alexa Developing Prediction Models from Results of Regression Analysis: Woodpecker Technique
ISSN: 2155-6180

Journal of Biometrics & Biostatistics
Open Access

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Research Article

Developing Prediction Models from Results of Regression Analysis: Woodpecker Technique

Alexander Goldfarb Rumyantzev1*, Ning Dong2, Sergei Krikov3, Olga Efimova4, Lev Barenbaum5 and Shiva Gautam6

1Division of Nephrology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA

2Department of Pulmonary and Critical Care Medicine, Massachusetts General Hospital, Boston, MA, USA

3Division of Medical Genetics, Department of Pediatrics, University of Utah, Salt Lake City, UT, USA

4Department of Pathology, University of Utah, Salt Lake City, UT, USA

5Intelligent Decision, Inc., Izhevsk, Russia

6Department of Internal Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA

*Corresponding Author:
Alexander Goldfarb-Rumyantzev, MD, PhD
Division of Nephrology
Beth Israel Deaconess Medical Center and Harvard Medical School
185 Pilgrim Rd, FA-832, Boston
MA 02215, USA
Tel: 617-632-9880
Fax: 617-667-5276
E-mail: [email protected]

Received Date: January 25, 2016; Accepted Date: January 30, 2016; Published Date: February 08, 2016

Citation: Rumyantzev AG, Dong N, Krikov S, Efimova O, Barenbaum L, et al. (2016) Developing Prediction Models from Results of Regression Analysis: Woodpecker Technique. J Biom Biostat 7:276. doi:10.4172/2155-6180.1000276

Copyright: © 2016 Rumyantzev AG, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Background: Developing medical prediction models remains time and labor consuming. We propose the approach where information collected from published epidemiological outcome studies is quickly converted into prediction models. Methods: We used general expressions for regression models to derive prediction formulae defining the probability of the outcome and relative risk indicator. Risk indicator (R) is calculated as a linear combination of predictors multiplied by regression coefficients and then is placed on the scale of 0 to 10 for interpretability. Prediction expression for the probability (P) of the outcome is derived from general expression for logistic regression and proportional hazard models. The intercept is calculated based upon the outcome rate in the population and the risk indicator assigned to a subject representing mean characteristics of the population (Ȓ). We also consider linear expression where probability of outcome is the product of risk indicator and the ratio of observed outcome rate and Ȓ. Results: These models were explored and compared in a numeric simulation exercise and also using real data obtained from NHANES dataset. All three expressions generate very similar predictions in the lower categories of risk indicator. In the groups with the higher value of risk indicator linear expression tends to predict lower probability than exponential expressions and also lower than observed. Conclusions: We demonstrated simple technique (named Woodpecker™) that might allow deriving functional prediction model and risk stratification tool from the report of clinical outcome study using multivariate regression model.

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