Linked Life Data

18631970

Source:http://linkedlifedata.com/resource/pubmed/id/18631970

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3
pubmed:dateCreated
2008-7-17
pubmed:abstractText
Decision analytic policy models for Alzheimer's disease (AD) enable researchers and policy makers to investigate questions about the costs and benefits of a wide range of existing and potential screening, testing, and treatment strategies. Such models permit analysts to compare existing alternatives, explore hypothetical scenarios, and test the strength of underlying assumptions in an explicit, quantitative, and systematic way. Decision analytic models can best be viewed as complementing clinical trials both by filling knowledge gaps not readily addressed by empirical research and by extrapolating beyond the surrogate markers recorded in a trial. We identified and critiqued 13 distinct AD decision analytic policy models published since 1997. Although existing models provide useful insights, they also have a variety of limitations. (1) They generally characterize disease progression in terms of cognitive function and do not account for other distinguishing features, such as behavioral symptoms, functional performance, and the emotional well-being of AD patients and caregivers. (2) Many describe disease progression in terms of a limited number of discrete states, thus constraining the level of detail that can be used to characterize both changes in patient status and the relationships between disease progression and other factors, such as residential status, that influence outcomes of interest. (3) They have focused almost exclusively on evaluating drug treatments, thus neglecting other disease management strategies and combinations of pharmacologic and nonpharmacologic interventions. Future AD models should facilitate more realistic and compelling evaluations of various interventions to address the disease. An improved model will allow decision makers to better characterize the disease, to better assess the costs and benefits of a wide range of potential interventions, and to better evaluate the incremental costs and benefits of specific interventions used in conjunction with other disease management strategies.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
May
pubmed:issn
1552-5279
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
4
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
212-22
pubmed:meshHeading
pubmed:year
2008
pubmed:articleTitle
Decision analytic models for Alzheimer's disease: state of the art and future directions.
pubmed:affiliation
Center for the Evaluation of Value and Risk in Health, Institute for Clinical Research and Health Policy Studies, Tufts-New England Medical Center, Boston, MA, USA. jcohen@tufts-nemc.org
pubmed:publicationType
Journal Article, Review, Research Support, Non-U.S. Gov't