Calibrated predictions for multivariate competing risks models

Gorfine, Malka; Hsu, Li; Zucker, David M.; Parmigiani, Giovanni

doi:10.1007/s10985-013-9260-x

Cited by 12 publications

(10 citation statements)

References 19 publications

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“…To our knowledge, while the literature on the related competing risks problem has considered methods for cluster-correlated data settings (Katsahian et al, 2006; Chen et al, 2008; Gorfine and Hsu, 2011; Zhou et al, 2012; Gorfine et al, 2014), only one paper on the analysis of cluster-correlated semi-competing risks data has been published. Specifically, Liquet et al (2012) recently proposed a multi-state model that incorporated a hospital-specific random effect to account for cluster-correlation.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Models for Semicompeting Risks Data With Application to Quality of End-of-Life Care for Pancreatic Cancer

Lee¹,

Dominici

Schrag

et al. 2016

Journal of the American Statistical Association

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Readmission following discharge from an initial hospitalization is a key marker of quality of health care in the United States. For the most part, readmission has been studied among patients with ‘acute’ health conditions, such as pneumonia and heart failure, with analyses based on a logistic-Normal generalized linear mixed model (Normand et al., 1997). Naïve application of this model to the study of readmission among patients with ‘advanced’ health conditions such as pancreatic cancer, however, is problematic because it ignores death as a competing risk. A more appropriate analysis is to imbed such a study within the semi-competing risks framework. To our knowledge, however, no comprehensive statistical methods have been developed for cluster-correlated semi-competing risks data. To resolve this gap in the literature we propose a novel hierarchical modeling framework for the analysis of cluster-correlated semi-competing risks data that permits parametric or non-parametric specifications for a range of components giving analysts substantial flexibility as they consider their own analyses. Estimation and inference is performed within the Bayesian paradigm since it facilitates the straightforward characterization of (posterior) uncertainty for all model parameters, including hospital-specific random effects. Model comparison and choice is performed via the deviance information criterion and the log-pseudo marginal likelihood statistic, both of which are based on a partially marginalized likelihood. An efficient computational scheme, based on the Metropolis-Hastings-Green algorithm, is developed and had been implemented in the package. A comprehensive simulation study shows that the proposed framework performs very well in a range of data scenarios, and outperforms competitor analysis strategies. The proposed framework is motivated by and illustrated with an on-going study of the risk of readmission among Medicare beneficiaries diagnosed with pancreatic cancer. Using data on n=5,298 patients at J=112 hospitals in the six New England states between 2000–2009, key scientific questions we consider include the role of patient-level risk factors on the risk of readmission and the extent of variation in risk across hospitals not explained by differences in patient case-mix.

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Section: Introductionmentioning

confidence: 99%

Hierarchical Models for Semicompeting Risks Data With Application to Quality of End-of-Life Care for Pancreatic Cancer

Lee¹,

Dominici

Schrag

et al. 2016

Journal of the American Statistical Association

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“…The mean of SHARP-D score in the overall population was 2.8 ± 2.2 points (range − 4 to 11). The Wolber’s concordance index was 0.65 (0.63–0.68, p < 0.001) for the score, suggestive of good discrimination [22, 23]. The calibration of the model was assessed graphically by comparing the predicted probability of AF to the observed probability of AF at the end of follow-up across 10 deciles of predicted risk (Fig.…”

Section: Resultsmentioning

confidence: 99%

A risk score for predicting atrial fibrillation in individuals with preclinical diastolic dysfunction: a retrospective study in a single large urban center in the United States

Quispe

Madan

et al. 2019

BMC Cardiovasc Disord

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Background Left ventricular diastolic dysfunction has been shown to associate with increased risk of atrial fibrillation (AF). We aimed to examine the predictors of AF in individuals with preclinical diastolic dysfunction (PDD) - diastolic dysfunction without clinical heart failure – and develop a risk score in this population. Methods Patients underwent echocardiogram from December 2009 to December 2015 showing left ventricular ejection fraction (LVEF) ≥ 50% and grade 1 diastolic dysfunction, without clinical heart failure, valvular heart disease or AF were included. Outcome was defined as new onset AF. Cumulative probabilities were estimated and multivariable adjusted competing-risks regression analysis was performed to examine predictors of incident AF. A predictive score model was constructed. Results A total of 9591 PDD patients (mean age 66, 41% men) of racial/ethnical diversity were included in the study. During a median follow-up of 54 months, 455 (4.7%) patients developed AF. Independent predictors of AF included advanced age, male sex, race, hypertension, diabetes, and peripheral artery disease. A risk score including these factors showed a Wolber’s concordance index of 0.65 (0.63–0.68, p < 0.001), suggesting a good discrimination. Conclusions Our study revealed a set of predictors of AF in PDD patients. A simple risk score predicting AF in PDD was developed and internally validated. The scoring system could help clinical risk stratification, which may lead to prevention and early treatment strategies. Electronic supplementary material The online version of this article (10.1186/s12872-019-1024-4) contains supplementary material, which is available to authorized users.

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“…However, when additional information on covariates is available, the frailty models can be extended to include them in a straightforward way. Another important extension is the consideration of competing risks and multiple disease outcomes, and can be found in Gorfine et al (2013).…”

Section: Discussionmentioning

confidence: 99%

Frailty Models for Familial Risk With Application to Breast Cancer

Gorfine

Hsu

Parmigiani

2013

Journal of the American Statistical Association

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In evaluating familial risk for disease we have two main statistical tasks: assessing the probability of carrying an inherited genetic mutation conferring higher risk; and predicting the absolute risk of developing diseases over time, for those individuals whose mutation status is known. Despite substantial progress, much remains unknown about the role of genetic and environmental risk factors, about the sources of variation in risk among families that carry high-risk mutations, and about the sources of familial aggregation beyond major Mendelian effects. These sources of heterogeneity contribute substantial variation in risk across families. In this paper we present simple and efficient methods for accounting for this variation in familial risk assessment. Our methods are based on frailty models. We implemented them in the context of generalizing Mendelian models of cancer risk, and compared our approaches to others that do not consider heterogeneity across families. Our extensive simulation study demonstrates that when predicting the risk of developing a disease over time conditional on carrier status, accounting for heterogeneity results in a substantial improvement in the area under the curve of the receiver operating characteristic. On the other hand, the improvement for carriership probability estimation is more limited. We illustrate the utility of the proposed approach through the analysis of BRCA1 and BRCA2 mutation carriers in the Washington Ashkenazi Kin-Cohort Study of Breast Cancer.

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Calibrated predictions for multivariate competing risks models

Cited by 12 publications

References 19 publications

Hierarchical Models for Semicompeting Risks Data With Application to Quality of End-of-Life Care for Pancreatic Cancer

Hierarchical Models for Semicompeting Risks Data With Application to Quality of End-of-Life Care for Pancreatic Cancer

A risk score for predicting atrial fibrillation in individuals with preclinical diastolic dysfunction: a retrospective study in a single large urban center in the United States

Frailty Models for Familial Risk With Application to Breast Cancer

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