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Title
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Can we account for selection bias? A comparison between bare metal and drug-elting stents
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Author
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Abstract
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| Objective: In this article we investigate the possibility to account for selection bias in observational data by using econometric techniques. Objective: In this article we investigate the possibility to account for selection bias in observational data by using econometric techniques. Methods: One-year costs of 15,237 patients who received a drug-eluting stent (DES) or a bare metal stent (BMS) in Belgium in 2004 were compared. The treatment choice between DES and BMS could be influenced by patient characteristics; therefore, cost estimates could be biased by overt and/or hidden selection bias. Overt bias was addressed by regression adjustment and propensity score matching. Hidden selection bias was dealt with by using an instrumental variable (IV) approach. Results: Due to the higher purchase price DES patients incur higher (unadjusted) costs in the short-term; these costs are, however, compensated in the long-term due to less in-stent restenosis and hospitalizations. Analyses indicated that, for the diabetic population, the null hypothesis of similar average 1-year costs of patients receiving a BMS or DES cannot be rejected. For the non-diabetic patients a significant cost difference between BMS and DES patients was found. It cannot be ruled out that the treatment-effect model does not correct for all observable or unobservable characteristics and that the estimated treatment effect is biased, possibly due to weak instruments. Conclusion: It is interesting and necessary to explore the use of econometric tools in cost and cost effectiveness analysis to investigate the effect of a technology in everyday practice and to take into account patient and disease characteristicsanduncertainty. Further research ishowever necessary to investigate how we can fully correct for selection bias when using observational data.between DES and BMS could be influenced by patient characteristics; therefore, cost estimates could be biased by overt and/or hidden selection bias. Overt bias was addressed by regression adjustment and propensity score matching. Hidden selection bias was dealt with by using an instrumental variable (IV) approach. Results: Due to the higher purchase price DES patients incur higher (unadjusted) costs in the short-term; these costs are, however, compensated in the long-term due to less in-stent restenosis and hospitalizations. Analyses indicated that, for the diabetic population, the null hypothesis of similar average 1-year costs of patients receiving a BMS or DES cannot be rejected. For the non-diabetic patients a significant cost difference between BMS and DES patients was found. It cannot be ruled out that the treatment-effect model does not correct for all observable or unobservable characteristics and that the estimated treatment effect is biased, possibly due to weak instruments. Conclusion: It is interesting and necessary to explore the use of econometric tools in cost and cost effectiveness analysis to investigate the effect of a technology in everyday practice and to take into account patient and disease characteristicsanduncertainty. Further research ishowever necessary to investigate how we can fully correct for selection bias when using observational data. |
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Language
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English
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Source (journal)
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Value in health. - Oxford
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Publication
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Oxford
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2011
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ISSN
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1098-3015
[print]
1524-4733
[online]
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DOI
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10.1016/J.JVAL.2010.10.014
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Volume/pages
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14
:1
(2011)
, p. 3-14
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ISI
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000299039700002
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Full text (Publisher's DOI)
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Full text (open access)
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