Interpretability and importance of functionals in competing risks and multistate models

Andersen, Per Kragh; Keiding, Niels

doi:10.1002/sim.4385

Cited by 178 publications

(201 citation statements)

References 44 publications

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“…However, a plot of ˆ( ) H t against t can provide useful information in that its local slope approximates the ( ) h t (7). Survival function [ˆ( ) S t ] can be estimated nonparametrically using Kaplan-Meier estimator, and the ˆ( ) H t can be estimated using Nelson-Aalen estimator.…”

Section: Key Concepts In Survival Analysis With and Without Competingmentioning

confidence: 99%

“…The former considers competing risk events as non-informative censoring, whereas the latter takes into account the informative censoring nature of the competing risk events (1 As you can see, the estimated coefficient for cause 1 deviates a little from that obtained from cause-specific hazard model (HR: 1.87 vs. 1.94), reflecting different assumptions for the competing risks. The numerical values derived from Fine-Gray model have no simple interpretation, but it reflects the ordering of cumulative incidence curves (7,9). The cause-specific hazard is the rate of cause 1 failure per time unit for patients who are still alive.…”

Section: Subdistribution Hazards (Shs) Modelmentioning

confidence: 99%

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Survival analysis in the presence of competing risks

Zhang

2017

Ann. Transl. Med.

125

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in 2009, receiving Master Degree. His major research interests include hemodynamic monitoring in sepsis and septic shock, delirium, and outcome study for critically ill patients. He is experienced in data management and statistical analysis by using R and STATA, big data exploration, systematic review and meta-analysis. He has published more than 50 academic papers (science citation indexed) that have been cited for over 700 times. He has been appointed as reviewer for 10 journals, Abstract: Survival analysis in the presence of competing risks imposes additional challenges for clinical investigators in that hazard function (the rate) has no one-to-one link to the cumulative incidence function (CIF, the risk). CIF is of particular interest and can be estimated non-parametrically with the use cuminc() function. This function also allows for group comparison and visualization of estimated CIF. The effect of covariates on cause-specific hazard can be explored using conventional Cox proportional hazard model by treating competing events as censoring. However, the effect on hazard cannot be directly linked to the effect on CIF because there is no one-to-one correspondence between hazard and cumulative incidence. FineGray model directly models the covariate effect on CIF and it reports subdistribution hazard ratio (SHR).However, SHR only provide information on the ordering of CIF curves at different levels of covariates, it has no practical interpretation as HR in the absence of competing risks. Fine-Gray model can be fit with crr() function shipped with the cmprsk package. Time-varying covariates are allowed in the crr() function, which is specified by cov2 and tf arguments. Predictions and visualization of CIF for subjects with given covariate values are allowed for crr object. Alternatively, competing risk models can be fit with riskRegression package by employing different link functions between covariates and outcomes. The assumption of proportionality can be checked by testing statistical significance of interaction terms involving failure time. Schoenfeld residuals provide another way to check model assumption. Zhang. Survival analysis in the presence of competing risks

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Section: Key Concepts In Survival Analysis With and Without Competingmentioning

confidence: 99%

Section: Subdistribution Hazards (Shs) Modelmentioning

confidence: 99%

Survival analysis in the presence of competing risks

Zhang

2017

Ann. Transl. Med.

125

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“…Our model relies on a mixture factorization that factorizes the CIF into a product of the marginal probability of the event type j and the probability of surviving up to time t conditional on eventually experiencing an event of type j :

P (T ⩽ t, D = j) = P (T ⩽ t | D = j) P (D = j)

This factorization has been used by several authors 12, 13, 14. However, it has also been criticized because the marginal event probabilities, P ( D = j ), are poorly identified for data with a limited follow‐up duration relative to the timing of events, and because of interpretational issues due to conditioning on the future event type D 15. Here, we only use this factorization as a convenient mathematical tool for model formulation and focus our attention on CIF estimation over the observed follow‐up period.…”

Section: A Mixture Model For the Cumulative Incidence Function Based mentioning

confidence: 99%

Smooth semi‐nonparametric (SNP) estimation of the cumulative incidence function

Duc

Wolbers

2017

Statistics in Medicine

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This paper presents a novel approach to estimation of the cumulative incidence function in the presence of competing risks. The underlying statistical model is specified via a mixture factorization of the joint distribution of the event type and the time to the event. The time to event distributions conditional on the event type are modeled using smooth semi‐nonparametric densities. One strength of this approach is that it can handle arbitrary censoring and truncation while relying on mild parametric assumptions. A stepwise forward algorithm for model estimation and adaptive selection of smooth semi‐nonparametric polynomial degrees is presented, implemented in the statistical software R, evaluated in a sequence of simulation studies, and applied to data from a clinical trial in cryptococcal meningitis. The simulations demonstrate that the proposed method frequently outperforms both parametric and nonparametric alternatives. They also support the use of ‘ad hoc’ asymptotic inference to derive confidence intervals. An extension to regression modeling is also presented, and its potential and challenges are discussed. © 2017 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

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“…This condition violates major epidemiological principles for analysis of such data. 6 Because C. difficile infections chiefly influence length of stay, which is a major driver of costs, the estimates likely substantially underestimate the true effect. 7 In addition, these authors failed to consider cost clustering within main diagnosis group, and they only adjusted for a limited set of main diagnosis and comorbidities.…”

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confidence: 99%