications: 62N02, 62F15

The Joint Analysis of Survival Times and Response Rates in Clinical Trials on the Treatment of Cancer

1980 ◽  
Vol 19 (02) ◽  
pp. 112-114 ◽  
Author(s):  
J. Wahrendorf
Keyword(s):  
Clinical Trial ◽  
Survival Data ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Time Dependent ◽  
Joint Analysis ◽  
Survival Times ◽  
Logrank Test ◽  
Hazards Model ◽  
Time Dependent Covariates

A method is outlined which allows to analyse survival and response data from a clinical trial on the treatment of cancer when the question of interest is whether the somehow defined response to the treatment has an influence on patients’ survival times. This can be done by making use of Cox’s proportional hazards model for survival data together with time-dependent covariates. This allows an appropriate classification of non-respondors and responders by considering the time of diagnosis of response during follow-up. The resulting test can be viewed as a time-dependent logrank test. An example from a clinical trial on gastrointestinal cancer is considered.

Mediation analysis for mixture Cox proportional hazards cure models

2021 ◽  
pp. 096228022110031
Author(s):  
Xiaoxiao Zhou ◽  
Xinyuan Song
Keyword(s):  
Mediation Analysis ◽  
Proportional Hazards ◽  
Cox Proportional Hazards ◽  
Survival Models ◽  
Causal Mechanism ◽  
Specific Effects ◽  
Cure Fraction ◽  
Cure Models ◽  
Restricted Mean Survival Time ◽  
Latent Group

Mediation analysis aims to decompose a total effect into specific pathways and investigate the underlying causal mechanism. Although existing methods have been developed to conduct mediation analysis in the context of survival models, none of these methods accommodates the existence of a substantial proportion of subjects who never experience the event of interest, even if the follow-up is sufficiently long. In this study, we consider mediation analysis for the mixture of Cox proportional hazards cure models that cope with the cure fraction problem. Path-specific effects on restricted mean survival time and survival probability are assessed by introducing a partially latent group indicator and applying the mediation formula approach in a three-stage mediation framework. A Bayesian approach with P-splines for approximating the baseline hazard function is developed to conduct analysis. The satisfactory performance of the proposed method is verified through simulation studies. An application of the Alzheimer’s disease (AD) neuroimaging initiative dataset investigates the causal effects of APOE-[Formula: see text] allele on AD progression.

Parametric cure models of relative and cause-specific survival for grouped survival times

2000 ◽  
Vol 61 (2) ◽  
pp. 99-110 ◽  
Author(s):  
John W. Gamel ◽  
Edie A. Weller ◽  
Margaret N. Wesley ◽  
Eric J. Feuer
Keyword(s):  
Survival Times ◽  
Cure Models ◽  
Cause Specific Survival
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