scholarly journals Nonparametric Estimation of Cumulative Incidence Functions for Competing Risks Data with Missing Cause of Failure

2013 ◽  
Author(s):  
Georgios Effraimidis ◽  
Christian M. Dahl
Keyword(s):  
Competing Risks ◽  
Nonparametric Estimation ◽  
Cumulative Incidence ◽  
Cumulative Incidence Functions ◽  
Competing Risks Data ◽  
Cause Of Failure ◽  
Missing Cause Of Failure ◽  
Incidence Functions

scholarly journals The Estimation and Modeling of Cause-specific Cumulative Incidence Functions Using Time-dependent Weights

2017 ◽  
Vol 17 (1) ◽  
pp. 181-207 ◽  
Author(s):  
Paul C. Lambert
Keyword(s):  
Survival Analysis ◽  
Competing Risks ◽  
Cumulative Incidence ◽  
American Statistical Association ◽  
Survival Models ◽  
Cumulative Incidence Functions ◽  
Competing Risks Data ◽  
Incidence Functions ◽  
Competing Risk Models ◽  
Nonparametric Estimates

Competing risks occur in survival analysis when an individual is at risk of more than one type of event and one event's occurrence precludes another's. The cause-specific cumulative incidence function (CIF) is a measure of interest with competing-risks data. It gives the absolute (or crude) risk of having the event by time t, accounting for the fact that it is impossible to have the event if a competing event occurs first. The user-written command stcompet calculates nonparametric estimates of the cause-specific CIF, and the official Stata command stcrreg fits the Fine and Gray (1999, Journal of the American Statistical Association 94: 496–509) model for competing-risks data. Geskus (2011, Biometrics 67: 39–49) has recently shown that standard software can estimate some of the key measures in competing risks by restructuring the data and incorporating weights. This has a number of advantages because any tools developed for standard survival analysis can then be used to analyze competing-risks data. In this article, I describe the stcrprep command, which restructures the data and calculates the appropriate weights. After one uses stcrprep, a number of standard Stata survival analysis commands can then be used to analyze competing risks. For example, sts graph, failure will give a plot of the cause-specific CIF, and stcox will fit the Fine and Gray (1999) proportional subhazards model. Using stcrprep together with stcox is computationally much more efficient than using stcrreg. In addition, stcrprep opens up new opportunities for competing-risk models. I illustrate this by fitting flexible parametric survival models to the expanded data to directly model the cause-specific CIF.

scholarly journals Semiparametric regression and risk prediction with competing risks data under missing cause of failure

2020 ◽  
Vol 26 (4) ◽  
pp. 659-684 ◽  
Author(s):  
Giorgos Bakoyannis ◽  
Ying Zhang ◽  
Constantin T. Yiannoutsos
Keyword(s):  
Risk Prediction ◽  
Competing Risks ◽  
Cumulative Incidence ◽  
Semiparametric Regression ◽  
Missing At Random ◽  
Regression Coefficients ◽  
Cumulative Incidence Functions ◽  
Hazards Model ◽  
Cause Of Failure ◽  
Incidence Functions

Abstract The cause of failure in cohort studies that involve competing risks is frequently incompletely observed. To address this, several methods have been proposed for the semiparametric proportional cause-specific hazards model under a missing at random assumption. However, these proposals provide inference for the regression coefficients only, and do not consider the infinite dimensional parameters, such as the covariate-specific cumulative incidence function. Nevertheless, the latter quantity is essential for risk prediction in modern medicine. In this paper we propose a unified framework for inference about both the regression coefficients of the proportional cause-specific hazards model and the covariate-specific cumulative incidence functions under missing at random cause of failure. Our approach is based on a novel computationally efficient maximum pseudo-partial-likelihood estimation method for the semiparametric proportional cause-specific hazards model. Using modern empirical process theory we derive the asymptotic properties of the proposed estimators for the regression coefficients and the covariate-specific cumulative incidence functions, and provide methodology for constructing simultaneous confidence bands for the latter. Simulation studies show that our estimators perform well even in the presence of a large fraction of missing cause of failures, and that the regression coefficient estimator can be substantially more efficient compared to the previously proposed augmented inverse probability weighting estimator. The method is applied using data from an HIV cohort study and a bladder cancer clinical trial.

scholarly journals Estimating cumulative incidence functions in competing risks data with dependent left‐truncation

2019 ◽  
Vol 39 (4) ◽  
pp. 481-493
Author(s):  
Regina Stegherr ◽  
Arthur Allignol ◽  
Reinhard Meister ◽  
Christof Schaefer ◽  
Jan Beyersmann
Keyword(s):  
Competing Risks ◽  
Cumulative Incidence ◽  
Left Truncation ◽  
Cumulative Incidence Functions ◽  
Competing Risks Data ◽  
Incidence Functions

A quantile based test for comparing cumulative incidence functions of competing risks models

2010 ◽  
Vol 80 (9-10) ◽  
pp. 886-891 ◽  
Author(s):  
P.G. Sankaran ◽  
N. Unnikrishnan Nair ◽  
E.P. Sreedevi
Keyword(s):  
Competing Risks ◽  
Cumulative Incidence ◽  
Cumulative Incidence Functions ◽  
Incidence Functions
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