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Grant Details

Grant Number: 1R01CA181360-01 Interpret this number
Primary Investigator: Haneuse, Sebastien
Organization: Harvard School Of Public Health
Project Title: Clustered Semi-Competing Risks Analysis in Quality of End-of-Life Care Studies
Fiscal Year: 2014


DESCRIPTION: A recent Institute of Medicine report highlighted the pressing need to control health care costs in the US without sacrificing quality of care. As the largest payer of health car costs, the Centers for Medicare and Medicaid Services (CMS) conducts comprehensive national efforts to monitor quality of care. However, these efforts focus on acute conditions for which cure rates are high and mortality low. For a broad range of increasingly prevalent 'advanced health conditions', such cancer and Alzheimer's disease, cure rates are low, short-term mortality is high and the focus of disease management is end-of-life (EOL) palliative care. Such care is expensive, however. In 2010 national cost of cancer care was estimated to be $125 billion. Despite these huge costs, there are no comprehensive national efforts to monitor quality of EOL care. A key barrier to these efforts is the lack of appropriate statistical methodology. To estimat hospital-specific readmission rates, CMS currently uses a logistic-Normal generalized linear mixed model (GLMM). However, this model ignores death as a truncating event. As such, na1¿ ve application of the current CMS approach for quality of EOL assessments for advanced health conditions is inappropriate, would likely lead to bias and could have a major impact on how hospitals are rewarded/penalized for excellent/poor quality of care. In the statistics literature, he study of a non-terminal event (e.g. readmission) that is subject to a terminal event (e.g. death) i known as the 'semi-competing risks' problem. Current national quality of care assessment efforts ignore the semi-competing risks problem. A major contributing factor is that clustered semi-competing risks data has not been considered in the statistical literature. Novel statistical methods for semi-competing risks data must, therefore, be developed and evaluated. We will develop a comprehensive, unified Bayesian analysis framework for semi-competing risks data. The proposed framework will permit researchers to take advantage of the numerous benefits afforded within the Bayesian paradigm. A crucial contribution will be the development of novel Bayesian hierarchical models for repeated measures semi- competing data, where individuals are clustered within hospitals. Novel multivariate hospital-level measures that jointly accommodate non-terminal and terminal events over time will be developed, as will methods for estimation, inference, ranking and the identification of excellent/poor hospitals. Finally, using data on all Medicare enrollees from 2000-2010 and tumor data from SEER-Medicare, we will apply our methods to quality of EOL care for cancers of the pancreas, lung, colon and brain. The proposed work will immediately and substantially improve and expand the set of statistical tools use for EOL care quality assessments, as well as provide key epidemiological results on cancer care in the US. The methods will be broadly applicable to all advanced health conditions, beyond cancer, many of which directly affect large segments of an increasingly aging US population.


