Illustration of the Impact of Unmeasured Confounding Within an Economic Evaluation Based on Nonrandomized Data
Background: Propensity score (PS) methods are frequently used within economic evaluations based on nonrandomized data to adjust for measured confounders, but many researchers omit the fact that they cannot adjust for unmeasured confounders. Objective: To illustrate how confounding due to unmeasured...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2017-03-01
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| Series: | MDM Policy & Practice |
| Online Access: | https://doi.org/10.1177/2381468317697711 |
| _version_ | 1819060344204558336 |
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| author | Jason R. Guertin MSc, PhD James M. Bowen BScPhm, MSc Guy De Rose BSc, MD Daria J. O’Reilly MSc, PhD Jean-Eric Tarride MA, PhD |
| author_facet | Jason R. Guertin MSc, PhD James M. Bowen BScPhm, MSc Guy De Rose BSc, MD Daria J. O’Reilly MSc, PhD Jean-Eric Tarride MA, PhD |
| author_sort | Jason R. Guertin MSc, PhD |
| collection | DOAJ |
| description | Background: Propensity score (PS) methods are frequently used within economic evaluations based on nonrandomized data to adjust for measured confounders, but many researchers omit the fact that they cannot adjust for unmeasured confounders. Objective: To illustrate how confounding due to unmeasured confounders can bias an economic evaluation despite PS matching. Methods: We used data from a previously published nonrandomized study to select a prematched population consisting of 121 patients (46.5%) who received endovascular aneurysm repair (EVAR) and 139 patients (53.5%) who received open surgical repair (OSR), in which sufficient data regarding eight measured confounders were available. One-to-one PS matching was used within this population to select two PS-matched subpopulations. The Matched PS-Smoking Excluded Subpopulation was selected by matching patients using a PS model that omitted patients’ smoking status (one of the measured confounders), whereas the Matched PS-Smoking Included Subpopulation was selected by matching patients using a PS model that included all eight measured confounders. Incremental cost-effectiveness ratios (ICERs) were assessed within both subpopulations. Results: Both subpopulations were composed of two different sets of 164 patients. Balance within the Matched PS-Smoking Excluded Subpopulation was achieved on all confounders except for patients’ smoking status, whereas balance within the Matched PS-Smoking Included Subpopulation was achieved on all confounders. Results indicated that the ICER of EVAR over OSR differed between both subpopulations; the ICER was estimated at $157,909 per life-year gained (LYG) within the Matched PS-Smoking Excluded Subpopulation, while it was estimated at $235,074 per LYG within the Matched PS-Smoking Included Subpopulation. Discussion: Although effective in controlling for measured confounding, PS matching may not adjust for unmeasured confounders that may bias the results of an economic evaluation based on nonrandomized data. |
| first_indexed | 2024-12-21T14:25:30Z |
| format | Article |
| id | doaj.art-cb1f1a3bf0a24702819b75c41cf26202 |
| institution | Directory Open Access Journal |
| issn | 2381-4683 |
| language | English |
| last_indexed | 2024-12-21T14:25:30Z |
| publishDate | 2017-03-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | MDM Policy & Practice |
| spelling | doaj.art-cb1f1a3bf0a24702819b75c41cf262022022-12-21T19:00:39ZengSAGE PublishingMDM Policy & Practice2381-46832017-03-01210.1177/2381468317697711Illustration of the Impact of Unmeasured Confounding Within an Economic Evaluation Based on Nonrandomized DataJason R. Guertin MSc, PhDJames M. Bowen BScPhm, MScGuy De Rose BSc, MDDaria J. O’Reilly MSc, PhDJean-Eric Tarride MA, PhDBackground: Propensity score (PS) methods are frequently used within economic evaluations based on nonrandomized data to adjust for measured confounders, but many researchers omit the fact that they cannot adjust for unmeasured confounders. Objective: To illustrate how confounding due to unmeasured confounders can bias an economic evaluation despite PS matching. Methods: We used data from a previously published nonrandomized study to select a prematched population consisting of 121 patients (46.5%) who received endovascular aneurysm repair (EVAR) and 139 patients (53.5%) who received open surgical repair (OSR), in which sufficient data regarding eight measured confounders were available. One-to-one PS matching was used within this population to select two PS-matched subpopulations. The Matched PS-Smoking Excluded Subpopulation was selected by matching patients using a PS model that omitted patients’ smoking status (one of the measured confounders), whereas the Matched PS-Smoking Included Subpopulation was selected by matching patients using a PS model that included all eight measured confounders. Incremental cost-effectiveness ratios (ICERs) were assessed within both subpopulations. Results: Both subpopulations were composed of two different sets of 164 patients. Balance within the Matched PS-Smoking Excluded Subpopulation was achieved on all confounders except for patients’ smoking status, whereas balance within the Matched PS-Smoking Included Subpopulation was achieved on all confounders. Results indicated that the ICER of EVAR over OSR differed between both subpopulations; the ICER was estimated at $157,909 per life-year gained (LYG) within the Matched PS-Smoking Excluded Subpopulation, while it was estimated at $235,074 per LYG within the Matched PS-Smoking Included Subpopulation. Discussion: Although effective in controlling for measured confounding, PS matching may not adjust for unmeasured confounders that may bias the results of an economic evaluation based on nonrandomized data.https://doi.org/10.1177/2381468317697711 |
| spellingShingle | Jason R. Guertin MSc, PhD James M. Bowen BScPhm, MSc Guy De Rose BSc, MD Daria J. O’Reilly MSc, PhD Jean-Eric Tarride MA, PhD Illustration of the Impact of Unmeasured Confounding Within an Economic Evaluation Based on Nonrandomized Data MDM Policy & Practice |
| title | Illustration of the Impact of Unmeasured Confounding Within an Economic Evaluation Based on Nonrandomized Data |
| title_full | Illustration of the Impact of Unmeasured Confounding Within an Economic Evaluation Based on Nonrandomized Data |
| title_fullStr | Illustration of the Impact of Unmeasured Confounding Within an Economic Evaluation Based on Nonrandomized Data |
| title_full_unstemmed | Illustration of the Impact of Unmeasured Confounding Within an Economic Evaluation Based on Nonrandomized Data |
| title_short | Illustration of the Impact of Unmeasured Confounding Within an Economic Evaluation Based on Nonrandomized Data |
| title_sort | illustration of the impact of unmeasured confounding within an economic evaluation based on nonrandomized data |
| url | https://doi.org/10.1177/2381468317697711 |
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