|
Activity Number:
|
88
|
|
Type:
|
Invited
|
|
Date/Time:
|
Monday, August 7, 2006 : 8:30 AM to 10:20 AM
|
|
Sponsor:
|
Biopharmaceutical Section
|
| Abstract - #305203 |
|
Title:
|
Data Mining Trees: Mining Clinical Trials Data
|
|
Author(s):
|
Javier Cabrera*+
|
|
Companies:
|
Rutgers University
|
|
Address:
|
Statistics and Biostatistics, Piscataway, NJ, 08854,
|
|
Keywords:
|
datamining ; clinical trials ; bump hunting ; recursive partition
|
|
Abstract:
|
Mining clinical trial data is becoming an important tool for extracting information that may help design better clinical trials. One important objective is to identify the characteristics of a subset of cases that responds much differently than the rest of the cases. For example, what are the characteristics of placebo respondents or the highest respondents or lowest respondents to some treatment? Are secondary endpoints higher for some group of patients? The two existing methodologies that try to address these issues are "bump hunting" and "recursive partitioning." We introduce data mining trees as a method that compromises between recursive partitioning and bump hunting. We illustrate the methodology with examples that use clinical trial data. This work is a collaboration with J. Alvir; H. Nguyen; M. Lakshminarayanan, Pfizer; and D. Amaratunga, JnJPRD.
|
- The address information is for the authors that have a + after their name.
- Authors who are presenting talks have a * after their name.
Back to the full JSM 2006 program |
