Analysis of Survival Data with Dependent Censoring

2018 ◽  
Author(s):  
Takeshi Emura ◽  
Yi-Hau Chen
Keyword(s):  
Survival Data ◽  
Dependent Censoring

scholarly journals Targeted Minimum Loss-Based Estimation of Causal Effects in Right-Censored Survival Data with Time-Dependent Covariates: Warfarin, Stroke, and Death in Atrial Fibrillation

2013 ◽  
Vol 1 (2) ◽  
pp. 235-254 ◽  
Author(s):  
Jordan C. Brooks ◽  
Mark J. van der Laan ◽  
Daniel E. Singer ◽  
Alan S. Go
Keyword(s):  
Survival Data ◽  
Causal Effect ◽  
Estimating Equation ◽  
Dependent Censoring ◽  
Time Dependent ◽  
Causal Effects ◽  
Consistent Estimation ◽  
Censored Survival Data ◽  
Inverse Probability ◽  
Time Dependent Covariates

AbstractCausal effects in right-censored survival data can be formally defined as the difference in the marginal cumulative event probabilities under particular interventions. Conventional estimators, such as the Kaplan-Meier (KM), fail to consistently estimate these marginal parameters under dependent treatment assignment or dependent censoring. Several modern estimators have been developed that reduce bias under both dependent treatment assignment and dependent censoring by incorporating information from baseline and time-dependent covariates. In the present article we describe a recently developed targeted minimum loss-based estimation (TMLE) algorithm for general longitudinal data structures and present in detail its application in right-censored survival data with time-dependent covariates. The treatment-specific marginal cumulative event probability is defined via a series of iterated conditional expectations in a time-dependent counting process framework. The TMLE involves an initial estimator of each conditional expectation and sequentially updates these such that the resulting estimator solves the efficient influence curve estimating equation in the nonparametric statistical model. We describe the assumptions required for consistent estimation of statistical parameters and additional assumptions required for consistent estimation of the causal effect parameter. Using simulated right-censored survival data, the mean squared error, bias, and 95% confidence interval coverage probability of the TMLE is compared with those of the conventional KM and the inverse probability of censoring weight estimating equation, conventional maximum likelihood substitution estimator, and the double robustaugmented inverse probability of censoring weighted estimating equation. We conclude the article with estimation of the causal effect of warfarin medical therapy on the probability of “stroke or death” within a 1-year time frame using data from the ATRIA-1 observational cohort of persons with atrial fibrillation. Our results suggest that a fixed policy of warfarin treatment for all patients would result in 2% fewer deaths or strokes within 1-year as compared with a policy of withholding warfarin from all patients.

scholarly journals Flexible parametric model for survival data subject to dependent censoring

2019 ◽  
Vol 62 (1) ◽  
pp. 136-156 ◽  
Author(s):  
Negera Wakgari Deresa ◽  
Ingrid Van Keilegom
Keyword(s):  
Survival Data ◽  
Parametric Model ◽  
Dependent Censoring ◽  
Flexible Parametric Model ◽  
Data Subject

Survival forests for data with dependent censoring

2017 ◽  
Vol 28 (2) ◽  
pp. 445-461 ◽  
Author(s):  
Hoora Moradian ◽  
Denis Larocque ◽  
François Bellavance
Keyword(s):  
Survival Data ◽  
Survival Function ◽  
Real Data ◽  
Dependent Censoring ◽  
Right Censoring ◽  
True Time ◽  
Splitting Rule ◽  
The Individual ◽  
Individual Trees ◽  
Survival Forests

Tree-based methods are very powerful and popular tools for analysing survival data with right-censoring. The existing methods assume that the true time-to-event and the censoring times are independent given the covariates. We propose different ways to build survival forests when dependent censoring is suspected, by using an appropriate estimator of the survival function when aggregating the individual trees and/or by modifying the splitting rule. The appropriate estimator used in this paper is the copula-graphic estimator. We also propose a new method for building survival forests, called p-forest, that may be used not only when dependent censoring is suspected, but also as a new survival forest method in general. The results from a simulation study indicate that these modifications improve greatly the estimation of the survival function in situations of dependent censoring. A real data example illustrates how the proposed methods can be used to perform a sensitivity analysis.

scholarly journals Gene selection for survival data under dependent censoring: A copula-based approach

