Discrete‐time survival data with longitudinal covariates

2020 ◽  
Vol 39 (29) ◽  
pp. 4372-4385
Author(s):  
Chi‐Chung Wen ◽  
Yi‐Hau Chen
Keyword(s):  
Discrete Time ◽  
Survival Data ◽  
Longitudinal Covariates

The Effect of Partly Missing Covariates on Statistical Power in Randomized Controlled Trials With Discrete-Time Survival Endpoints

Methodology ◽  
2017 ◽  
Vol 13 (2) ◽  
pp. 41-60
Author(s):  
Shahab Jolani ◽  
Maryam Safarkhani
Keyword(s):  
Randomized Controlled Trials ◽  
Discrete Time ◽  
Treatment Effect ◽  
Survival Data ◽  
Controlled Trials ◽  
Missing Covariates ◽  
Indicator Method ◽  
Imputation Methods ◽  
Randomized Controlled ◽  
Baseline Covariates

Abstract. In randomized controlled trials (RCTs), a common strategy to increase power to detect a treatment effect is adjustment for baseline covariates. However, adjustment with partly missing covariates, where complete cases are only used, is inefficient. We consider different alternatives in trials with discrete-time survival data, where subjects are measured in discrete-time intervals while they may experience an event at any point in time. The results of a Monte Carlo simulation study, as well as a case study of randomized trials in smokers with attention deficit hyperactivity disorder (ADHD), indicated that single and multiple imputation methods outperform the other methods and increase precision in estimating the treatment effect. Missing indicator method, which uses a dummy variable in the statistical model to indicate whether the value for that variable is missing and sets the same value to all missing values, is comparable to imputation methods. Nevertheless, the power level to detect the treatment effect based on missing indicator method is marginally lower than the imputation methods, particularly when the missingness depends on the outcome. In conclusion, it appears that imputation of partly missing (baseline) covariates should be preferred in the analysis of discrete-time survival data.

scholarly journals Dynamic estimation with random forests for discrete‐time survival data

2021 ◽  
Author(s):  
Hoora Moradian ◽  
Weichi Yao ◽  
Denis Larocque ◽  
Jeffrey S. Simonoff ◽  
Halina Frydman
Keyword(s):  
Discrete Time ◽  
Random Forests ◽  
Survival Data ◽  
Dynamic Estimation

scholarly journals Proportional exponentiated link transformed hazards (ELTH) models for discrete time survival data with application

2015 ◽  
Vol 22 (1) ◽  
pp. 38-62 ◽  
Author(s):  
Hee-Koung Joeng ◽  
Ming-Hui Chen ◽  
Sangwook Kang
Keyword(s):  
Discrete Time ◽  
Survival Data

Measures of prediction error for survival data with longitudinal covariates

2011 ◽  
Vol 53 (2) ◽  
pp. 275-293 ◽  
Author(s):  
Rotraut Schoop ◽  
Martin Schumacher ◽  
Erika Graf
Keyword(s):  
Survival Data ◽  
Prediction Error ◽  
Longitudinal Covariates

Modelling discrete time survival data with random slopes: evaluating haemodialysis centres

2003 ◽  
Vol 22 (22) ◽  
pp. 3543-3555 ◽  
Author(s):  
Marilia S� Carvalho ◽  
Leonhard Knorr-Held
Keyword(s):  
Discrete Time ◽  
Survival Data ◽  
Random Slopes

scholarly journals A scalable discrete-time survival model for neural networks

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6257 ◽  
Author(s):  
Michael F. Gensheimer ◽  
Balasubramanian Narasimhan
Keyword(s):  
Neural Networks ◽  
Discrete Time ◽  
Survival Data ◽  
Real Data ◽  
Survival Model ◽  
Stochastic Gradient Descent ◽  
Likelihood Method ◽  
Learning Framework ◽  
Censoring Indicator

There is currently great interest in applying neural networks to prediction tasks in medicine. It is important for predictive models to be able to use survival data, where each patient has a known follow-up time and event/censoring indicator. This avoids information loss when training the model and enables generation of predicted survival curves. In this paper, we describe a discrete-time survival model that is designed to be used with neural networks, which we refer to as Nnet-survival. The model is trained with the maximum likelihood method using mini-batch stochastic gradient descent (SGD). The use of SGD enables rapid convergence and application to large datasets that do not fit in memory. The model is flexible, so that the baseline hazard rate and the effect of the input data on hazard probability can vary with follow-up time. It has been implemented in the Keras deep learning framework, and source code for the model and several examples is available online. We demonstrate the performance of the model on both simulated and real data and compare it to existing models Cox-nnet and Deepsurv.

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