Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/9753
Title: Incomplete Data in Clinical Studies: Analysis, Sensitivity, and Sensitivity Analysis
Authors: MOLENBERGHS, Geert 
Issue Date: 2009
Publisher: DRUG INFORMATION ASSOC
Source: DRUG INFORMATION JOURNAL, 43(4). p. 409-429
Abstract: Statistical analysis often extends beyond the data available. This is especially true when data are incompletely recorded because ad hoc as well as model-based approaches are rooted not only in the observed data and the mechanism governing missingness, but also in the unobserved given the observed data. Other instances of this phenomenon include but are not limited to censored time-to-event data, random effects models, and latent class approaches. One needs to be aware of (1) changes in results and intuition relative to complete-data analysis; (2) the assumptions under which such approaches are valid; (3) the sensitivities implied by departures; and (4) in response to these, what sensitivity analysis avenues are available. This article provides a bird's-eye perspective on these. Some of the developments are illustrated using data from a clinical trial in onychomycosis.
Notes: Univ Hasselt, Ctr Stat, B-3590 Diepenbeek, Belgium.
Keywords: Linear mixed model; Missing at random; Missing completely at random; Non-future dependence; Pattern-mixture model; Selection model; Shared-parameter model;linear mixed model; missing at random; missing completely at random; non-future dependence; pattern-mixture model; selection model; shared-parameter model
Document URI: http://hdl.handle.net/1942/9753
ISSN: 0092-8615
ISI #: 000267877500004
Rights: (c) 2009 Drug Information Association Inc.
Category: A1
Type: Journal Contribution
Validations: ecoom 2010
Appears in Collections:Research publications

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