2016 ◽  
Vol 25 (6) ◽  
pp. 2840-2857 ◽  
Author(s):  
Takeshi Emura ◽  
Yi-Hau Chen
Keyword(s):  
Survival Data ◽  
Gene Selection ◽  
Cox Regression ◽  
Selection Procedure ◽  
Dependent Censoring ◽  
Small Cell Lung ◽  
Gene Expressions ◽  
Cancer Data ◽  
Independent Censoring ◽  
Selection For

Dependent censoring arises in biomedical studies when the survival outcome of interest is censored by competing risks. In survival data with microarray gene expressions, gene selection based on the univariate Cox regression analyses has been used extensively in medical research, which however, is only valid under the independent censoring assumption. In this paper, we first consider a copula-based framework to investigate the bias caused by dependent censoring on gene selection. Then, we utilize the copula-based dependence model to develop an alternative gene selection procedure. Simulations show that the proposed procedure adjusts for the effect of dependent censoring and thus outperforms the existing method when dependent censoring is indeed present. The non-small-cell lung cancer data are analyzed to demonstrate the usefulness of our proposal. We implemented the proposed method in an R “compound.Cox” package.

scholarly journals Model evaluation and variable selection for interval-censored data

2015 ◽  
Author(s):  
◽  
Tyler Cook
Keyword(s):  
Survival Analysis ◽  
Variable Selection ◽  
Survival Time ◽  
Censored Data ◽  
Survival Data ◽  
Failure Time ◽  
Dependent Censoring ◽  
Interval Censored Data ◽  
Interval Censored ◽  
Selection For

Survival analysis is a popular area of statistics dealing with time-to-event data. A special characteristic of survival data is the presence of censoring. Censoring occurs when the survival time is only partially known. In medical studies, censoring can be caused by patients dropping out of the study before their disease event occurs. This dissertation focuses on the analysis of interval-censored data, where the failure time is only known to belong to some interval of observation times. One problem researchers face when analyzing survival data is how to handle the censoring distribution. This is an important consideration because sometimes a patient's survival time is related to the time they drop out of the study. It is often assumed that these two times are unrelated, so special methods need to be developed when they are dependent. Part of this dissertation investigates the effectiveness of methods developed for interval-censored data with dependent censoring when the censoring is actually independent. The results of these simulation studies can provide guidelines for deciding between models when facing a practical problem where one is unsure about the dependence of the censoring distribution. Another important problem seen in survival analysis is variable selection. For example, doctors might want to identify a set of diagnostic tests or measurements that can predict patient survival. We propose an imputation approach for variable selection of interval-censored data that utilizes penalized likelihood procedures. This work is significant because researchers currently do not have many tools to select important variables related to the survival time for interval-censored data.

An approach to model clustered survival data with dependent censoring

2019 ◽  
Vol 62 (1) ◽  
pp. 157-174 ◽  
Author(s):  
Silvana Schneider ◽  
Fábio Nogueira Demarqui ◽  
Enrico Antônio Colosimo ◽  
Vinícius Diniz Mayrink
Keyword(s):  
Survival Data ◽  
Dependent Censoring ◽  
Clustered Survival Data

On semiparametric modelling, estimation and inference for survival data subject to dependent censoring

Biometrika ◽  
2020 ◽  
Author(s):  
N W Deresa ◽  
I Van Keilegom
Keyword(s):  
Linear Model ◽  
Survival Data ◽  
Estimation Method ◽  
Bivariate Normal Distribution ◽  
Dependent Censoring ◽  
Transformation Model ◽  
Model Parameters ◽  
Finite Sample ◽  
Asymptotically Normal ◽  
Nonparametric Transformation

Abstract When modelling survival data, it is common to assume that the survival time T is conditionally independent of the censoring time C given a set of covariates. However, there are numerous situations in which this assumption is not realistic. The goal of this paper is therefore to develop a semiparametric normal transformation model, which assumes that after a proper nonparametric monotone transformation, the vector (T, C) follows a linear model, and the vector of errors in this bivariate linear model follows a standard bivariate normal distribution with possibly non-diagonal covariance matrix. We show that this semiparametric model is identifiable, and propose estimators of the nonparametric transformation, the regression coefficients and the correlation between the error terms. It is shown that the estimators of the model parameters and the transformation are consistent and asymptotically normal. We also assess the finite sample performance of the proposed method by comparing it with an estimation method under a fully parametric model. Finally, our method is illustrated using data from the AIDS Clinical Trial Group 175 study.

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