The application of sample selection models to outcomes research: the case of evaluating the effects of antidepressant therapy on resource utilization

Non‐randomized studies of treatment effects have come under criticism because of their failure to control for potential biases introduced by unobserved variables correlated with treatment selection and outcomes. This paper describes the basic concepts of sample selection models – a technique used wi...

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Bibliographic Details
Published in:Statistics in medicine 1998-09, Vol.17 (17), p.1943-1958
Main Authors: Crown, William H., Obenchain, Robert L., Englehart, Luella, Lair, Tamra, Buesching, Don P., Croghan, Thomas
Format: Article
Language:English
Subjects:
Antidepressive Agents - economics
Antidepressive Agents - therapeutic use
Biological and medical sciences
Computerized, statistical medical data processing and models in biomedicine
Cost-Benefit Analysis
Depressive Disorder - drug therapy
Depressive Disorder - economics
Female
Health Resources - economics
Health Resources - statistics & numerical data
Humans
Male
Medical sciences
Medical statistics
Models, Statistical
Outcome Assessment (Health Care) - statistics & numerical data
Patient Selection
Sick Role
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Summary:Non‐randomized studies of treatment effects have come under criticism because of their failure to control for potential biases introduced by unobserved variables correlated with treatment selection and outcomes. This paper describes the basic concepts of sample selection models – a technique used widely in the economics evaluation literature for nearly two decades – and discusses the potential role of these models in outcomes research. In addition, it presents a case study of the application of the sample selection modelling approach to evaluation of the effects of antidepressant therapies on medical expenditures for physician services. This case study presents empirical comparisons of alternative model specifications and discusses practical issues in evaluation of sample selection models. We demonstrate that, in this particular case, sample selection models yield very different conclusions regarding treatment effects than traditional ordinary least squares regression. © 1998 John Wiley & Sons, Ltd.
ISSN:0277-6715
1097-0258
DOI:10.1002/(SICI)1097-0258(19980915)17:17<1943::AID-SIM885>3.0.CO;2-0