A sensitivity approach to modeling longitudinal bivariate ordered data subject to informative dropouts

Incomplete data abound in epidemiological and clinical studies. When the missing data process is not properly investigated, inferences may be misleading. An increasing number of models that incorporate nonrandom incomplete data have become available. At the same time, however, serious doubts have ar...

Full description

Saved in:
Bibliographic Details
Published inHealth services and outcomes research methodology Vol. 6; no. 1-2; pp. 37 - 57
Main Authors Todem, D., Kim, K., Lesaffre, E.
Format Journal Article
LanguageEnglish
Published Dordrecht Springer Nature B.V 01.06.2006
Subjects
Antidepressants
Bias
Biostatistics
Epidemiology
Longitudinal studies
Mental depression
Missing data
Panic attacks
Patients
Psychiatry
School dropouts
Sensitivity
Sensitivity analysis
Studies
Online AccessGet full text

Cover

More Information
Summary:Incomplete data abound in epidemiological and clinical studies. When the missing data process is not properly investigated, inferences may be misleading. An increasing number of models that incorporate nonrandom incomplete data have become available. At the same time, however, serious doubts have arisen about the validity of these models, known to rely on strong and unverifiable assumptions. A common conclusion emerging from the current literature is the clear need for a sensitivity analysis. We propose in this paper a detailed sensitivity analysis using graphical and analytical techniques to understand the impact of missing-data assumptions on inferences. Specifically, we explore the influence of perturbing a missing at random model locally in the direction of non-random dropout models. Data from a psychiatric trial are used to illustrate the methodology. [PUBLICATION ABSTRACT]
Bibliography:SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ISSN:1387-3741
1572-9400
DOI:10.1007/s10742-006-0008-x