Mitigating selection bias in organ allocation models.
Organ allocation
Selection bias
Survivor bias
Transplant benefit
Waitlist urgency
Journal
BMC medical research methodology
ISSN: 1471-2288
Titre abrégé: BMC Med Res Methodol
Pays: England
ID NLM: 100968545
Informations de publication
Date de publication:
21 09 2021
21 09 2021
Historique:
received:
06
05
2021
accepted:
25
08
2021
entrez:
22
9
2021
pubmed:
23
9
2021
medline:
30
9
2021
Statut:
epublish
Résumé
The lung allocation system in the U.S. prioritizes lung transplant candidates based on estimated pre- and post-transplant survival via the Lung Allocation Scores (LAS). However, these models do not account for selection bias, which results from individuals being removed from the waitlist due to receipt of transplant, as well as transplanted individuals necessarily having survived long enough to receive a transplant. Such selection biases lead to inaccurate predictions. We used a weighted estimation strategy to account for selection bias in the pre- and post-transplant models used to calculate the LAS. We then created a modified LAS using these weights, and compared its performance to that of the existing LAS via time-dependent receiver operating characteristic (ROC) curves, calibration curves, and Bland-Altman plots. The modified LAS exhibited better discrimination and calibration than the existing LAS, and led to changes in patient prioritization. Our approach to addressing selection bias is intuitive and can be applied to any organ allocation system that prioritizes patients based on estimated pre- and post-transplant survival. This work is especially relevant to current efforts to ensure more equitable distribution of organs.
Sections du résumé
BACKGROUND
The lung allocation system in the U.S. prioritizes lung transplant candidates based on estimated pre- and post-transplant survival via the Lung Allocation Scores (LAS). However, these models do not account for selection bias, which results from individuals being removed from the waitlist due to receipt of transplant, as well as transplanted individuals necessarily having survived long enough to receive a transplant. Such selection biases lead to inaccurate predictions.
METHODS
We used a weighted estimation strategy to account for selection bias in the pre- and post-transplant models used to calculate the LAS. We then created a modified LAS using these weights, and compared its performance to that of the existing LAS via time-dependent receiver operating characteristic (ROC) curves, calibration curves, and Bland-Altman plots.
RESULTS
The modified LAS exhibited better discrimination and calibration than the existing LAS, and led to changes in patient prioritization.
CONCLUSIONS
Our approach to addressing selection bias is intuitive and can be applied to any organ allocation system that prioritizes patients based on estimated pre- and post-transplant survival. This work is especially relevant to current efforts to ensure more equitable distribution of organs.
Identifiants
pubmed: 34548017
doi: 10.1186/s12874-021-01379-7
pii: 10.1186/s12874-021-01379-7
pmc: PMC8454078
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
191Subventions
Organisme : NHLBI NIH HHS
ID : F31 HL139338
Pays : United States
Organisme : NIDDK NIH HHS
ID : R01 DK070869
Pays : United States
Informations de copyright
© 2021. The Author(s).
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