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Peter Sasieni - One of the best experts on this subject based on the ideXlab platform.
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do prostate cancer risk models improve the Predictive Accuracy of psa screening a meta analysis
Annals of Oncology, 2015Co-Authors: Karly S Louie, A Seigneurin, P Cathcart, Peter SasieniAbstract:ABSTRACT Despite the extensive development of prostate cancer (PCa) risk models that are used for patient–clinician decision-making for PCa screening, their Predictive Accuracy is unknown. In a meta-analysis of six different risk prediction models, results show that models have the potential to increase the sensitivity of PSA screening to detect any PCa (44% versus 21%). Background Despite the extensive development of risk prediction models to aid patient decision-making on prostate screening, it is unknown whether these models could improve Predictive Accuracy of PSA testing to detect prostate cancer (PCa). The objective of this study was to perform a systematic review to identify PCa risk models and to assess the model's performance to predict PCa by conducting a meta-analysis. Design A systematic literature search of Medline was conducted to identify PCa Predictive risk models that used at least two variables, of which one of the variables was prostate-specific antigen (PSA) level. Model performance (discrimination and calibration) was assessed. Prediction models validated in ≥5 study populations and reported area under the curve (AUC) for prediction of any or clinically significant PCa were eligible for meta-analysis. Summary AUC and 95% CIs were calculated using a random-effects model. Results The systematic review identified 127 unique PCa prediction models; however, only six models met study criteria for meta-analysis for predicting any PCa: Prostataclass, Finne, Karakiewcz Prostate Cancer Prevention Trial (PCPT), Chun, and the European Randomized Study of Screening for Prostate Cancer Risk Calculator 3 (ERSPC RC3). Summary AUC estimates show that PCPT does not differ from PSA testing (0.66) despite performing better in studies validating both PSA and PCPT. Predictive Accuracy to discriminate PCa increases with Finne (AUC = 0.74), Karakiewcz (AUC = 0.74), Chun (AUC = 0.76) and ERSPC RC3 and Prostataclass have the highest discriminative value (AUC = 0.79), which is equivalent to doubling the sensitivity of PSA testing (44% versus 21%) without loss of specificity. The discriminative Accuracy of PCPT to detect clinically significant PCa was AUC = 0.71. Calibration measures of the models were poorly reported. Conclusions Risk prediction models improve the Predictive Accuracy of PSA testing to detect PCa. Future developments in the use of PCa risk models should evaluate its clinical effectiveness in practice.
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Do prostate cancer risk models improve the Predictive Accuracy of PSA screening? A meta-analysis
Annals of Oncology, 2015Co-Authors: K. Louie, A Seigneurin, P Cathcart, Peter SasieniAbstract:BACKGROUND: Despite the extensive development of risk prediction models to aid patient decision-making on prostate screening, it is unknown whether these models could improve Predictive Accuracy of PSA testing to detect prostate cancer (PCa). The objective of this study was to perform a systematic review to identify PCa risk models and to assess the model's performance to predict PCa by conducting a meta-analysis. DESIGN: A systematic literature search of Medline was conducted to identify PCa Predictive risk models that used at least two variables, of which one of the variables was prostate-specific antigen (PSA) level. Model performance (discrimination and calibration) was assessed. Prediction models validated in ≥5 study populations and reported area under the curve (AUC) for prediction of any or clinically significant PCa were eligible for meta-analysis. Summary AUC and 95% CIs were calculated using a random-effects model. RESULTS: The systematic review identified 127 unique PCa prediction models; however, only six models met study criteria for meta-analysis for predicting any PCa: Prostataclass, Finne, Karakiewcz, Prostate Cancer Prevention Trial (PCPT), Chun, and the European Randomized Study of Screening for Prostate Cancer Risk Calculator 3 (ERSPC RC3). Summary AUC estimates show that PCPT does not differ from PSA testing (0.66) despite performing better in studies validating both PSA and PCPT. Predictive Accuracy to discriminate PCa increases with Finne (AUC = 0.74), Karakiewcz (AUC = 0.74), Chun (AUC = 0.76) and ERSPC RC3 and Prostataclass have the highest discriminative value (AUC = 0.79), which is equivalent to doubling the sensitivity of PSA testing (44% versus 21%) without loss of specificity. The discriminative Accuracy of PCPT to detect clinically significant PCa was AUC = 0.71. Calibration measures of the models were poorly reported. CONCLUSIONS: Risk prediction models improve the Predictive Accuracy of PSA testing to detect PCa. Future developments in the use of PCa risk models should evaluate its clinical effectiveness in practice.
