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Dynamic scores

Assessment of dynamic prognostic scores

Principal investigator

Dr. Erika Graf
Institute for Medical Biometry and Medical Informatics
University Hospital Freiburg
Stefan-Meier-Str. 26, 79104 Freiburg, Germany
Phone: ++49 (0)761 203 5000
Fax: ++49 (0)761 203 6677

Researchers

Dr. Erika Graf egr@imbi.uni-freiburg.de ++49 (0)761 203 5000
Dipl. Math. Oec. Rotraut Schoop rs@imbi.uni-freiburg.de ++49 (0)761 203 7705

Summary

This project is concerned with providing a general framework for the correct assessment of dynamic prognostic scores. A prognostic score in general is a summary of patient characteristics that bears prognostic information regarding an endpoint of interest, often a survival time. It can be model-based or an 'expert guess'. The dynamic aspect is introduced when patient data is not only collected at a defined starting point of an observation period, but also at later time points when the observation (e.g. the clinical trial) is already ongoing. The repeated calculation of the prognostic score then leads to a dynamic, or updated, score.
The correct chronological treatment of prognosis time and prognosis horizon complicates the assessment and has led to ad--hoc and sometimes even incorrect approaches that unfortunately are used far too often in the applied clinical literature. In the second funding period, the project aims to pursue a unified assessment approach and compare it with the assessment measures developed so far by the project and with approaches proposed in the literature. Comparisons are to be made on a population level as well as with real data.
This research is motivated by a number of clinical problems of our cooperation partners where an urgent need to assess prognostic models using updated patient covariate information has been expressed. This includes data from intensive care units, data on elderly patients from internal medicine wards and breast cancer patients.

