Mining Clinical Trial Data

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Author(s): Jose Ma. J. Alvir (Pfizer Inc., USA), Javier Cabrera (Rutgers University, USA), Frank Caridi (Pfizer Inc., USA)and Ha Nguyen (Pfizer Inc., USA)
Copyright: 2008
Pages: 19
Source title: Data Warehousing and Mining: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): John Wang (Montclair State University, USA)
DOI: 10.4018/978-1-59904-951-9.ch231

Keywords: Data Mining and Databases / Data Warehousing / Information Science Reference / 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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