A Seigneurin - One of the best experts on this subject based on the ideXlab platform.
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do prostate cancer risk models improve the Predictive Accuracy of psa screening a meta analysis
Annals of Oncology, 2015Co-Authors: Karly S Louie, A Seigneurin, P Cathcart, Peter SasieniAbstract:ABSTRACT Despite the extensive development of prostate cancer (PCa) risk models that are used for patient–clinician decision-making for PCa screening, their Predictive Accuracy is unknown. In a meta-analysis of six different risk prediction models, results show that models have the potential to increase the sensitivity of PSA screening to detect any PCa (44% versus 21%). Background Despite the extensive development of risk prediction models to aid patient decision-making on prostate screening, it is unknown whether these models could improve Predictive Accuracy of PSA testing to detect prostate cancer (PCa). The objective of this study was to perform a systematic review to identify PCa risk models and to assess the model's performance to predict PCa by conducting a meta-analysis. Design A systematic literature search of Medline was conducted to identify PCa Predictive risk models that used at least two variables, of which one of the variables was prostate-specific antigen (PSA) level. Model performance (discrimination and calibration) was assessed. Prediction models validated in ≥5 study populations and reported area under the curve (AUC) for prediction of any or clinically significant PCa were eligible for meta-analysis. Summary AUC and 95% CIs were calculated using a random-effects model. Results The systematic review identified 127 unique PCa prediction models; however, only six models met study criteria for meta-analysis for predicting any PCa: Prostataclass, Finne, Karakiewcz Prostate Cancer Prevention Trial (PCPT), Chun, and the European Randomized Study of Screening for Prostate Cancer Risk Calculator 3 (ERSPC RC3). Summary AUC estimates show that PCPT does not differ from PSA testing (0.66) despite performing better in studies validating both PSA and PCPT. Predictive Accuracy to discriminate PCa increases with Finne (AUC = 0.74), Karakiewcz (AUC = 0.74), Chun (AUC = 0.76) and ERSPC RC3 and Prostataclass have the highest discriminative value (AUC = 0.79), which is equivalent to doubling the sensitivity of PSA testing (44% versus 21%) without loss of specificity. The discriminative Accuracy of PCPT to detect clinically significant PCa was AUC = 0.71. Calibration measures of the models were poorly reported. Conclusions Risk prediction models improve the Predictive Accuracy of PSA testing to detect PCa. Future developments in the use of PCa risk models should evaluate its clinical effectiveness in practice.
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Do prostate cancer risk models improve the Predictive Accuracy of PSA screening? A meta-analysis
Annals of Oncology, 2015Co-Authors: K. Louie, A Seigneurin, P Cathcart, Peter SasieniAbstract:BACKGROUND: Despite the extensive development of risk prediction models to aid patient decision-making on prostate screening, it is unknown whether these models could improve Predictive Accuracy of PSA testing to detect prostate cancer (PCa). The objective of this study was to perform a systematic review to identify PCa risk models and to assess the model's performance to predict PCa by conducting a meta-analysis. DESIGN: A systematic literature search of Medline was conducted to identify PCa Predictive risk models that used at least two variables, of which one of the variables was prostate-specific antigen (PSA) level. Model performance (discrimination and calibration) was assessed. Prediction models validated in ≥5 study populations and reported area under the curve (AUC) for prediction of any or clinically significant PCa were eligible for meta-analysis. Summary AUC and 95% CIs were calculated using a random-effects model. RESULTS: The systematic review identified 127 unique PCa prediction models; however, only six models met study criteria for meta-analysis for predicting any PCa: Prostataclass, Finne, Karakiewcz, Prostate Cancer Prevention Trial (PCPT), Chun, and the European Randomized Study of Screening for Prostate Cancer Risk Calculator 3 (ERSPC RC3). Summary AUC estimates show that PCPT does not differ from PSA testing (0.66) despite performing better in studies validating both PSA and PCPT. Predictive Accuracy to discriminate PCa increases with Finne (AUC = 0.74), Karakiewcz (AUC = 0.74), Chun (AUC = 0.76) and ERSPC RC3 and Prostataclass have the highest discriminative value (AUC = 0.79), which is equivalent to doubling the sensitivity of PSA testing (44% versus 21%) without loss of specificity. The discriminative Accuracy of PCPT to detect clinically significant PCa was AUC = 0.71. Calibration measures of the models were poorly reported. CONCLUSIONS: Risk prediction models improve the Predictive Accuracy of PSA testing to detect PCa. Future developments in the use of PCa risk models should evaluate its clinical effectiveness in practice.
