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Hierarchical models for semi-competing risks data with application to quality of end-of-life care for pancreatic cancer.

Journal of the American Statistical Association

Lee KH, Dominici F, Schrag D, Haneuse S.
PMID: 28303074
J Am Stat Assoc. 2016;111(515):1075-1095. doi: 10.1080/01621459.2016.1164052. Epub 2016 Oct 18.

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...

Cite
Lee KH, Dominici F, Schrag D, et al. Hierarchical models for semi-competing risks data with application to quality of end-of-life care for pancreatic cancer. J Am Stat Assoc. 2016;111(515):1075-1095doi: 10.1080/01621459.2016.1164052.
Lee, K. H., Dominici, F., Schrag, D., & Haneuse, S. (2016). Hierarchical models for semi-competing risks data with application to quality of end-of-life care for pancreatic cancer. Journal of the American Statistical Association, 111(515), 1075-1095. https://doi.org/10.1080/01621459.2016.1164052
Lee, Kyu Ha, et al. "Hierarchical models for semi-competing risks data with application to quality of end-of-life care for pancreatic cancer." Journal of the American Statistical Association vol. 111,515 (2016): 1075-1095. doi: https://doi.org/10.1080/01621459.2016.1164052
Lee KH, Dominici F, Schrag D, Haneuse S. Hierarchical models for semi-competing risks data with application to quality of end-of-life care for pancreatic cancer. J Am Stat Assoc. 2016;111(515):1075-1095. doi: 10.1080/01621459.2016.1164052. Epub 2016 Oct 18. PMID: 28303074; PMCID: PMC5347153.
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Bayesian Semi-parametric Analysis of Semi-competing Risks Data: Investigating Hospital Readmission after a Pancreatic Cancer Diagnosis.

Journal of the Royal Statistical Society. Series C, Applied statistics

Lee KH, Haneuse S, Schrag D, Dominici F.
PMID: 25977592
J R Stat Soc Ser C Appl Stat. 2015 Feb 01;64(2):253-273. doi: 10.1111/rssc.12078.

In the U.S., the Centers for Medicare and Medicaid Services uses 30-day readmission, following hospitalization, as a proxy outcome to monitor quality of care. These efforts generally focus on treatable health conditions, such as pneumonia and heart failure. Expanding...

Cite
Lee KH, Haneuse S, Schrag D, et al. Bayesian Semi-parametric Analysis of Semi-competing Risks Data: Investigating Hospital Readmission after a Pancreatic Cancer Diagnosis. J R Stat Soc Ser C Appl Stat. 2015;64(2):253-273doi: 10.1111/rssc.12078.
Lee, K. H., Haneuse, S., Schrag, D., & Dominici, F. (2015). Bayesian Semi-parametric Analysis of Semi-competing Risks Data: Investigating Hospital Readmission after a Pancreatic Cancer Diagnosis. Journal of the Royal Statistical Society. Series C, Applied statistics, 64(2), 253-273. https://doi.org/10.1111/rssc.12078
Lee, Kyu Ha, et al. "Bayesian Semi-parametric Analysis of Semi-competing Risks Data: Investigating Hospital Readmission after a Pancreatic Cancer Diagnosis." Journal of the Royal Statistical Society. Series C, Applied statistics vol. 64,2 (2015): 253-273. doi: https://doi.org/10.1111/rssc.12078
Lee KH, Haneuse S, Schrag D, Dominici F. Bayesian Semi-parametric Analysis of Semi-competing Risks Data: Investigating Hospital Readmission after a Pancreatic Cancer Diagnosis. J R Stat Soc Ser C Appl Stat. 2015 Feb 01;64(2):253-273. doi: 10.1111/rssc.12078. PMID: 25977592; PMCID: PMC4427057.
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