Peihua Qiu | University of Florida (original) (raw)
Papers by Peihua Qiu
Technometrics, May 9, 2019
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Technometrics, Apr 2, 2016
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Technometrics, May 1, 2000
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IISE transactions, Dec 3, 2021
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Technometrics, Jun 28, 2021
Machine learning methods have been widely used in different applications, including process contr... more Machine learning methods have been widely used in different applications, including process control and monitoring. For handling statistical process control (SPC) problems, conventional supervised ...
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Iie Transactions, Jun 9, 2016
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Journal of Quality Technology, Jan 2, 2018
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Journal of the American Statistical Association, Dec 1, 2009
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Computers & Industrial Engineering, Jul 1, 2023
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John Wiley & Sons, Inc. eBooks, May 20, 2005
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arXiv (Cornell University), Apr 2, 2019
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Quality Engineering, 2011
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Technometrics, Aug 1, 2010
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Chapman and Hall/CRC eBooks, Oct 14, 2013
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JAMA network open, May 27, 2022
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Applied statistics, May 27, 2022
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Technometrics, Dec 4, 2018
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Background: Body mass index (BMI)-adjusted prostate-specific antigen (PSA) model has been propose... more Background: Body mass index (BMI)-adjusted prostate-specific antigen (PSA) model has been proposed to improve the predictive accuracy of serum PSA in prostate cancer (PCa) screening. However, how BMI change rate may influence PSA levels in PCa-free men has not been well studied. The current study is to examine the relationship between BMI change rate and serum PSA in PCa-free men and whether this relationship is modified by circulating testosterone. Methods: We conducted this study at a tertiary hospital in the Southeastern US using the Electronic Medical Records of PCa-free men with initial PSA less than 4 ng/mL (cutoff for prostate biopsy), at least 1 testosterone measurement and at least 2 BMI measurements during the study period. Time when the first BMI measurement was recorded served as the baseline, and the study period was defined from baseline to the most recent hospital visit. The included medical records ranged from Jun 2001 to Oct 2015. BMI change rate was created in two ways depending on the number of data points. For men with only 2 BMI measurements, it was calculated by firstly subtracting baseline BMI from the second BMI, then dividing the difference by time interval (months) between the two BMI measurements. For men with more than 2 BMI measurements, we firstly regressed BMI to time interval (months) between that measurement and baseline, then took the β regression coefficient (slope) as the BMI change rate for that men. Multivariable linear regression was used to assess the association of BMI change rate with three PSA measures, including peak, the most recent, and mean PSA during the study period. Effect modification by testosterone was assessed through stratified analysis by testosterone level of 280 ng/dL as cutoff. Results: A total of 470 men with a mean study period of 97.6 months were included. Median age at baseline was 62 years. After adjusting for covariates including baseline BMI, no significant association of BMI change rate was observed with peak PSA (β =0.416, P =0.078), the most recent PSA (β =0.360, P =0.139), or mean PSA (β =0.405, P =0.064) in the overall sample. However, testosterone-stratified analyses indicated that BMI change rate was positively associated with peak PSA (β =1.118, P =0.013), the most recent PSA (β =0.932, P =0.044), and mean PSA (β =1.034, P =0.013) in men with testosterone <280 ng/dL, but no significant association was observed in men with testosterone ≥280 ng/dL (for peak PSA, β =0.076, P =0.785; for the most recent PSA, β =0.072, P =0.802; for mean PSA, β =0.099, P =0.700). Conclusion: Accelerated BMI increase in middle-to-late adulthood might correlate with higher PSA level if a low circulating testosterone occurred concurrently. Further studies are needed to confirm this finding. Citation Format: Kai Wang, Mattia Prosperi, Peihua Qiu, Ting-Yuan David Cheng, Victoria Y. Bird, Xinguang Chen, Mingyang Song. Circulating testosterone in modifying the association of BMI change rate with serum PSA in prostate cancer-free men with initial-PSA less than 4 ng/mL [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 591.
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Statistics in Medicine
In medical studies, composite indices and/or scores are routinely used for predicting medical con... more In medical studies, composite indices and/or scores are routinely used for predicting medical conditions of patients. These indices are usually developed from observed data of certain disease risk factors, and it has been demonstrated in the literature that single index models can provide a powerful tool for this purpose. In practice, the observed data of disease risk factors are often longitudinal in the sense that they are collected at multiple time points for individual patients, and there are often multiple aspects of a patient's medical condition that are of our concern. However, most existing single‐index models are developed for cases with independent data and a single response variable, which are inappropriate for the problem just described in which within‐subject observations are usually correlated and there are multiple mutually correlated response variables involved. This paper aims to fill this methodological gap by developing a single index model for analyzing longi...
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Technometrics, May 9, 2019
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Technometrics, Apr 2, 2016
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Technometrics, May 1, 2000
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IISE transactions, Dec 3, 2021
Bookmarks Related papers MentionsView impact
Technometrics, Jun 28, 2021
Machine learning methods have been widely used in different applications, including process contr... more Machine learning methods have been widely used in different applications, including process control and monitoring. For handling statistical process control (SPC) problems, conventional supervised ...