P Cathcart - One of the best experts on this subject based on the ideXlab platform.
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do prostate cancer risk models improve the Predictive Accuracy of psa screening a meta analysis
Annals of Oncology, 2015Co-Authors: Karly S Louie, A Seigneurin, P Cathcart, Peter SasieniAbstract:ABSTRACT Despite the extensive development of prostate cancer (PCa) risk models that are used for patient–clinician decision-making for PCa screening, their Predictive Accuracy is unknown. In a meta-analysis of six different risk prediction models, results show that models have the potential to increase the sensitivity of PSA screening to detect any PCa (44% versus 21%). Background Despite the extensive development of risk prediction models to aid patient decision-making on prostate screening, it is unknown whether these models could improve Predictive Accuracy of PSA testing to detect prostate cancer (PCa). The objective of this study was to perform a systematic review to identify PCa risk models and to assess the model's performance to predict PCa by conducting a meta-analysis. Design A systematic literature search of Medline was conducted to identify PCa Predictive risk models that used at least two variables, of which one of the variables was prostate-specific antigen (PSA) level. Model performance (discrimination and calibration) was assessed. Prediction models validated in ≥5 study populations and reported area under the curve (AUC) for prediction of any or clinically significant PCa were eligible for meta-analysis. Summary AUC and 95% CIs were calculated using a random-effects model. Results The systematic review identified 127 unique PCa prediction models; however, only six models met study criteria for meta-analysis for predicting any PCa: Prostataclass, Finne, Karakiewcz Prostate Cancer Prevention Trial (PCPT), Chun, and the European Randomized Study of Screening for Prostate Cancer Risk Calculator 3 (ERSPC RC3). Summary AUC estimates show that PCPT does not differ from PSA testing (0.66) despite performing better in studies validating both PSA and PCPT. Predictive Accuracy to discriminate PCa increases with Finne (AUC = 0.74), Karakiewcz (AUC = 0.74), Chun (AUC = 0.76) and ERSPC RC3 and Prostataclass have the highest discriminative value (AUC = 0.79), which is equivalent to doubling the sensitivity of PSA testing (44% versus 21%) without loss of specificity. The discriminative Accuracy of PCPT to detect clinically significant PCa was AUC = 0.71. Calibration measures of the models were poorly reported. Conclusions Risk prediction models improve the Predictive Accuracy of PSA testing to detect PCa. Future developments in the use of PCa risk models should evaluate its clinical effectiveness in practice.
