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Analytic, Sensitivity and Graphical Methods for Investigating Dropout Data

Award Information

Agency:
Department of Health and Human Services
Branch:
N/A
Award ID:
88973
Program Year/Program:
2008 / SBIR
Agency Tracking Number:
CA135996
Solicitation Year:
N/A
Solicitation Topic Code:
N/A
Solicitation Number:
N/A
Small Business Information
DATA NUMERICA INSTITUTE, INC.
6120 149TH AVE SE BELLEVUE, WA 98006-4620
View profile »
Woman-Owned: No
Minority-Owned: No
HUBZone-Owned: Yes
 
Phase 1
Fiscal Year: 2008
Title: Analytic, Sensitivity and Graphical Methods for Investigating Dropout Data
Agency: HHS
Contract: 1R44CA135996-01
Award Amount: $113,149.00
 

Abstract:

DESCRIPTION (provided by applicant): Longitudinal data are very common in sociological, behavioral and biomedical researches. The data may come from longitudinal clinical trials, community surveys, family studies or spatial-temporal studies to investigate some health outcomes. The responses are measured repeatedly over a period of time, and it could be either continuous or discrete. Typically, the interest focuses on the impact of some treatment intervention or the pattern of change in response over time. S uch data could be very complex when there are multiple levels of data structures. In addition, it is often the case that there exists missing response in the data. In the analysis of longitudinal data, the missing data mechanisms have to be incorporated in order to derive valid results. In the most severe case, the missing mechanism is not ignorable, i.e. one has to model simultaneously the observed and unobserved outcome variables and the missing indicator. On the other hand, those modeling assumptions are often not testable, and one has to rely on the sensitivity analysis and graphical methods to study the robustness of the assumptions. We are interested in developing software that incorporates the analytic methods, sensitivity analysis and graphical metho ds in one software. Such software is not available in the market yet. We will develop a user-friendly system with web and desktop applications. We will also develop algorithms and dynamic graphical methods for the analysis of dropout data and the diagnosis of modeling assumptions. The software will be useful to biomedical researchers working on sociological, behavioral and biomedical studies with complex data structures. Manuscripts and course packs will be developed to assist practitioners in applying appr opriate methods and tools in their studies. PUBLIC HEALTH RELEVANCE This project aims at statistical software for the analysis of complex longitudinal data with non-ignorable missing responses. The methods and software will be useful for biomedical studies , e.g. longitudinal clinical trials. We will develop algorithms, analytic methods and dynamic graphical tools for model fitting, model diagnosis and justification of assumptions.

Principal Investigator:

Edward C. Chao
4255917944
ECHAO@DATANUMERICA.COM

Business Contact:


kwang@datanumerica.com
Small Business Information at Submission:

DATA NUMERICA INSTITUTE, INC.
DATA NUMERICA INSTITUTE, INC. 6120 149TH AVE SE BELLEVUE, WA 98006

EIN/Tax ID: 912061418
DUNS: N/A
Number of Employees: N/A
Woman-Owned: No
Minority-Owned: No
HUBZone-Owned: No