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

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

scholarly journals Additive Hazard Regression for the Analysis of Clustered Survival Data from Case-Cohort Studies

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
June Liu ◽  
Yi Zhang
Keyword(s):  
Cohort Studies ◽  
Survival Data ◽  
Failure Time ◽  
Weight Functions ◽  
Disease Rate ◽  
Failure Time Data ◽  
Finite Sample ◽  
Time Data ◽  
Cohort Design ◽  
Clustered Survival Data

The case-cohort design is an effective and economical method in large cohort studies, especially when the disease rate is low. Case-cohort design in most of the existing literature is mainly used to analyze the univariate failure time data. But in practice, multivariate failure time data are commonly encountered in biomedical research. In this paper, we will propose methods based on estimating equation method for case-cohort designs for clustered survival data. By introducing the event failure rate, three different weight functions are constructed. Then, three estimating equations and parameter estimators are presented. Furthermore, consistency and asymptotic normality of the proposed estimators are established. Finally, the simulation results show that the proposed estimation procedure has reasonable finite sample behaviors.

Rank tests for clustered survival data when dependent subunits are randomized

2005 ◽  
Vol 25 (3) ◽  
pp. 361-373 ◽  
Author(s):  
Jong-Hyeon Jeong ◽  
Sin-Ho Jung
Keyword(s):  
Survival Data ◽  
Rank Tests ◽  
Clustered Survival Data
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