Analytic, Sensitivity and Graphical Methods for Investigating Dropout Data

Award Information
Agency:
Department of Health and Human Services
Branch
n/a
Amount:
$754,469.00
Award Year:
2009
Program:
SBIR
Phase:
Phase II
Contract:
4R44CA135996-02
Agency Tracking Number:
CA135996
Solicitation Year:
2009
Solicitation Topic Code:
n/a
Solicitation Number:
PHS2009-2
Small Business Information
DATA NUMERICA INSTITUTE, INC.
DATA NUMERICA INSTITUTE, INC., 6120 149TH AVE SE, BELLEVUE, WA, 98006
Hubzone Owned:
N
Socially and Economically Disadvantaged:
N
Woman Owned:
N
Duns:
003849838
Principal Investigator:
EDWARD CHAO
(425) 591-7944
ECHAO@DATANUMERICA.COM
Business Contact:
KATHERINE WANG
(425) 591-7944
kwang@datanumerica.com
Research Institution:
n/a
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. Such 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 methods 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 appropriate methods and tools in their studies.

* information listed above is at the time of submission.

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