Research Design and Biostatistics

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Overview and Vision:

The Rockefeller University Hospital Department for Research Design and Biostatistics supports the Center's mission to provide the safest and highest quality research for advancing scientific knowledge to improve the care of future patients around the world. This mission is achieved through collaboration with both investigators and other Clinical Research Resources, as outlined in "New Statistical Paradigms Leading to Web-Based Tools for Clinical/Translational Science" presented at the May 23, 2005 NIH Roadmap meeting "Enhancing the Discipline of Clinical and Translational Research."

Organization:

The Department of Research Design and Biostatistics is headed by the Center's Biostatistician, Dr. Knut M. Wittkowski, who acts as a member of the Advisory Committee for Clinical and Translational Science (ACCTS), reviews protocols for the IRB, and assists investigators with experimental design and statistical analyses. Support is provided at four levels: (1) biostatistics consultation, (2) application development for individual investigators, (3) project assistance with data entry, graphics, and analyses, and (4) data management. The Center's Research Design and Biostatistics core includes a data manager and an application programmer (biostatistical support for Center investigators) as well as a project manager and a statistical programmer (development of knowledge based Web tools). The Biomedical informatics Core provides additional support, including (1) database design and data management, (2) data safety and security, (3) Web-based education modules, and (4) installation and support of statistical analysis software.

Scope of Support:

The services offered by the Research Design and Biostatistics core include:

Research Areas:

A novel statistical approach to multivariate data based on u-statistics (µStat) differs from traditional approaches in that it is 'intrinsically valid', so that time consuming steps for empirical validation can be skipped. Moreover, it allows one to search not only for correlated variables along individual pathways, but also for identifying collaborating (parallel) pathways. These methods have been used in:

Currently, research focuses on

To facilitate use of statistical methods in multivariate settings, we are developing knowledge based user interfaces based on an ontology for describing not only the variables, but also conditions under which data were collected. One possible ontology that can be used for this propose was developed by Dr. Wittkowski as part of the PANOS project.

Biostatistical tools:

Based on the emerging results from these research projects, several tools are integrated and developed. The tools made available for traditional statistical analyses include:

In addition, several tools implementing recently developed statistical methods based on u-statistics for multivariate ordinal data (µStat) are currently available to assist clinical investigators in addressing the novel challenges posed by haplotypes, epistatic interaction, and genomic pathways. The µStat tools received the 2005 Insightful Impact Award and include:

Research Design Tools:

WISDOM (Web-based Interface for Statistical Design, Organization, and Management) aims at providing investigators with integrated design, compliance, and biostatistics support during all stages of research:

WISDOM is based on an innovative meta data model that allows acquisition of information not only about the process of the study, but also about its background and purpose. WISDOM exerts its support by hosting a comprehensive knowledge base on design and conduct of studies and by exchanging data and knowledge between various software through standard interfaces, thereby facilitating:

Integration with other CTSA Activities:

Together, these integrated innovative approaches in method development, knowledge acquisition, teaching, programming, and data management, enable several CTSA activities (outlined in the Sections indicated):

References:

WITTKOWSKI, KM (1988) Building a statistical expert system with knowledge bases of different levels of abstraction. In: Edwards, D; Raune, NE (eds) Compstat 1988 - Proceedings in Computational Statistics. Heidelberg, D: Physica, 129-34

WITTKOWSKI, KM (1989) On the use of statistical expert systems for supporting quality assurance in analysis of clinical trials. In: Rienhoff, O; Piccolo, U; Schneider, B (eds) Expert Systems and Decision Support in Medicine. Berlin, D: Springer, 182-7

WITTKOWSKI, KM; LIU, X (2002) A statistically valid alternative to the TDT. Hum Hered 54: 157-64. (22513746)

WITTKOWSKI, KM (2003) Novel Methods for Multivariate Ordinal Data applied to Genetic Diplotypes, Genomic Pathways, Risk Profiles, and Pattern Similarity. Computing Science and Statistics 35: 626-46

WITTKOWSKI, KM; LEE, E; et al. (2004) Combining several ordinal measures in clinical studies. Stat Med 23: 1579-92

SUAREZ-FARINAS, M; PELLEGRINO, M; et al. (2005) Harshlight: a "corrective make-up" program for microarray chips. BMC Bioinformatics 6: 294

WITTKOWSKI, KM (2006) Statistical methods for multivariate ordinal data which are used for data base driven decision support. US Patent 7,072,794. U.S.A.: The Rockefeller University: contact: RU Office of Technology Transfer

Dept. Web Page| E-Mail | Publications