IDEAS home Printed from https://ideas.repec.org/r/bla/biomet/v58y2002i3p657-664.html
   My bibliography  Save this item

Combining Several Screening Tests: Optimality of the Risk Score

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Weining Shen & Jing Ning & Ying Yuan & Anna S. Lok & Ziding Feng, 2018. "Model†free scoring system for risk prediction with application to hepatocellular carcinoma study," Biometrics, The International Biometric Society, vol. 74(1), pages 239-248, March.
  2. Drehmann, Mathias & Juselius, Mikael, 2014. "Evaluating early warning indicators of banking crises: Satisfying policy requirements," International Journal of Forecasting, Elsevier, vol. 30(3), pages 759-780.
  3. Ming-Yueh Huang & Chin-Tsang Chiang, 2017. "Estimation and Inference Procedures for Semiparametric Distribution Models with Varying Linear-Index," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 44(2), pages 396-424, June.
  4. Yuxin Zhu & Mei‐Cheng Wang, 2022. "Obtaining optimal cutoff values for tree classifiers using multiple biomarkers," Biometrics, The International Biometric Society, vol. 78(1), pages 128-140, March.
  5. Debashis Ghosh, 2004. "Semiparametric methods for the binormal model with multiple biomarkers," The University of Michigan Department of Biostatistics Working Paper Series 1046, Berkeley Electronic Press.
  6. Chiang, Chin-Tsang & Chiu, Chih-Heng, 2012. "Nonparametric and semiparametric optimal transformations of markers," Journal of Multivariate Analysis, Elsevier, vol. 103(1), pages 124-141, January.
  7. Alba M. Franco-Pereira & Christos T. Nakas & M. Carmen Pardo, 2020. "Biomarker assessment in ROC curve analysis using the length of the curve as an index of diagnostic accuracy: the binormal model framework," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 104(4), pages 625-647, December.
  8. Margaret Pepe & Tianxi Cai & Zheng Zhang, 2004. "Combining Predictors for Classification Using the Area Under the ROC Curve," UW Biostatistics Working Paper Series 1021, Berkeley Electronic Press.
  9. Lahiri Kajal & Yang Liu, 2016. "A non-linear forecast combination procedure for binary outcomes," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 20(4), pages 421-440, September.
  10. Sonia Pérez-Fernández & Pablo Martínez-Camblor & Peter Filzmoser & Norberto Corral, 2021. "Visualizing the decision rules behind the ROC curves: understanding the classification process," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 105(1), pages 135-161, March.
  11. Ying Huang & Peter B. Gilbert & Holly Janes, 2012. "Assessing Treatment-Selection Markers using a Potential Outcomes Framework," Biometrics, The International Biometric Society, vol. 68(3), pages 687-696, September.
  12. Chin-Tsang Chiang & Shr-Yan Huang, 2009. "Estimation for the Optimal Combination of Markers without Modeling the Censoring Distribution," Biometrics, The International Biometric Society, vol. 65(1), pages 152-158, March.
  13. Debashis Ghosh, 2004. "Semiparametic models and estimation procedures for binormal ROC curves with multiple biomarkers," The University of Michigan Department of Biostatistics Working Paper Series 1038, Berkeley Electronic Press.
  14. Yingye Zheng & Tianxi Cai & Ziding Feng, 2006. "Application of the Time-Dependent ROC Curves for Prognostic Accuracy with Multiple Biomarkers," Biometrics, The International Biometric Society, vol. 62(1), pages 279-287, March.
  15. Binbing Yu, 2009. "Approximating the risk score for disease diagnosis using MARS," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(7), pages 769-778.
  16. Margaret Sullivan Pepe & Tianxi Cai & Gary Longton, 2006. "Combining Predictors for Classification Using the Area under the Receiver Operating Characteristic Curve," Biometrics, The International Biometric Society, vol. 62(1), pages 221-229, March.
  17. Osamu Komori, 2011. "A boosting method for maximization of the area under the ROC curve," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 63(5), pages 961-979, October.
  18. Debashis Ghosh & Arul Chinnaiyan, 2004. "Classification and selection of biomarkers in genomic data using LASSO," The University of Michigan Department of Biostatistics Working Paper Series 1041, Berkeley Electronic Press.
  19. Qing Lu & Nancy Obuchowski & Sungho Won & Xiaofeng Zhu & Robert C. Elston, 2010. "Using the Optimal Robust Receiver Operating Characteristic (ROC) Curve for Predictive Genetic Tests," Biometrics, The International Biometric Society, vol. 66(2), pages 586-593, June.
  20. Pablo Martínez-Camblor & Sonia Pérez-Fernández & Susana Díaz-Coto, 2021. "Optimal classification scores based on multivariate marker transformations," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 105(4), pages 581-599, December.
  21. Zheng Yuan & Debashis Ghosh, 2008. "Combining Multiple Biomarker Models in Logistic Regression," Biometrics, The International Biometric Society, vol. 64(2), pages 431-439, June.
