The development and execution of medical prediction models Book Section
| Authors: | Kattan, M. W.; Gonen, M.; Scardino, P. T. |
| Editors: | Taktak, A. F. G.; Fisher, A. C. |
| Article/Chapter Title: | The development and execution of medical prediction models |
| Abstract: | Multivariable regression models are firmly established as the standard method in medical literature for obtaining adjusted estimates and adjusted tests of association. Prediction of individual patient outcome is a different area than testing for associations. The purpose of this chapter is to present the strategy developed by others that is used for building and implementing several regression-based prediction models. One of the software products is the most common prognostic tools in cancer for the personal digital assistant, according to a survey by the American Society of Clinical Oncology. One of the specific messages conveyed here is that building prediction models require a different strategy even though the statistical models are the same. This chapter emphasizes on achieving high predictive accuracy as the goal rather than building a model with statistically significant factors. It also reviews the importance of model validation and calibration as indispensable steps before finalizing a predictive model.It focuses on simple and effective communication of the results. The tool used for this purpose is the nomogram. This graphical depiction of a multivariable model has been used for a long time but not as widely as one might expect, given its advantages. © 2007 Elsevier B.V. All rights reserved. |
| Book Title: | Outcome Prediction in Cancer |
| ISBN: | 978-0-444-52855-1 |
| Publisher: | Elsevier Inc. |
| Publication Place: | Amsterdam, Netherlands |
| Date Published: | 2007-01-01 |
| Start Page: | 443 |
| End Page: | 455 |
| Language: | English |
| DOI: | 10.1016/B978-044452855-1/50018-0 |
| PROVIDER: | scopus |
| DOI/URL: | |
| Notes: | --- - Book Chapter 16 - 9780444528551 (ISBN) - "Export Date: 1 October 2013" - "Source: Scopus" |
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