Author:

Keywords:

sensitivity; modeling; longitudinal; data; dropout, Science & Technology, Life Sciences & Biomedicine, Health Care Sciences & Services, Bivariate ordinal outcomes, Latent variable, Maximum marginal likelihood, Non-ignorable dropout, Non-parametric density estimation, Repeated measures, Sensitivity analysis, Shared random effects, Threshold crossing model, 15 Commerce, Management, Tourism and Services, Health Policy & Services

Abstract:

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. © Springer Science+Business Media LLC 2006.