Mining Clinical Trial Data

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Author(s): Jose Ma. J. Alvir (Pfizer Inc., USA)and Javier Cabrera (Rutgers University, USA)
Copyright: 2007
Pages: 17
Source title: Knowledge Discovery and Data Mining: Challenges and Realities
Source Author(s)/Editor(s): Xingquan Zhu (University of Vermont, USA)and Ian Davidson (State University of New York at Albany, USA)
DOI: 10.4018/978-1-59904-252-7.ch003

Keywords: Data Mining and Databases / Information Science Reference / Knowledge Discovery / Library & Information Science

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Abstract

Mining clinical trails is becoming an important tool for extracting information that might help design better clinical trials. One important objective is to identify characteristics of a subset of cases that responds substantially differently than the rest. For example, what are the characteristics of placebo respondents? Who have the best or worst response to a particular treatment? Are there subsets among the treated group who perform particularly well? In this chapter we give an overview of the processes of conducting clinical trials and the places where data mining might be of interest. We also introduce an algorithm for constructing data mining trees that are very useful for answering the above questions by detecting interesting features of the data. We illustrate the ARF method with an analysis of data from four placebo-controlled trials of ziprasidone in schizophrenia.

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