alexa Combining Prediction Models in a Linear Way: Results of Numeric Simulation
ISSN: 2155-6180

Journal of Biometrics & Biostatistics
Open Access

Like us on:
OMICS International organises 3000+ Global Conferenceseries Events every year across USA, Europe & Asia with support from 1000 more scientific Societies and Publishes 700+ Open Access Journals which contains over 50000 eminent personalities, reputed scientists as editorial board members.

Open Access Journals gaining more Readers and Citations

700 Journals and 15,000,000 Readers Each Journal is getting 25,000+ Readers

This Readership is 10 times more when compared to other Subscription Journals (Source: Google Analytics)

Research Article

Combining Prediction Models in a Linear Way: Results of Numeric Simulation

Alexander Goldfarb-Rumyantzev1* and Ning Dong2

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

*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: Goldfarb-Rumyantzev A, Dong N (2016) Combining Prediction Models in a Linear Way: Results of Numeric Simulation. J Biom Biostat 7:275. doi:10.4172/2155-6180.1000275

Copyright: © 2016 Goldfarb-Rumyantzev A, 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.



Background: Using standard expressions for logistic regression and proportional hazard models and data from published outcome studies might allow generating prediction models and risk stratification tools in a more streamline fashion. However it might require combining the models, adding or removing predictors. The feasibility of this approach has been examined here. Methods: The outcome of this simulation study is mortality. The simulation exercise was based on the imaginary population of 20,000 subjects whose mortality was completely determined by five variables in the specified logistic regression model. In the first simulation exercise using “full model”, we evaluated the option of combining the results of two separate studies (studies A and B) each based on subset of the population. In the second simulation exercise studies A and B were based on limited number of predictors. Each simulation was repeated 50 times. Results: Both simulation exercises demonstrated the robustness of the model and feasibility of adding or removing predictors to/from the model. We also compared the results of linear model to the more complex exponential model using all five predictors. In subjects with lower risk indicator the outcome of linear model is similar to the outcome of the logistic regression model and to the true outcome rate, however it underestimates the risk in the high-risk groups. On the other hand, logistic regression model is accurate compared to actual outcomes. This confirms our hypothesis that dropping or adding variables should not distort the prediction in any noticeable way. Conclusions: Simple linear combination of prediction models, adding or removing predictors do not cause distortion of the model and predictions remain robust. Prediction of linear model is similar to exponential model, except the former underestimate the outcome in the high risk groups.


Share This Page

Additional Info

Loading Please wait..
Peer Reviewed Journals
Make the best use of Scientific Research and information from our 700 + peer reviewed, Open Access Journals
International Conferences 2017-18
Meet Inspiring Speakers and Experts at our 3000+ Global Annual Meetings

Contact Us

Agri, Food, Aqua and Veterinary Science Journals

Dr. Krish

[email protected]

1-702-714-7001 Extn: 9040

Clinical and Biochemistry Journals

Datta A

[email protected]

1-702-714-7001Extn: 9037

Business & Management Journals


[email protected]

1-702-714-7001Extn: 9042

Chemical Engineering and Chemistry Journals

Gabriel Shaw

[email protected]

1-702-714-7001 Extn: 9040

Earth & Environmental Sciences

Katie Wilson

[email protected]

1-702-714-7001Extn: 9042

Engineering Journals

James Franklin

[email protected]

1-702-714-7001Extn: 9042

General Science and Health care Journals

Andrea Jason

[email protected]

1-702-714-7001Extn: 9043

Genetics and Molecular Biology Journals

Anna Melissa

[email protected]

1-702-714-7001 Extn: 9006

Immunology & Microbiology Journals

David Gorantl

[email protected]

1-702-714-7001Extn: 9014

Informatics Journals

Stephanie Skinner

[email protected]

1-702-714-7001Extn: 9039

Material Sciences Journals

Rachle Green

[email protected]

1-702-714-7001Extn: 9039

Mathematics and Physics Journals

Jim Willison

[email protected]

1-702-714-7001 Extn: 9042

Medical Journals

Nimmi Anna

[email protected]

1-702-714-7001 Extn: 9038

Neuroscience & Psychology Journals

Nathan T

[email protected]

1-702-714-7001Extn: 9041

Pharmaceutical Sciences Journals

John Behannon

[email protected]

1-702-714-7001Extn: 9007

Social & Political Science Journals

Steve Harry

[email protected]

1-702-714-7001 Extn: 9042

© 2008-2017 OMICS International - Open Access Publisher. Best viewed in Mozilla Firefox | Google Chrome | Above IE 7.0 version