Literature DB >> 10900448

Comparing tumour staging and grading systems: a case study and a review of the issues, using thymoma as a model.

C B Begg1, L D Cramer, E S Venkatraman, J Rosai.   

Abstract

We consider the problem of comparing alternative cancer staging and grading systems. Statistical comparisons are on the basis of the ability to predict survival, but more qualitative criteria, such as parsimony, and distinctive prognostic separability of the categories are relevant also. Furthermore, some staging systems are clearly ordinal, while others are not. Three candidate statistical measures are studied and compared: explained variation; area under the ROC curve; and the probability of concordance of stage and survival. Each of these has individual strengths and weaknesses. A data set involving the staging of thymoma is analysed in detail to motivate the problem and illustrate the results. Copyright 2000 John Wiley & Sons, Ltd.

Entities:  

Mesh:

Year:  2000        PMID: 10900448     DOI: 10.1002/1097-0258(20000815)19:15<1997::aid-sim511>3.0.co;2-c

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  22 in total

1.  Comparing two correlated C indices with right-censored survival outcome: a one-shot nonparametric approach.

Authors:  Le Kang; Weijie Chen; Nicholas A Petrick; Brandon D Gallas
Journal:  Stat Med       Date:  2014-11-17       Impact factor: 2.373

2.  Survival prediction for terminally ill cancer patients: revision of the palliative prognostic score with incorporation of delirium.

Authors:  Emanuela Scarpi; Marco Maltoni; Rosalba Miceli; Luigi Mariani; Augusto Caraceni; Dino Amadori; Oriana Nanni
Journal:  Oncologist       Date:  2011-10-31

3.  Use of nomograms for predictions of outcome in patients with advanced bladder cancer.

Authors:  Shahrokh F Shariat; Pierre I Karakiewicz; Guilherme Godoy; Seth P Lerner
Journal:  Ther Adv Urol       Date:  2009-04

4.  Post-operative nomogram for predicting freedom from recurrence after surgery in localised breast cancer receiving adjuvant hormone therapy.

Authors:  Chafika Mazouni; Frédéric Fina; Sylvie Romain; Pascal Bonnier; L'houcine Ouafik; Pierre-Marie Martin
Journal:  J Cancer Res Clin Oncol       Date:  2014-11-30       Impact factor: 4.553

Review 5.  [Value of biomarkers in urology].

Authors:  P J Goebell; B Keck; S Wach; B Wullich
Journal:  Urologe A       Date:  2010-04       Impact factor: 0.639

Review 6.  Statistical consideration for clinical biomarker research in bladder cancer.

Authors:  Shahrokh F Shariat; Yair Lotan; Andrew Vickers; Pierre I Karakiewicz; Bernd J Schmitz-Dräger; Peter J Goebell; Nuria Malats
Journal:  Urol Oncol       Date:  2010 Jul-Aug       Impact factor: 3.498

Review 7.  Critical review of prostate cancer predictive tools.

Authors:  Shahrokh F Shariat; Michael W Kattan; Andrew J Vickers; Pierre I Karakiewicz; Peter T Scardino
Journal:  Future Oncol       Date:  2009-12       Impact factor: 3.404

8.  Lasso tree for cancer staging with survival data.

Authors:  Yunzhi Lin; Sijian Wang; Richard J Chappell
Journal:  Biostatistics       Date:  2012-12-05       Impact factor: 5.899

Review 9.  Reporting performance of prognostic models in cancer: a review.

Authors:  Susan Mallett; Patrick Royston; Rachel Waters; Susan Dutton; Douglas G Altman
Journal:  BMC Med       Date:  2010-03-30       Impact factor: 8.775

10.  Impact of Polymorphic Variations of Gemcitabine Metabolism, DNA Damage Repair, and Drug-Resistance Genes on the Effect of High-Dose Chemotherapy for Relapsed or Refractory Lymphoid Malignancies.

Authors:  Keiji Shinozuka; Hongwei Tang; Roy B Jones; Donghui Li; Yago Nieto
Journal:  Biol Blood Marrow Transplant       Date:  2015-12-29       Impact factor: 5.742

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.