Bookmarks Related papers MentionsView impact
Iie Transactions, Jun 9, 2016
Bookmarks Related papers MentionsView impact
Journal of Quality Technology, Jan 2, 2018
Bookmarks Related papers MentionsView impact
Journal of the American Statistical Association, Dec 1, 2009
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Computers & Industrial Engineering, Jul 1, 2023
Bookmarks Related papers MentionsView impact
Bookmarks Related papers MentionsView impact
John Wiley & Sons, Inc. eBooks, May 20, 2005
Bookmarks Related papers MentionsView impact
arXiv (Cornell University), Apr 2, 2019
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Quality Engineering, 2011
Bookmarks Related papers MentionsView impact
Technometrics, Aug 1, 2010
Bookmarks Related papers MentionsView impact
Chapman and Hall/CRC eBooks, Oct 14, 2013
Bookmarks Related papers MentionsView impact
JAMA network open, May 27, 2022
Bookmarks Related papers MentionsView impact
Applied statistics, May 27, 2022
Bookmarks Related papers MentionsView impact
Technometrics, Dec 4, 2018
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Background: Body mass index (BMI)-adjusted prostate-specific antigen (PSA) model has been propose... more Background: Body mass index (BMI)-adjusted prostate-specific antigen (PSA) model has been proposed to improve the predictive accuracy of serum PSA in prostate cancer (PCa) screening. However, how BMI change rate may influence PSA levels in PCa-free men has not been well studied. The current study is to examine the relationship between BMI change rate and serum PSA in PCa-free men and whether this relationship is modified by circulating testosterone. Methods: We conducted this study at a tertiary hospital in the Southeastern US using the Electronic Medical Records of PCa-free men with initial PSA less than 4 ng/mL (cutoff for prostate biopsy), at least 1 testosterone measurement and at least 2 BMI measurements during the study period. Time when the first BMI measurement was recorded served as the baseline, and the study period was defined from baseline to the most recent hospital visit. The included medical records ranged from Jun 2001 to Oct 2015. BMI change rate was created in two ways depending on the number of data points. For men with only 2 BMI measurements, it was calculated by firstly subtracting baseline BMI from the second BMI, then dividing the difference by time interval (months) between the two BMI measurements. For men with more than 2 BMI measurements, we firstly regressed BMI to time interval (months) between that measurement and baseline, then took the β regression coefficient (slope) as the BMI change rate for that men. Multivariable linear regression was used to assess the association of BMI change rate with three PSA measures, including peak, the most recent, and mean PSA during the study period. Effect modification by testosterone was assessed through stratified analysis by testosterone level of 280 ng/dL as cutoff. Results: A total of 470 men with a mean study period of 97.6 months were included. Median age at baseline was 62 years. After adjusting for covariates including baseline BMI, no significant association of BMI change rate was observed with peak PSA (β =0.416, P =0.078), the most recent PSA (β =0.360, P =0.139), or mean PSA (β =0.405, P =0.064) in the overall sample. However, testosterone-stratified analyses indicated that BMI change rate was positively associated with peak PSA (β =1.118, P =0.013), the most recent PSA (β =0.932, P =0.044), and mean PSA (β =1.034, P =0.013) in men with testosterone <280 ng/dL, but no significant association was observed in men with testosterone ≥280 ng/dL (for peak PSA, β =0.076, P =0.785; for the most recent PSA, β =0.072, P =0.802; for mean PSA, β =0.099, P =0.700). Conclusion: Accelerated BMI increase in middle-to-late adulthood might correlate with higher PSA level if a low circulating testosterone occurred concurrently. Further studies are needed to confirm this finding. Citation Format: Kai Wang, Mattia Prosperi, Peihua Qiu, Ting-Yuan David Cheng, Victoria Y. Bird, Xinguang Chen, Mingyang Song. Circulating testosterone in modifying the association of BMI change rate with serum PSA in prostate cancer-free men with initial-PSA less than 4 ng/mL [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 591.
Bookmarks Related papers MentionsView impact
Statistics in Medicine
In medical studies, composite indices and/or scores are routinely used for predicting medical con... more In medical studies, composite indices and/or scores are routinely used for predicting medical conditions of patients. These indices are usually developed from observed data of certain disease risk factors, and it has been demonstrated in the literature that single index models can provide a powerful tool for this purpose. In practice, the observed data of disease risk factors are often longitudinal in the sense that they are collected at multiple time points for individual patients, and there are often multiple aspects of a patient's medical condition that are of our concern. However, most existing single‐index models are developed for cases with independent data and a single response variable, which are inappropriate for the problem just described in which within‐subject observations are usually correlated and there are multiple mutually correlated response variables involved. This paper aims to fill this methodological gap by developing a single index model for analyzing longi...
Bookmarks Related papers MentionsView impact