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Abstract

Grant Number:	1R43RR017862-01A1
PI Name:	SCHIMERT, JAMES
PI Email:	jschimert@insightful.com
PI Title:
Project Title:	Software to Handle Missing Values in Large Data

Abstract: DESCRIPTION (provided by applicant): This SBIR aims to produce commercial software for handling missing data in large data sets, where the goal is data mining and knowledge discovery. There may be a large number of subjects, variables, or both. Examples include microarray data, surveys, genomic data, and high throughput screening data. Handling missing data is one important step of careful data preparation, which is key to the success of an entire project. Missing values often arise in medical data. This is an obstacle because many data mining tools either require complete data or are not robust to missing data. Principled methods of handling missing data are computationally intensive. Therefore computational feasibility is a challenge to handling missing values in large data sets. Phase I work will explore strategies such as sampling, constraining parameters, and monotone data algorithms for model based techniques. Factor analysis and multivariate linear mixed effects models will be used to reduce the number of parameters. A variable-by-variable approach using a popular data mining technique, recursive partitioning, will also be used to impute missing values. For each of the methods, we will write prototype software and test performance on missing data patterns simulated on real data. Several ad hoc techniques will serve as a baseline for comparison. Experience writing prototypes and using them in simulations will lead to preliminary software design that will serve as the foundation of Phase II work. This proposed software will enable medical researchers to gain more from their data mining efforts: maximally extracting information and achieving unbiased predictions, despite missing data.

Thesaurus Terms:
artificial intelligence, computer data analysis, computer program /software, computer system design /evaluation, data management, mathematical model, statistics /biometry
data collection methodology /evaluation
clinical research, human data

Institution:	INSIGHTFUL CORPORATION
	1700 WESTLAKE AVE N, STE 500
	SEATTLE, WA 98109
Fiscal Year:	2003
Department:
Project Start:	01-JUL-2003
Project End:	30-SEP-2004
ICD:	NATIONAL CENTER FOR RESEARCH RESOURCES
IRG:	ZRG1

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