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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...

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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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Penalized solutions to functional regression problems.

Computational statistics & data analysis

Harezlak J, Coull BA, Laird NM, Magari SR, Christiani DC.
PMID: 18552972
Comput Stat Data Anal. 2007 Jun 15;51(10):4911-4925. doi: 10.1016/j.csda.2006.09.034.

Recent technological advances in continuous biological monitoring and personal exposure assessment have led to the collection of subject-specific functional data. A primary goal in such studies is to assess the relationship between the functional predictors and the functional responses....

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Harezlak J, Coull BA, Laird NM, et al. Penalized solutions to functional regression problems. Comput Stat Data Anal. 2007;51(10):4911-4925doi: 10.1016/j.csda.2006.09.034.
Harezlak, J., Coull, B. A., Laird, N. M., Magari, S. R., & Christiani, D. C. (2007). Penalized solutions to functional regression problems. Computational statistics & data analysis, 51(10), 4911-4925. https://doi.org/10.1016/j.csda.2006.09.034
Harezlak, Jaroslaw, et al. "Penalized solutions to functional regression problems." Computational statistics & data analysis vol. 51,10 (2007): 4911-4925. doi: https://doi.org/10.1016/j.csda.2006.09.034
Harezlak J, Coull BA, Laird NM, Magari SR, Christiani DC. Penalized solutions to functional regression problems. Comput Stat Data Anal. 2007 Jun 15;51(10):4911-4925. doi: 10.1016/j.csda.2006.09.034. PMID: 18552972; PMCID: PMC2084351.
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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...

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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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Marginalized zero-altered models for longitudinal count data.

Statistics in biosciences

Tabb LP, Tchetgen EJ, Wellenius GA, Coull BA.
PMID: 27867423
Stat Biosci. 2016 Oct;8(2):181-203. doi: 10.1007/s12561-015-9136-6. Epub 2015 Sep 22.

Count data often exhibit more zeros than predicted by common count distributions like the Poisson or negative binomial. In recent years, there has been considerable interest in methods for analyzing zero-inflated count data in longitudinal or other correlated data...

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Tabb LP, Tchetgen EJ, Wellenius GA, et al. Marginalized zero-altered models for longitudinal count data. Stat Biosci. 2015;8(2):181-203doi: 10.1007/s12561-015-9136-6.
Tabb, L. P., Tchetgen, E. J., Wellenius, G. A., & Coull, B. A. (2016). Marginalized zero-altered models for longitudinal count data. Statistics in biosciences, 8(2), 181-203. https://doi.org/10.1007/s12561-015-9136-6
Tabb, Loni Philip, et al. "Marginalized zero-altered models for longitudinal count data." Statistics in biosciences vol. 8,2 (2016): 181-203. doi: https://doi.org/10.1007/s12561-015-9136-6
Tabb LP, Tchetgen EJ, Wellenius GA, Coull BA. Marginalized zero-altered models for longitudinal count data. Stat Biosci. 2016 Oct;8(2):181-203. doi: 10.1007/s12561-015-9136-6. Epub 2015 Sep 22. PMID: 27867423; PMCID: PMC5111636.
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Using Wavelet-Based Functional Mixed Models to Characterize Population Heterogeneity in Accelerometer Profiles: A Case Study.

Journal of the American Statistical Association

Morris JS, Arroyo C, Coull BA, Ryan LM, Herrick R, Gortmaker SL.
PMID: 19169424
J Am Stat Assoc. 2006 Dec 01;101(476):1352-1364. doi: 10.1198/016214506000000465.

We present a case study illustrating the challenges of analyzing accelerometer data taken from a sample of children participating in an intervention study designed to increase physical activity. An accelerometer is a small device worn on the hip that...

