Association analysis of successive events data in the presence of competing risks

2016 ◽  
Vol 27 (6) ◽  
pp. 1661-1682
Author(s):  
Xiaotian Chen ◽  
Yu Cheng ◽  
Ellen Frank ◽  
David J Kupfer
Keyword(s):  
Bipolar Disorder ◽  
Cumulative Incidence ◽  
Manic Episode ◽  
Cumulative Incidence Function ◽  
Bivariate Distribution ◽  
Dependent Censoring ◽  
Competing Risk ◽  
Incidence Function ◽  
Event Times ◽  
Events Data

We aim to close a methodological gap in analyzing durations of successive events that are subject to induced dependent censoring as well as competing-risk censoring. In the Bipolar Disorder Center for Pennsylvanians study, some patients who managed to recover from their symptomatic entry later developed a new depressive or manic episode. It is of great clinical interest to quantify the association between time to recovery and time to recurrence in patients with bipolar disorder. The estimation of the bivariate distribution of the gap times with independent censoring has been well studied. However, the existing methods cannot be applied to failure times that are censored by competing causes such as in the Bipolar Disorder Center for Pennsylvanians study. Bivariate cumulative incidence function has been used to describe the joint distribution of parallel event times that involve multiple causes. To the best of our knowledge, however, there is no method available for successive events with competing-risk censoring. Therefore, we extend the bivariate cumulative incidence function to successive events data, and propose non-parametric estimators of the bivariate cumulative incidence function and the related conditional cumulative incidence function. Moreover, an odds ratio measure is proposed to describe the cause-specific dependence, leading to the development of a formal test for independence of successive events. Simulation studies demonstrate that the estimators and tests perform well for realistic sample sizes, and our methods can be readily applied to the Bipolar Disorder Center for Pennsylvanians study.

Dynamic prediction of competing risk events using landmark sub-distribution hazard model with multiple longitudinal biomarkers

2020 ◽  
Vol 29 (11) ◽  
pp. 3179-3191
Author(s):  
Cai Wu ◽  
Liang Li ◽  
Ruosha Li
Keyword(s):  
Kidney Disease ◽  
Cumulative Incidence ◽  
Disease Risk ◽  
Predictor Variable ◽  
Cumulative Incidence Function ◽  
Competing Risk ◽  
Model Parameters ◽  
Dynamic Prediction ◽  
Risk Of Death ◽  
Incidence Function

The cause-specific cumulative incidence function quantifies the subject-specific disease risk with competing risk outcome. With longitudinally collected biomarker data, it is of interest to dynamically update the predicted cumulative incidence function by incorporating the most recent biomarker as well as the cumulating longitudinal history. Motivated by a longitudinal cohort study of chronic kidney disease, we propose a framework for dynamic prediction of end stage renal disease using multivariate longitudinal biomarkers, accounting for the competing risk of death. The proposed framework extends the local estimation-based landmark survival modeling to competing risks data, and implies that a distinct sub-distribution hazard regression model is defined at each biomarker measurement time. The model parameters, prediction horizon, longitudinal history and at-risk population are allowed to vary over the landmark time. When the measurement times of biomarkers are irregularly spaced, the predictor variable may not be observed at the time of prediction. Local polynomial is used to estimate the model parameters without explicitly imputing the predictor or modeling its longitudinal trajectory. The proposed model leads to simple interpretation of the regression coefficients and closed-form calculation of the predicted cumulative incidence function. The estimation and prediction can be implemented through standard statistical software with tractable computation. We conducted simulations to evaluate the performance of the estimation procedure and predictive accuracy. The methodology is illustrated with data from the African American Study of Kidney Disease and Hypertension.

scholarly journals Cause of Death Among Patients With Peripheral Artery Disease

2020 ◽  
Vol 13 (11) ◽  
Author(s):  
Ajar Kochar ◽  
Hillary Mulder ◽  
Frank W. Rockhold ◽  
Iris Baumgartner ◽  
Jeffrey S. Berger ◽  
...  
Keyword(s):  
Cause Of Death ◽  
Peripheral Artery Disease ◽  
Cumulative Incidence ◽  
Causes Of Death ◽  
Cardiovascular Death ◽  
Cumulative Incidence Function ◽  
Competing Risk ◽  
Peripheral Artery ◽  
Incidence Function ◽  
Artery Disease