Authors: Haneuse S. , Schrag D. , Dominici F. , Normand S.L. , Lee K.H. .
Source: The annals of applied statistics, 2022 Sep; 16(3), p. 1586-1607.
EPub date: 2022-07-19.
PMID: 36483542
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Modeling semi-competing risks data as a longitudinal bivariate process.
Authors: Nevo D. , Blacker D. , Larson E.B. , Haneuse S. .
Source: Biometrics, 2022 Sep; 78(3), p. 922-936.
EPub date: 2021-05-18.
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Fitting a shared frailty illness-death model to left-truncated semi-competing risks data to examine the impact of education level on incident dementia.
Authors: Lee C. , Gilsanz P. , Haneuse S. .
Source: BMC medical research methodology, 2021-01-11; 21(1), p. 18.
EPub date: 2021-01-11.
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Estimation and inference for semi-competing risks based on data from a nested case-control study.
Authors: Jazić I. , Lee S. , Haneuse S. .
Source: Statistical methods in medical research, 2020 Nov; 29(11), p. 3326-3339.
EPub date: 2020-06-17.
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Invited Commentary: Opportunities That Come With Studying the Co-Occurrence of Multiple Outcomes.
Authors: Haneuse S. , Schrag D. , Nevo D. .
Source: American journal of epidemiology, 2020-09-01; 189(9), p. 982-984.
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Time-to-event analysis when the event is defined on a finite time interval.
Authors: Lee C. , Lee S.J. , Haneuse S. .
Source: Statistical methods in medical research, 2020 Jun; 29(6), p. 1573-1591.
EPub date: 2019-08-22.
PMID: 31436136
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Design and analysis of nested case-control studies for recurrent events subject to a terminal event.
Authors: Jazić I. , Haneuse S. , French B. , MacGrogan G. , Rondeau V. .
Source: Statistics in medicine, 2019-09-30; 38(22), p. 4348-4362.
EPub date: 2019-07-09.
PMID: 31290191
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SemiCompRisks: An R Package for the Analysis of Independent and Cluster-correlated Semi-competing Risks Data.
Authors: Alvares D. , Haneuse S. , Lee C. , Lee K.H. .
Source: The R journal, 2019 Jun; 11(1), p. 376-400.
EPub date: 2019-08-20.
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Discussion on "Time-dynamic profiling with application to hospital readmission among patients on dialysis," by Jason P. Estes, Danh V. Nguyen, Yanjun Chen, Lorien S. Dalrymple, Connie M. Rhee, Kamyar Kalantar-Zadeh, and Damla Senturk.
Authors: Haneuse S. , Zubizarreta J. , Normand S.T. .
Source: Biometrics, 2018 Dec; 74(4), p. 1395-1397.
EPub date: 2018-06-05.
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Assessment of Between-Hospital Variation in Readmission and Mortality After Cancer Surgical Procedures.
Authors: Haneuse S. , Dominici F. , Normand S.L. , Schrag D. .
Source: JAMA network open, 2018-10-05; 1(6), p. e183038.
EPub date: 2018-10-05.
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Prediction of absolute risk of acute graft-versus-host disease following hematopoietic cell transplantation.
Authors: Lee C. , Haneuse S. , Wang H.L. , Rose S. , Spellman S.R. , Verneris M. , Hsu K.C. , Fleischhauer K. , Lee S.J. , Abdi R. .
Source: PloS one, 2018; 13(1), p. e0190610.
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Long-term viral suppression and immune recovery during first-line antiretroviral therapy: a study of an HIV-infected adult cohort in Hanoi, Vietnam.
Authors: Tanuma J. , Matsumoto S. , Haneuse S. , Cuong D.D. , Vu T.V. , Thuy P.T.T. , Dung N.T. , Dung N.T.H. , Trung N.V. , Kinh N.V. , et al. .
Source: Journal of the International AIDS Society, 2017 Dec; 20(4), .
PMID: 29211347
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Accelerated failure time models for semi-competing risks data in the presence of complex censoring.
Authors: Lee K.H. , Rondeau V. , Haneuse S. .
Source: Biometrics, 2017 Dec; 73(4), p. 1401-1412.
EPub date: 2017-04-10.
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A Semi-parametric Transformation Frailty Model for Semi-competing Risks Survival Data.
Authors: Jiang F. , Haneuse S. .
Source: Scandinavian journal of statistics, theory and applications, 2017 Mar; 44(1), p. 112-129.
EPub date: 2016-08-31.
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Commentary: Multiple Causes of Death: The Importance of Substantive Knowledge in the Big Data Era.
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Beyond Composite Endpoints Analysis: Semicompeting Risks as an Underutilized Framework for Cancer Research.
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Semi-Competing Risks Data Analysis: Accounting for Death as a Competing Risk When the Outcome of Interest Is Nonterminal.
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Source: Circulation. Cardiovascular quality and outcomes, 2016 May; 9(3), p. 322-31.
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Mitigating Bias in Generalized Linear Mixed Models: The Case for Bayesian Nonparametrics.
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Source: Statistical science : a review journal of the Institute of Mathematical Statistics, 2016 Feb; 31(1), p. 80-95.
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Incidence of AIDS-Defining Opportunistic Infections and Mortality during Antiretroviral Therapy in a Cohort of Adult HIV-Infected Individuals in Hanoi, 2007-2014.
Authors: Tanuma J. , Lee K.H. , Haneuse S. , Matsumoto S. , Nguyen D.T. , Nguyen D.T. , Do C.D. , Pham T.T. , Nguyen K.V. , Oka S. .
Source: PloS one, 2016; 11(3), p. e0150781.
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Hierarchical models for semi-competing risks data with application to quality of end-of-life care for pancreatic cancer.
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Source: Journal of the American Statistical Association, 2016; 111(515), p. 1075-1095.
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Bayesian Semi-parametric Analysis of Semi-competing Risks Data: Investigating Hospital Readmission after a Pancreatic Cancer Diagnosis.
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