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Use of OSWALD for analyzing longitudinal data with informative dropout
Amy E Begley1, Gong Tang, Sati Mazumdar
1Western Psychiatric Institute and Clinic, Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA. begleyae@upmc.edu
OSWALD software analyzes longitudinal data with dropouts. It shows less bias for informative dropout when assuming informative dropout missing data mechanisms.
Area of Science:
- Biostatistics
- Statistical Software
Background:
- Longitudinal data analysis is complex, especially with missing data due to dropouts.
- Limited public domain software exists for analyzing longitudinal data with dropouts.
Purpose of the Study:
- To introduce and illustrate the use of OSWALD (Object-oriented Software for the Analysis of Longitudinal Data) for longitudinal data analysis.
- To evaluate OSWALD's performance under different dropout mechanisms (CRD, RD, ID).
Main Methods:
- OSWALD software implemented in S-PLUS.
- Analysis of a psychiatric clinical trial.
- Simulation study with three dropout mechanisms (CRD, RD, ID).
- Supplementation of OSWALD with a bootstrap procedure for standard error estimation.
Main Results:
- OSWALD demonstrates reduced parameter estimate bias for informative dropout (ID) data when the ID missing data assumption is used.
- Standard error estimates are not provided by OSWALD under an ID mechanism.
- Bootstrap procedure effectively derives standard errors for OSWALD analyses.
Conclusions:
- OSWALD is a valuable tool for analyzing longitudinal data with dropouts.
- Appropriate conclusions depend on the assumed dropout mechanism.
- The bootstrap method enhances OSWALD's utility by providing standard errors.
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