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Do prostate cancer risk models improve the Predictive Accuracy of PSA screening? A meta-analysis
Annals of Oncology, 2015Co-Authors: K. Louie, A Seigneurin, P Cathcart, Peter SasieniAbstract:BACKGROUND: Despite the extensive development of risk prediction models to aid patient decision-making on prostate screening, it is unknown whether these models could improve Predictive Accuracy of PSA testing to detect prostate cancer (PCa). The objective of this study was to perform a systematic review to identify PCa risk models and to assess the model's performance to predict PCa by conducting a meta-analysis. DESIGN: A systematic literature search of Medline was conducted to identify PCa Predictive risk models that used at least two variables, of which one of the variables was prostate-specific antigen (PSA) level. Model performance (discrimination and calibration) was assessed. Prediction models validated in ≥5 study populations and reported area under the curve (AUC) for prediction of any or clinically significant PCa were eligible for meta-analysis. Summary AUC and 95% CIs were calculated using a random-effects model. RESULTS: The systematic review identified 127 unique PCa prediction models; however, only six models met study criteria for meta-analysis for predicting any PCa: Prostataclass, Finne, Karakiewcz, Prostate Cancer Prevention Trial (PCPT), Chun, and the European Randomized Study of Screening for Prostate Cancer Risk Calculator 3 (ERSPC RC3). Summary AUC estimates show that PCPT does not differ from PSA testing (0.66) despite performing better in studies validating both PSA and PCPT. Predictive Accuracy to discriminate PCa increases with Finne (AUC = 0.74), Karakiewcz (AUC = 0.74), Chun (AUC = 0.76) and ERSPC RC3 and Prostataclass have the highest discriminative value (AUC = 0.79), which is equivalent to doubling the sensitivity of PSA testing (44% versus 21%) without loss of specificity. The discriminative Accuracy of PCPT to detect clinically significant PCa was AUC = 0.71. Calibration measures of the models were poorly reported. CONCLUSIONS: Risk prediction models improve the Predictive Accuracy of PSA testing to detect PCa. Future developments in the use of PCa risk models should evaluate its clinical effectiveness in practice.
Patrick J. Heagerty - One of the best experts on this subject based on the ideXlab platform.
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non parametric estimation of a time dependent Predictive Accuracy curve
Biostatistics, 2013Co-Authors: Paramita Sahachaudhuri, Patrick J. HeagertyAbstract:A major biomedical goal associated with evaluating a candidate biomarker or developing a Predictive model score for event-time outcomes is to accurately distinguish between incident cases from the controls surviving beyond t throughout the entire study period. Extensions of standard binary classification measures like time-dependent sensitivity, specificity, and receiver operating characteristic (ROC) curves have been developed in this context (Heagerty, P. J., and others, 2000. Time-dependent ROC curves for censored survival data and a diagnostic marker. Biometrics 56, 337–344). We propose a direct, non-parametric method to estimate the time-dependent Area under the curve (AUC) which we refer to as the weighted mean rank (WMR) estimator. The proposed estimator performs well relative to the semi-parametric AUC curve estimator of Heagerty and Zheng (2005. Survival model Predictive Accuracy and ROC curves. Biometrics 61, 92–105). We establish the asymptotic properties of the proposed estimator and show that the Accuracy of markers can be compared very simply using the difference in the WMR statistics. Estimators of pointwise standard errors are provided.
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time dependent Predictive Accuracy in the presence of competing risks
Biometrics, 2010Co-Authors: P Saha, Patrick J. HeagertyAbstract:Competing risks arise naturally in time-to-event studies. In this article, we propose time-dependent Accuracy measures for a marker when we have censored survival times and competing risks. Time-dependent versions of sensitivity or true positive (TP) fraction naturally correspond to consideration of either cumulative (or prevalent) cases that accrue over a fixed time period, or alternatively to incident cases that are observed among event-free subjects at any select time. Time-dependent (dynamic) specificity (1-false positive (FP)) can be based on the marker distribution among event-free subjects. We extend these definitions to incorporate cause of failure for competing risks outcomes. The proposed estimation for cause-specific cumulative TP/dynamic FP is based on the nearest neighbor estimation of bivariate distribution function of the marker and the event time. On the other hand, incident TP/dynamic FP can be estimated using a possibly nonproportional hazards Cox model for the cause-specific hazards and riskset reweighting of the marker distribution. The proposed methods extend the time-dependent Predictive Accuracy measures of Heagerty, Lumley, and Pepe (2000, Biometrics 56, 337-344) and Heagerty and Zheng (2005, Biometrics 61, 92-105).
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Survival model Predictive Accuracy and ROC curves.
