Literature DB >> 16011683

Adaptive risk group refinement.

Michael LeBlanc1, James Moon, John Crowley.   

Abstract

We construct interpretable prognostic rules based on a sequence of "box-shaped" regions in the predictor space indexed by the fraction of patients in the prognostic group. In addition, the method can be used as a building block to construct more general prognostic rules based on unions of boxes, or even as a tool to find multiple prognostic groups. Simulations are used to study the properties of the new method and compare it to constructing prognostic groups based on regression trees and linear proportional hazards (PH) models. We consider an example based on data from several completed clinical trials for patients with multiple myeloma.

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Year:  2005        PMID: 16011683     DOI: 10.1111/j.1541-0420.2005.020738.x

Source DB:  PubMed          Journal:  Biometrics        ISSN: 0006-341X            Impact factor:   2.571


  8 in total

1.  Adaptive index models for marker-based risk stratification.

Authors:  Lu Tian; Robert Tibshirani
Journal:  Biostatistics       Date:  2010-07-27       Impact factor: 5.899

2.  Cross-Validation of Survival Bump Hunting by Recursive Peeling Methods.

Authors:  Jean-Eudes Dazard; Michael Choe; Michael LeBlanc; J Sunil Rao
Journal:  Proc Am Stat Assoc       Date:  2014-08

3.  R package PRIMsrc: Bump Hunting by Patient Rule Induction Method for Survival, Regression and Classification.

Authors:  Jean-Eudes Dazard; Michael Choe; Michael LeBlanc; J Sunil Rao
Journal:  Proc Am Stat Assoc       Date:  2015-08

4.  Cross-validation and Peeling Strategies for Survival Bump Hunting using Recursive Peeling Methods.

Authors:  Jean-Eudes Dazard; Michael Choe; Michael LeBlanc; J Sunil Rao
Journal:  Stat Anal Data Min       Date:  2016-01-22       Impact factor: 1.051

5.  A PRIM approach to predictive-signature development for patient stratification.

Authors:  Gong Chen; Hua Zhong; Anton Belousov; Viswanath Devanarayan
Journal:  Stat Med       Date:  2014-10-27       Impact factor: 2.373

6.  A comparison of subgroup identification methods in clinical drug development: Simulation study and regulatory considerations.

Authors:  Cynthia Huber; Norbert Benda; Tim Friede
Journal:  Pharm Stat       Date:  2019-07-03       Impact factor: 1.894

7.  Identification of subgroup effect with an individual participant data meta-analysis of randomised controlled trials of three different types of therapist-delivered care in low back pain.

Authors:  Siew Wan Hee; Dipesh Mistry; Tim Friede; Sarah E Lamb; Nigel Stallard; Martin Underwood; Shilpa Patel
Journal:  BMC Musculoskelet Disord       Date:  2021-02-16       Impact factor: 2.362

8.  An Application of the Patient Rule-Induction Method to Detect Clinically Meaningful Subgroups from Failed Phase III Clinical Trials.

Authors:  Greg Dyson
Journal:  Int J Clin Biostat Biom       Date:  2021-06-28
  8 in total

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