Covariate adjustment of cumulative incidence functions for competing risks data using inverse probability of treatment weighting

2016 ◽  
Vol 129 ◽  
pp. 63-70 ◽  
Author(s):  
Anke Neumann ◽  
Cécile Billionnet
Keyword(s):  
Competing Risks ◽  
Cumulative Incidence ◽  
Covariate Adjustment ◽  
Inverse Probability ◽  
Cumulative Incidence Functions ◽  
Competing Risks Data ◽  
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 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

scholarly journals A competing risks analysis should report results on all cause-specific hazards and cumulative incidence functions

2013 ◽  
Vol 66 (6) ◽  
pp. 648-653 ◽  
Author(s):  
Aurelien Latouche ◽  
Arthur Allignol ◽  
Jan Beyersmann ◽  
Myriam Labopin ◽  
Jason P. Fine
Keyword(s):  
Competing Risks ◽  
Cumulative Incidence ◽  
Cumulative Incidence Functions ◽  
Competing Risks Analysis ◽  
Incidence Functions
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