Backward joint model and dynamic prediction of survival with multivariate longitudinal data

2021 ◽  
Author(s):  
Fan Shen ◽  
Liang Li
Keyword(s):  
Longitudinal Data ◽  
Joint Model ◽  
Dynamic Prediction ◽  
Multivariate Longitudinal Data ◽  
Prediction Of Survival

Joint model for left-censored longitudinal data, recurrent events and terminal event: Predictive abilities of tumor burden for cancer evolution with application to the FFCD 2000-05 trial

Biometrics ◽  
2016 ◽  
Vol 72 (3) ◽  
pp. 907-916 ◽  
Author(s):  
Agnieszka Król ◽  
Loïc Ferrer ◽  
Jean-Pierre Pignon ◽  
Cécile Proust-Lima ◽  
Michel Ducreux ◽  
...  
Keyword(s):  
Longitudinal Data ◽  
Recurrent Events ◽  
Joint Model ◽  
Tumor Burden ◽  
Terminal Event ◽  
Cancer Evolution

Landmark Prediction of Survival for Breast Cancer Patients: A Case Study in Tehran, Iran

2020 ◽  
Author(s):  
Behnaz ALAFCHI ◽  
Leili TAPAK ◽  
Omid HAMIDI ◽  
Jalal POOROLAJAL ◽  
Hossein MAHJUB
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
High Risk Patient ◽  
Prognostic Index ◽  
Cox Proportional Hazards ◽  
Risk Patient ◽  
Breast Cancer Patients ◽  
Dynamic Prediction ◽  
Data Set ◽  
Prediction Of Survival

Background: Breast cancer is the first non-cutaneous malignancy in women and the second cause of death due to cancer all over the world. There are situations where researchers are interested in dynamic prediction of survival of patients where traditional models might fail to achieve this goal. We aimed to use a dynamic prediction model in analyzing survival of breast cancer patients. Methods: We used a data set originates from a retrospective cohort (registry-based) study conducted in 2014 in Tehran, Iran, information of 550 patients were available analyzed. A method of landmarking was utilized for dynamic prediction of survival of the patients. The criteria of time-dependent area under the curve and prediction error curve were used to evaluate the performance of the model. Results: An index of risk score (prognostic index) was calculated according to the available covariates based on Cox proportional hazards. Therefore, hazard of dying for a high-risk patient with breast cancer within the next five years was 2.69 to 3.04 times of that for a low-risk patient. The value of the dynamic C-index was 0.89 using prognostic index as covariate. Conclusion: Generally, the landmark model showed promising performance in predicting survival or probability of dying for breast cancer patients in this study in a predefined window. Therefore, this model can be used in other studies as a useful model for investigating the survival of breast cancer patients.

scholarly journals A joint model for mixed and truncated longitudinal data and survival data, with application to HIV vaccine studies

Biostatistics ◽  
2017 ◽  
Vol 19 (3) ◽  
pp. 374-390 ◽  
Author(s):  
Tingting Yu ◽  
Lang Wu ◽  
Peter B Gilbert
Keyword(s):  
Longitudinal Data ◽  
Survival Data ◽  
Joint Model ◽  
Hiv Vaccine ◽  
Likelihood Inference ◽  
Likelihood Method ◽  
Parameter Estimates ◽  
Computationally Efficient ◽  
Approximate Likelihood ◽  
Longitudinal And Survival Data

SUMMARY In HIV vaccine studies, a major research objective is to identify immune response biomarkers measured longitudinally that may be associated with risk of HIV infection. This objective can be assessed via joint modeling of longitudinal and survival data. Joint models for HIV vaccine data are complicated by the following issues: (i) left truncations of some longitudinal data due to lower limits of quantification; (ii) mixed types of longitudinal variables; (iii) measurement errors and missing values in longitudinal measurements; (iv) computational challenges associated with likelihood inference. In this article, we propose a joint model of complex longitudinal and survival data and a computationally efficient method for approximate likelihood inference to address the foregoing issues simultaneously. In particular, our model does not make unverifiable distributional assumptions for truncated values, which is different from methods commonly used in the literature. The parameters are estimated based on the h-likelihood method, which is computationally efficient and offers approximate likelihood inference. Moreover, we propose a new approach to estimate the standard errors of the h-likelihood based parameter estimates by using an adaptive Gauss–Hermite method. Simulation studies show that our methods perform well and are computationally efficient. A comprehensive data analysis is also presented.

A Joint Model for Survival and Longitudinal Data Measured with Error

Biometrics ◽  
1997 ◽  
Vol 53 (1) ◽  
pp. 330 ◽  
Author(s):  
Michael S. Wulfsohn ◽  
Anastasios A. Tsiatis
Keyword(s):  
Longitudinal Data ◽  
Joint Model
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