scholarly journals Summarizing differences in cumulative incidence functions

2008 ◽  
Vol 27 (24) ◽  
pp. 4939-4949 ◽  
Author(s):  
Mei-Jie Zhang ◽  
Jason Fine
Keyword(s):  
Cumulative Incidence ◽  
Cumulative Incidence Functions ◽  
Incidence Functions

Constrained parametric model for simultaneous inference of two cumulative incidence functions

2012 ◽  
Vol 55 (1) ◽  
pp. 82-96 ◽  
Author(s):  
Haiwen Shi ◽  
Yu Cheng ◽  
Jong-Hyeon Jeong
Keyword(s):  
Cumulative Incidence ◽  
Parametric Model ◽  
Simultaneous Inference ◽  
Cumulative Incidence Functions ◽  
Incidence Functions

Empirical Likelihood Based Test for Equality of Cumulative Incidence Functions

2020 ◽  
Vol 21 (2) ◽  
pp. 427-436
Author(s):  
Asokan Mulayath Variyath ◽  
P. G. Sankaran
Keyword(s):  
Empirical Likelihood ◽  
Cumulative Incidence ◽  
Cumulative Incidence Functions ◽  
Incidence Functions

New-onset congestive heart failure (CHF) and cardiovascular disease (CVD) in older colorectal cancer (CRC) survivors: A population-based study.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 10011-10011
Author(s):  
Kelly Kenzik ◽  
Courtney Balentine ◽  
Smita Bhatia ◽  
Grant Richard Williams
Keyword(s):  
Cumulative Incidence ◽  
Cox Regression ◽  
The Elderly ◽  
Population Based ◽  
Diabetic Patients ◽  
Cumulative Incidence Functions ◽  
Increased Risk ◽  
History Of ◽  
Incidence Functions ◽  
New Onset

10011 Background: CRC is primarily a disease of the elderly. The high burden of pre-existing comorbidities alone or in concert with cancer treatment place the older patients with CRC at increased risk of new-onset morbidities, specifically, CVD and CHF. However, the magnitude of risk of new-onset morbidity, and its association with pre-existing comorbidities or treatment remain unknown. Methods: Using SEER-Medicare data, we evaluated individuals diagnosed with incident stage I-III CRC at age ≥66y between 1/1/2000 and 12/31/2011 who had survived ≥2y after diagnosis (n = 57,256; 77% with colon cancer). We compared these to an age, sex-, and race-frequency matched comparison group of non-cancer Medicare patients (n = 104,731). We evaluated new-onset CHF and CVD using competing risk cumulative incidence functions and multivariable Cox regression models. Results: The median age at diagnosis was 77y (66-106y); 45% males; and 85% non-Hispanic white. Median follow-up was 8y (2-14y) from diagnosis of CRC. Treatment included surgery for 99%, chemotherapy for 31%, and radiation for 12%. New-onset morbidity: The 10y cumulative incidence of new-onset CHF and CVD were 43.6% and 58.9%, respectively. After controlling for pre-cancer comorbidities, CRC survivors were at increased risk of new-onset CHF (HR 1.29) and CVD (HR 1.74) (all p < 0.001) compared to controls. Patients receiving radiation (HR 1.29) or 5-FU+oxaliplatin (HR 1.09) were at increased risk of CVD compared to those without those therapies (p < 0.001). Pre-existing diabetes (HR 1.16) and CHF (HR 1.21) independently increased the risk of CVD (p < 0.001). While 5FU+oxaliplatin did not increase the risk of CHF independently (HR 0.97), diabetic patients treated with 5-FU+oxaliplatin were at 1.71-fold increased risk of developing CHF (p < 0.001) when compared with those without pre-existing diabetes. Conclusions: Older CRC survivors are at increased of developing CHF and CVD. Monitoring survivors with a history of exposure to 5FU+oxaliplatin or radiation, and improving management of pre-existing comorbidities may reduce the burden of long-term morbidity for older CRC survivors.

Two-sample tests of the equality of two cumulative incidence functions

2007 ◽  
Vol 51 (9) ◽  
pp. 4269-4281 ◽  
Author(s):  
Ruta Bajorunaite ◽  
John P. Klein
Keyword(s):  
Cumulative Incidence ◽  
Cumulative Incidence Functions ◽  
Incidence Functions

scholarly journals Tests for Comparing Mark-Specific Hazards and Cumulative Incidence Functions

2004 ◽  
Vol 10 (1) ◽  
pp. 5-28 ◽  
Author(s):  
Peter B. Gilbert ◽  
Ian W. Mckeague ◽  
Yanqing Sun
Keyword(s):  
Cumulative Incidence ◽  
Cumulative Incidence Functions ◽  
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

scholarly journals Estimation of cumulative incidence functions in competing risks studies under an order restriction

2004 ◽  
Vol 118 (1-2) ◽  
pp. 145-165 ◽  
Author(s):  
Hammou El Barmi ◽  
Subhash C. Kochar ◽  
Hari Mukerjee ◽  
Francisco J. Samaniego
Keyword(s):  
Competing Risks ◽  
Cumulative Incidence ◽  
Order Restriction ◽  
Cumulative Incidence Functions ◽  
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

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.

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