  22. Heikki Kauppi, 2016. "The Generalized Receiver Operating Characteristic Curve," Discussion Papers 114, Aboa Centre for Economics.
  23. Yifei Sun & Sy Han Chiou & Mei‐Cheng Wang, 2020. "ROC‐guided survival trees and ensembles," Biometrics, The International Biometric Society, vol. 76(4), pages 1177-1189, December.
  24. Dat Huynh & Oliver Laeyendecker & Ron Brookmeyer, 2014. "A serial risk score approach to disease classification that accounts for accuracy and cost," Biometrics, The International Biometric Society, vol. 70(4), pages 1042-1051, December.
  25. Chin-Tsang Chiang & Shao-Hsuan Wang & Ming-Yueh Huang, 2018. "Versatile estimation in censored single-index hazards regression," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 70(3), pages 523-551, June.
  26. Holly Janes & Gary Longton & Margaret S. Pepe, 2009. "Accommodating covariates in receiver operating characteristic analysis," Stata Journal, StataCorp LP, vol. 9(1), pages 17-39, March.
  27. Mei-Cheng Wang & Shanshan Li, 2012. "Bivariate Marker Measurements and ROC Analysis," Biometrics, The International Biometric Society, vol. 68(4), pages 1207-1218, December.
  28. Jin, Hua & Lu, Ying, 2009. "Permutation test for non-inferiority of the linear to the optimal combination of multiple tests," Statistics & Probability Letters, Elsevier, vol. 79(5), pages 664-669, March.
  29. Zhang Zhiwei & Ma Shujie & Nie Lei & Soon Guoxing, 2017. "A Quantitative Concordance Measure for Comparing and Combining Treatment Selection Markers," The International Journal of Biostatistics, De Gruyter, vol. 13(1), pages 1-24, May.
  30. Debashis Ghosh & Moulinath Banerjee & Pinaki Biswas, 2004. "Binary isotonic regression procedures, with application to cancer biomarkers," The University of Michigan Department of Biostatistics Working Paper Series 1037, Berkeley Electronic Press.
  31. Chen, Xiwei & Vexler, Albert & Markatou, Marianthi, 2015. "Empirical likelihood ratio confidence interval estimation of best linear combinations of biomarkers," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 186-198.
  32. Yilong Zhang & Xiaoxia Han & Yongzhao Shao, 2021. "The ROC of Cox proportional hazards cure models with application in cancer studies," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 27(2), pages 195-215, April.
  33. Daniel J. Luckett & Eric B. Laber & Samer S. El‐Kamary & Cheng Fan & Ravi Jhaveri & Charles M. Perou & Fatma M. Shebl & Michael R. Kosorok, 2021. "Receiver operating characteristic curves and confidence bands for support vector machines," Biometrics, The International Biometric Society, vol. 77(4), pages 1422-1430, December.
  34. Yanqing Wang & Yingqi Zhao & Yingye Zheng, 2022. "Targeted Search for Individualized Clinical Decision Rules to Optimize Clinical Outcomes," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 14(3), pages 564-581, December.
  35. Carol Y. Lin & Lance A. Waller & Robert H. Lyles, 2012. "The likelihood approach for the comparison of medical diagnostic system with multiple binary tests," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(7), pages 1437-1454, December.
  36. Yue Wang & Jeremy Taylor, 2004. "Monotone Constrained Tensor-product B-spline with application to screening studies," The University of Michigan Department of Biostatistics Working Paper Series 1022, Berkeley Electronic Press.
  37. Kajal Lahiri & Liu Yang, 2023. "Predicting binary outcomes based on the pair-copula construction," Empirical Economics, Springer, vol. 64(6), pages 3089-3119, June.
  38. Paul Blanche & Cécile Proust-Lima & Lucie Loubère & Claudine Berr & Jean-François Dartigues & Hélène Jacqmin-Gadda, 2015. "Quantifying and comparing dynamic predictive accuracy of joint models for longitudinal marker and time-to-event in presence of censoring and competing risks," Biometrics, The International Biometric Society, vol. 71(1), pages 102-113, March.
  39. Sanjay Jain & Jónas Oddur Jónasson & Jean Pauphilet & Kamalini Ramdas, 2023. "Robust combination testing: methods and application to COVID-19 detection," Economics Series Working Papers 1009, University of Oxford, Department of Economics.
  40. Yanqing Wang & Ying‐Qi Zhao & Yingye Zheng, 2020. "Learning‐based biomarker‐assisted rules for optimized clinical benefit under a risk constraint," Biometrics, The International Biometric Society, vol. 76(3), pages 853-862, September.
  41. Holly Janes & Margaret S. Pepe, 2008. "Matching in Studies of Classification Accuracy: Implications for Analysis, Efficiency, and Assessment of Incremental Value," Biometrics, The International Biometric Society, vol. 64(1), pages 1-9, March.
  42. Tianxi Cai & Thomas A Gerds & Yingye Zheng & Jinbo Chen, 2011. "Robust Prediction of t-Year Survival with Data from Multiple Studies," Biometrics, The International Biometric Society, vol. 67(2), pages 436-444, June.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.