Biometrics, 2005Co-Authors: Patrick J. Heagerty, Yingye ZhengAbstract:Summary The Predictive Accuracy of a survival model can be summarized using extensions of the proportion of variation explained by the model, or R2, commonly used for continuous response models, or using extensions of sensitivity and specificity, which are commonly used for binary response models. In this article we propose new time-dependent Accuracy summaries based on time-specific versions of sensitivity and specificity calculated over risk sets. We connect the Accuracy summaries to a previously proposed global concordance measure, which is a variant of Kendall's tau. In addition, we show how standard Cox regression output can be used to obtain estimates of time-dependent sensitivity and specificity, and time-dependent receiver operating characteristic (ROC) curves. Semiparametric estimation methods appropriate for both proportional and nonproportional hazards data are introduced, evaluated in simulations, and illustrated using two familiar survival data sets.
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Survival Model Predictive Accuracy and ROC Curves
2004Co-Authors: Patrick J. Heagerty, Yingye ZhengAbstract:The Predictive Accuracy of a survival model can be summarized using extensions of the proportion of variation explained by the model, or R^2, commonly used for continuous response models, or using extensions of sensitivity and specificity which are commonly used for binary response models.In this manuscript we propose new time-dependent Accuracy summaries based on time-specific versions of sensitivity and specificity calculated over risk sets. We connect the Accuracy summaries to a previously proposed global concordance measure which is a variant of Kendall's tau. In addition, we show how standard Cox regression output can be used to obtain estimates of time-dependent sensitivity and specificity, and time-dependent reciever operating characteristic (ROC) curves. Semi-parametric estimation methods appropriate for both proportional hazards and non-proportional hazards data are introduced, evaluated in simulations, and illustrated using two familiar survival data sets.
Karly S Louie - One of the best experts on this subject based on the ideXlab platform.
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do prostate cancer risk models improve the Predictive Accuracy of psa screening a meta analysis
Annals of Oncology, 2015Co-Authors: Karly S Louie, A Seigneurin, P Cathcart, Peter SasieniAbstract:ABSTRACT Despite the extensive development of prostate cancer (PCa) risk models that are used for patient–clinician decision-making for PCa screening, their Predictive Accuracy is unknown. In a meta-analysis of six different risk prediction models, results show that models have the potential to increase the sensitivity of PSA screening to detect any PCa (44% versus 21%). Background Despite the extensive development of risk prediction models to aid patient decision-making on prostate screening, it is unknown whether these models could improve Predictive Accuracy of PSA testing to detect prostate cancer (PCa). The objective of this study was to perform a systematic review to identify PCa risk models and to assess the model's performance to predict PCa by conducting a meta-analysis. Design A systematic literature search of Medline was conducted to identify PCa Predictive risk models that used at least two variables, of which one of the variables was prostate-specific antigen (PSA) level. Model performance (discrimination and calibration) was assessed. Prediction models validated in ≥5 study populations and reported area under the curve (AUC) for prediction of any or clinically significant PCa were eligible for meta-analysis. Summary AUC and 95% CIs were calculated using a random-effects model. Results The systematic review identified 127 unique PCa prediction models; however, only six models met study criteria for meta-analysis for predicting any PCa: Prostataclass, Finne, Karakiewcz Prostate Cancer Prevention Trial (PCPT), Chun, and the European Randomized Study of Screening for Prostate Cancer Risk Calculator 3 (ERSPC RC3). Summary AUC estimates show that PCPT does not differ from PSA testing (0.66) despite performing better in studies validating both PSA and PCPT. Predictive Accuracy to discriminate PCa increases with Finne (AUC = 0.74), Karakiewcz (AUC = 0.74), Chun (AUC = 0.76) and ERSPC RC3 and Prostataclass have the highest discriminative value (AUC = 0.79), which is equivalent to doubling the sensitivity of PSA testing (44% versus 21%) without loss of specificity. The discriminative Accuracy of PCPT to detect clinically significant PCa was AUC = 0.71. Calibration measures of the models were poorly reported. Conclusions Risk prediction models improve the Predictive Accuracy of PSA testing to detect PCa. Future developments in the use of PCa risk models should evaluate its clinical effectiveness in practice.
