Use of OSWALD for analyzing longitudinal data with informative dropout

  • Authors:

  • Amy E. Begley;Gong Tang;Sati Mazumdar;Patricia R. Houck;John Scott;Benoit H. Mulsant;Charles F. Reynolds, III

  • Affiliations:

  • Western Psychiatric Institute and Clinic, Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, United States;Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15213, United States;Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15213, United States;Western Psychiatric Institute and Clinic, Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, United States;Western Psychiatric Institute and Clinic, Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, United States and Department of Biostatistics, Graduate Schoo ...;Western Psychiatric Institute and Clinic, Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, United States;Western Psychiatric Institute and Clinic, Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, United States

  • Venue:
  • Computer Methods and Programs in Biomedicine
  • Year:
  • 2007

Quantified Score

Hi-index 0.00

Visualization

Abstract

OSWALD (Object-oriented Software for the Analysis of Longitudinal Data) is flexible and powerful software written for S-PLUS for the analysis of longitudinal data with dropout for which there is little other software available in the public domain. The implementation of OSWALD is described through analysis of a psychiatric clinical trial that compares antidepressant effects in an elderly depressed sample and a simulation study. In the simulation study, three different dropout mechanisms: completely random dropout (CRD), random dropout (RD) and informative dropout (ID), are considered and the results from using OSWALD are compared across mechanisms. The parameter estimates for ID-simulated data show less bias with OSWALD under the ID missing data assumption than under the CRD or RD assumptions. Under an ID mechanism, OSWALD does not provide standard error estimates. We supplement OSWALD with a bootstrap procedure to derive the standard errors. This report illustrates the usage of OSWALD for analyzing longitudinal data with dropouts and how to draw appropriate conclusions based on the analytic results under different assumptions regarding the dropout mechanism.