Background: Peripheral artery disease is common and associated with high mortality. There are limited data detailing causes of death among patients with peripheral artery disease. Methods: EUCLID (Examining Use of Ticagrelor in Peripheral Artery Disease) was a randomized clinical trial that assigned patients with peripheral artery disease to clopidogrel or ticagrelor. We describe the causes of death in EUCLID using mortality end points adjudicated through a clinical events classification process. The association between baseline factors and cardiovascular death was evaluated by Cox proportional hazards modeling. The competing risk of noncardiovascular death was assessed by the cumulative incidence function for cardiovascular death and the Fine and Gray method to ascertain the association between baseline characteristics and cardiovascular mortality. Results: A total of 1263 out of 13 885 (9.1%) patients died (median follow-up: 30 months). There were 706 patients (55.9%) with a cardiovascular cause of death and 522 (41.3%) with a noncardiovascular cause of death. The most common cause of cardiovascular death was sudden cardiac death (20.1%); while myocardial infarction (5.2%) and ischemic stroke (3.2%) were uncommon. The most common causes of noncardiovascular death were malignancies (17.9%) and infections (11.9%). The factor most associated with a higher risk of cardiovascular death was age per 5 year increase (HR, 1.26 [95% CI, 1.20–1.32]). Female sex was associated with a lower risk of cardiovascular death (HR, 0.68 [95% CI, 0.56–0.82]). To evaluate the effect of noncardiovascular death as a competing risk, we superimposed the cumulative incidence function curve with the Kaplan-Meier curve. These curves closely approximated each other. After accounting for the competing risk of noncardiovascular death, the magnitude and direction of the factors associated with cardiovascular death were minimally changed. Conclusions: Among patients with symptomatic peripheral artery disease, noncardiovascular causes of death reflected a high proportion (40%) of deaths. Accounting for noncardiovascular deaths as a competing risk, there was not a significant change in the risk estimation for cardiovascular death. Registration: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT01732822.

scholarly journals Analysis of Survival Data with Competing Risks in ADRD (Alzheimer's Disease and Related Dementias) Research

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 645-645
Author(s):  
Dianxu Ren ◽  
Oscar Lopez ◽  
Jennifer Lingler
Keyword(s):  
At Risk ◽  
Competing Risks ◽  
Cumulative Incidence ◽  
Hazard Model ◽  
Cumulative Incidence Function ◽  
Competing Risk ◽  
Subdistribution Hazard ◽  
Incidence Function ◽  
Diabetes Status ◽  
Conventional Methods

Abstract Competing risk is an event that precludes the occurrence of the primary event of interest. For example, when studying risk factors associated with dementia, death before the onset of dementia serve as a competing event. A subject who dies is no longer at risk of dementia. This issue play more important role in ADRD research given the elderly population. Conventional methods for survival analysis assume independent censoring and ignore the competing events. However, there are some challenge issues using those conventional methods in the presence of competing risks. First, no one-to-one link between hazard function and cumulative incidence function (CIF), and Kaplan-Meier approach overestimates the cumulative incidence of the event of interest. Second, the effect of covariates on hazard rate cannot be directly linked to the effect of cumulative incidence (the risk). We will discuss two types of analyses in the presence of competing risk: Cause-specific hazard model and Fine-Gray subdistribution hazard model. Cause-specific hazard model directly quantify the cause-specific hazard among subjects who are at risk of developing the event of interest, while Fine-Gray subdistribution hazard model directly model the effects of covariates on the cumulative incidence function. The type of research questions (Association vs. Prediction) may guide the choice of different statistical approaches. We will illustrate those two competing risk analyses using the large national dataset from National Alzheimer’s Coordinating Center (NACC). We will analyze the association between baseline diabetes status and the incidence of dementia, in which death before the onset of dementia is a competing event.

scholarly journals Cumulative Incidence Function in Studies on the Duration of the Unemployment Exit Process

2017 ◽  
Vol 17 (1) ◽  
pp. 138-150 ◽  
Author(s):  
Beata Bieszk-Stolorz
Keyword(s):  
Old Age ◽  
Cumulative Incidence ◽  
Cumulative Incidence Function ◽  
Further Education ◽  
Competing Risk ◽  
Individual Data ◽  
Incidence Function ◽  
Different Types ◽  
Censored Observations ◽  
The Individual

Abstract When we analyse the employment seeking process, an event that ends the observation of a given individual is their employment. The remaining observations are considered to be censored: the observations concluded before the end of the study or the cases of deregistering for other causes (e.g. old-age pension, taking up residence in a foreign country, starting further education). The act of taking up income-generating work can take various forms: taking up a job, setting up a business or taking advantage of subsidised job programmes. Jobseekers are often deregistered from poviat labour offices because they refuse to take up an offered job or fail to report to the office in due time. All the above events are forms of competing risk. The purpose of this paper is to use the cumulative incidence function to assess the probability of the unemployment exit with regard to different types of the competing risk. When competing-risk events occur, a solution sometimes is used where the remaining endpoint events are considered censored observations. Such a solution leads to an overestimation of probability. The results implicate that the beneficiaries’ will to find employment was not a principal reason for a registering decision. The study is based on the individual data of jobseekers registered in the Poviat Labour Office in Szczecin.

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