*Purpose: The lung allocation system in the U.S. prioritizes lung transplant candidates based on estimated pre- and post-transplant survival. However, these models do not account for all waitlist candidates; rather, they only account for those who survive on the waitlist long enough to receive transplant. Transplanted candidates may differ from un-transplanted candidates, resulting in survivor bias and inaccurate predictions.

*Methods: We propose a weighted estimation strategy to account for survivor bias in the pre- and post-transplant models used to calculate Lung Allocation Scores (LAS), the current basis for prioritizing lung transplant candidates in the U.S. 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: Overall, accounting for survivor bias improved discrimination and calibration over the existing LAS, and led to changes in patient prioritization. Individuals who received lower (worse) priority under the modified LAS tended to experience increases in their estimated waitlist urgency, whereas those who received higher (better) priority under the modified LAS tended to experience increases in their estimated transplant benefit.

*Conclusions: Our approach to addressing survivor bias is intuitive and can be applied to any organ allocation system that prioritizes patients based on estimated post-transplant survival. This work is especially relevant to current debate about methods to ensure more equitable distribution of organs.

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