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Morris JS, Arroyo C, Coull BA, et al. Using Wavelet-Based Functional Mixed Models to Characterize Population Heterogeneity in Accelerometer Profiles: A Case Study. J Am Stat Assoc. 2006;101(476):1352-1364doi: 10.1198/016214506000000465.
Morris, J. S., Arroyo, C., Coull, B. A., Ryan, L. M., Herrick, R., & Gortmaker, S. L. (2006). Using Wavelet-Based Functional Mixed Models to Characterize Population Heterogeneity in Accelerometer Profiles: A Case Study. Journal of the American Statistical Association, 101(476), 1352-1364. https://doi.org/10.1198/016214506000000465
Morris, Jeffrey S, et al. "Using Wavelet-Based Functional Mixed Models to Characterize Population Heterogeneity in Accelerometer Profiles: A Case Study." Journal of the American Statistical Association vol. 101,476 (2006): 1352-1364. doi: https://doi.org/10.1198/016214506000000465
Morris JS, Arroyo C, Coull BA, Ryan LM, Herrick R, Gortmaker SL. Using Wavelet-Based Functional Mixed Models to Characterize Population Heterogeneity in Accelerometer Profiles: A Case Study. J Am Stat Assoc. 2006 Dec 01;101(476):1352-1364. doi: 10.1198/016214506000000465. PMID: 19169424; PMCID: PMC2630189.
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The importance of scale for spatial-confounding bias and precision of spatial regression estimators.

Statistical science : a review journal of the Institute of Mathematical Statistics

Paciorek CJ.
PMID: 21528104
Stat Sci. 2010 Feb;25(1):107-125. doi: 10.1214/10-STS326.

Residuals in regression models are often spatially correlated. Prominent examples include studies in environmental epidemiology to understand the chronic health effects of pollutants. I consider the effects of residual spatial structure on the bias and precision of regression coefficients,...

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Paciorek CJ. The importance of scale for spatial-confounding bias and precision of spatial regression estimators. Stat Sci. 2010;25(1):107-125doi: 10.1214/10-STS326.
Paciorek, C. J. (2010). The importance of scale for spatial-confounding bias and precision of spatial regression estimators. Statistical science : a review journal of the Institute of Mathematical Statistics, 25(1), 107-125. https://doi.org/10.1214/10-STS326
Paciorek, Christopher J. "The importance of scale for spatial-confounding bias and precision of spatial regression estimators." Statistical science : a review journal of the Institute of Mathematical Statistics vol. 25,1 (2010): 107-125. doi: https://doi.org/10.1214/10-STS326
Paciorek CJ. The importance of scale for spatial-confounding bias and precision of spatial regression estimators. Stat Sci. 2010 Feb;25(1):107-125. doi: 10.1214/10-STS326. PMID: 21528104; PMCID: PMC3082155.
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NONLINEAR PREDICTIVE LATENT PROCESS MODELS FOR INTEGRATING SPATIO-TEMPORAL EXPOSURE DATA FROM MULTIPLE SOURCES.

The annals of applied statistics

Bliznyuk N, Paciorek CJ, Schwartz J, Coull B.
PMID: 29861821
Ann Appl Stat. 2014 Sep;8(3):1538-1560. doi: 10.1214/14-AOAS737. Epub 2014 Oct 23.

Spatio-temporal prediction of levels of an environmental exposure is an important problem in environmental epidemiology. Our work is motivated by multiple studies on the spatio-temporal distribution of mobile source, or traffic related, particles in the greater Boston area. When...

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Bliznyuk N, Paciorek CJ, Schwartz J, et al. NONLINEAR PREDICTIVE LATENT PROCESS MODELS FOR INTEGRATING SPATIO-TEMPORAL EXPOSURE DATA FROM MULTIPLE SOURCES. Ann Appl Stat. 2014;8(3):1538-1560doi: 10.1214/14-AOAS737.
Bliznyuk, N., Paciorek, C. J., Schwartz, J., & Coull, B. (2014). NONLINEAR PREDICTIVE LATENT PROCESS MODELS FOR INTEGRATING SPATIO-TEMPORAL EXPOSURE DATA FROM MULTIPLE SOURCES. The annals of applied statistics, 8(3), 1538-1560. https://doi.org/10.1214/14-AOAS737
Bliznyuk, Nikolay, et al. "NONLINEAR PREDICTIVE LATENT PROCESS MODELS FOR INTEGRATING SPATIO-TEMPORAL EXPOSURE DATA FROM MULTIPLE SOURCES." The annals of applied statistics vol. 8,3 (2014): 1538-1560. doi: https://doi.org/10.1214/14-AOAS737
Bliznyuk N, Paciorek CJ, Schwartz J, Coull B. NONLINEAR PREDICTIVE LATENT PROCESS MODELS FOR INTEGRATING SPATIO-TEMPORAL EXPOSURE DATA FROM MULTIPLE SOURCES. Ann Appl Stat. 2014 Sep;8(3):1538-1560. doi: 10.1214/14-AOAS737. Epub 2014 Oct 23. PMID: 29861821; PMCID: PMC5983907.
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Showing 1 to 7 of 7 entries