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 the fact that individuals who receive a transplant must survive on the waitlist long enough to receive the offered donor organ. Individuals who meet these criteria can differ from those who do not, resulting in survivor bias and inaccurate predictions. 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. Overall, the modified LAS exhibited better discrimination and calibration than the existing LAS, and led to changes in patient prioritization. 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.