Publications

  • Holländer N, Augustin N, Sauerbrei W: Investigation on the improvement of prediction by bootstrap model averaging. Method Inform Med, 2006; 45: 44-50.
  • Kröger N, Milde-Langosch K, Riethdorf S, Schmoor C, Schumacher M, Zander AR, Löning T: Prognostic and predictive effects of immunohistochemical factors in high-risk primary breast cancer patients. Clin Cancer Res, 2006; 12: 159-168.
  • Müller-Berndorff H, Haas PS, Kunzmann R, Schulte Mönting J, Lübbert M: Comparison of five prognostic scoring systems, the French-American-British (FAB) and World Health Organization (WHO) classifications in patients with myelodysplastic syndromes: results of a single-center analysis. Ann Hematol, 2006; 85: 502-513.
  • Rücker G, Schoop R, Beyersmann J, Schumacher M, Zuschneid I: Are KISS data representative of German intensive care units? - Statistical issues. Method Inform Med, 2006; 45: 424-429.
  • Schumacher M, Holländer N, Schwarzer G, Sauerbrei W: Prognostic Factor Studies. In: John Crowley and Donna Pauler Ankerst (Hrsg): Handbook of Statistics in Clinical Oncology, 2. Auflage. Boca Raton, FL: Chapman & Hall /CRC, 2006; 289-333.
  • Augustin N, Sauerbrei W, Schumacher M: The practical utility of incorporating model selection uncertainty into prognostic models for survival data. Statistical Modelling, 2005; 5: 95-118.
  • Graf E: Explained variation measures in survival analysis. In: Peter Armitage, Theodore Colton (Hrsg): Encyclopedia of Biostatistics , 3. Auflage. chichester: John Wiley & Sons, Ltd, 2005; 1856-1858.
  • McShane LM, Altman DG, Sauerbrei W: Identification of clinically useful cancer prognostic factors: What are we missing? J Natl Cancer I, 2005; 97: 1023-1025.
  • Sauerbrei W: Prognostic Factors. Confusion caused by bad quality of design, analysis and reporting of many studies. In: Bier H (Hrsg): Current Research in Head and Neck Cancer. Basel: Karger, 2005; 184-200.
  • Sauerbrei W, Royston P, Schumacher M: Austin, P.C., and Tu, J.V. (2004), 'Bootstrap Methods for developing predictive models', The American Statistician, 58, 131-137: Comment by Sauerbrei, Royston, and Schumacher to reply. Am Stat, 2005; 59: 116-118.
  • Antes G, Augustin N, Beyersmann J, Caputo A, Falck-Ytter Y, Gerds T, Gerlach A, Graf E, Holländer N, Ihorst G, Lang B, Meier-Hirmer C, Musio M, Olschewski M, Roßner R, Sauerbrei W, Schlingmann J, Schmoor C, Schulgen G, Schulte Mönting J, Schwarzer G, Schumacher M: Freiburger Beiträge zur Biometrie und Klinischen Epidemiologie. Informatik, Biometrie und Epidemiologie in Medizin und Biologie, 2004; 35: 74-122.
  • Brunner M, Olschewski M, Geibel A, Bode C, Zehender M: Long-term survival after pacemaker implantation. Prognostic importance of gender and baseline patient characteristics. Eur Heart J, 2004; 25: 88-95.
  • Holländer N, Sauerbrei W, Schumacher M: Confidence intervals for the effect of a prognostic factor after selection of an 'optimal' cutpoint. Stat Med, 2004; 23: 1701-1713.
  • Lausen B, Hothorn T, Bretz F, Schumacher M: Assessment of optimal selected prognostic factors. Biometrical J, 2004; 46: 364-374.
  • Mohm JM, Rump J-A, Schulte Mönting J, Schneider J: Prognostic value of proliferative responses to HIV-1 antigen in chronically HIV-infected patients under antiretroviral therapy. J Clin Virol, 2004; 30: 239-242.
  • Royston P, Sauerbrei W: A new measure of prognostic separation in survival data. Stat Med, 2004; 23: 723-748.
  • Watermann D, Madjar H, Sauerbrei W, Hirt V, Prömpeler H, Stickeler E: Assessment of breast cancer vascularisation by Doppler ultrasound as a prognostic factor of survival. Oncol Rep, 2004; 11: 905-910
  • Wehberg S, Schumacher M: A comparison of nonparametric error rate estimation methods in classification problems. Biometrical J, 2004; 46: 35-47.
  • Schumacher M, Graf E, Gerds T: How to assess prognostic models for survival data: a case study in oncology. Method Inform Med, 2003; 42: 564-571.
  • Schwarzer G, Nagata T, Mattern D, Schmelzeisen R, Schumacher M: Comparison of fuzzy inference, logistic regression, and classification trees (CART) for the prediction of cervical lymph node metastasis in carcinoma of the tongue. Method Inform Med, 2003; 42 (5) : 572-577.
  • Riedinger F, Kuehr J, Strauch E, Schulz H, Ihorst G, Forster J, the Ozon Working Group: Natural history of hay fever and pollen sensitization, and doctors' diagnosis of hay fever and pollen asthma in German schoolchildren. Allergy, 2002; 57: 488-492.
  • Schwarzer G, Schumacher M: Artificial neural networks for diagnosis and prognosis in prostate cancer. Semin Urol Oncol, 2002; 20: 89-95.
  • Minckwitz von G, Costa SD, Raab G, Blohmer J-U, Eidtmann H, Hilfrich J, Merkle E, Jackisch C, Gademann G, Tulusan AH, Eiermann W, Graf E, Kaufmann M for the German Preoperative Adramycin-Docetaxel and German Adjuvant Breast Cancer Study Groups: Dose-dense Doxorubicin, Docetaxel, and Granlulocyte colony-stimulating factor support with or without Tamoxifen as preoperative therapy in patients with operable carcinoma of the breast. A randomized, controlled, open phase IIb study. J Clin Oncol 2001; 19: 3506-3515.
  • Schumacher M, Holländer N, Schwarzer G, Sauerbrei W: Prognostic Factor Studies. In: John Crowley (Ed.): Handbook of Statistics in Clinical Oncology. Marcel Dekker, Inc., 2001; Chapter 17: 321-378.
  • Schwarzer G, Vach W, Schumacher M: On the misuses of artificial neural networks for prognostic and diagnostic classification in oncology. Stat Med, 2000; 19: 541-561.
  • Graf E, Schmoor C, Sauerbrei W, Schumacher M: Assessment and comparison of prognostic classification schemes for survival data. Stat Med, 1999; 18: 2229-2245.
  • Graf E: Explained variation measures in survival analysis. In: Armitage P Colton T (Edt.): Encyclopedia of Biostatistics. John Wiley and Sons, Chichester, 1998; 1441-1443.
  • Kropec A, Schulgen G, Just HJ, Geiger K, Schumacher M, Daschner FD: A scoring system for nosocomial pneumonia in intensive care units. Intensive Care Med, 1996; 22: 1155-1161.
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