# Accounting for Survivor Bias in Lung Allocation

> **NIH NIH F31** · UNIVERSITY OF PENNSYLVANIA · 2021 · $26,536

## Abstract

Project Summary
In May 2005, the Organ Procurement and Transplantation Network (OPTN) modified their lung transplantation
policy from one in which potential recipients were prioritized based on the amount of time spent on the waiting
list to one in which donor lungs were allocated to recipients based on a lung allocation score (LAS). Since the
adoption of the LAS, studies have shown that the mortality rate for patients on the lung transplant waiting list
has decreased, but resource use and geographic and gender disparities in lung allocation have increased.
Additionally, while the statistical models used to construct the LAS control for patients’ demographics,
diagnoses, and laboratory values, they do not account for the fact that in order to receive a lung transplant, an
individual must survive on the waiting list long enough for a suitable lung to become available. Since
individuals who survive one year or more on the waiting list might be inherently different from individuals who
die, receive a transplant, or are censored prior to one year, failure to incorporate this information can lead to
inaccurate LAS predictions. Epidemiologists refer to such bias as “survivor bias.” The goal of this research and
training plan is to improve the predictive accuracy of the LAS by accounting for survivor bias so that lungs are
allocated to the appropriate patients in the appropriate order. This goal will be accomplished via three aims. In
Aim 1, we will use advanced causal inference methods, such as inverse probability weighting, to quantify the
bias imposed by the fact that individuals who survive one year or more on the waiting list might be inherently
different from individuals who die, receive transplant, or are censored prior to one year. Such methods rely on
the “potential outcomes” framework to “map” the survival probabilities obtained among the post-transplant
group back to the full waiting list population. In Aim 2, we will compare the predictive accuracy of the model
developed in Aim 1 to the original LAS. Specifically, we will look at model calibration, discrimination, and
computational efficiency. Finally, in Aim 3, we will use qualitative research methods to begin to 1) understand
how physicians use the current LAS to make treatment decisions in lung disease care, 2) determine whether
the modifications to the LAS from Aim 1 alters their decision-making process, and 3) provide the applicant with
the training necessary to conduct future qualitative research. This innovative approach will allow the transplant
community to better understand how the current LAS is used in clinical practice and how survivor bias
influences physicians’ estimates of the benefit of transplant. Findings from this study can also be applied in
other areas of medicine which rely upon prediction models, such as cardiology and intensive care. The
accompanying training plan consists of both didactic and experiential learning opportunities, and will enable the
applicant to deve...

## Key facts

- **NIH application ID:** 10232273
- **Project number:** 5F31HL149338-03
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** Erin Schnellinger
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $26,536
- **Award type:** 5
- **Project period:** 2019-09-01 → 2022-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10232273

## Citation

> US National Institutes of Health, RePORTER application 10232273, Accounting for Survivor Bias in Lung Allocation (5F31HL149338-03). Retrieved via AI Analytics 2026-06-24 from https://api.ai-analytics.org/grant/nih/10232273. Licensed CC0.

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