Linked Life Data

17885867

Source:http://linkedlifedata.com/resource/pubmed/id/17885867

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
5
pubmed:dateCreated
2007-9-21
pubmed:abstractText
Assessing treatment effectiveness in longitudinal observational data is complicated as patients may change medications at any time. To illustrate, three general statistical strategies were utilized to assess treatment effectiveness in an observational schizophrenia study: ignoring, eliminating, and modeling the switching. Differential switching rates produced dramatic differences in estimates of treatment effectiveness across the strategies, with p-values ranging from nearly 0 to almost 1. Ignoring the treatment switching by utilizing intent-to-treat approaches resulted in treatment effect estimates of near zero. Various methods of eliminating the switching, such as epoch analyses and on-drug subset analyses, along with use of marginal structural models generated reasonably consistent non-zero treatment effect estimates. When analyzing longitudinal observational data, researchers must understand the options, key concepts and assumptions behind the various statistical methods available. Marginal structural models are a promising approach to estimation of causal treatment effects in such data.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:issn
1054-3406
pubmed:author
pubmed:issnType
Print
pubmed:volume
17
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
809-26
pubmed:dateRevised
2008-11-21
pubmed:meshHeading
pubmed:year
2007
pubmed:articleTitle
Analysis of treatment effectiveness in longitudinal observational data.
pubmed:affiliation
Outcomes Research, Eli Lilly & Company, Indianapolis, Indiana, USA. d.faries@lilly.com
pubmed:publicationType
Journal Article