Revised Deep Learning Semiparametric Regression for Testing and Estimating Treatment Effect Heterogeneity in Observational Studies: A Lung Transplant Case Study.

Shuai Yuan, Fei Zou, Baiming Zou

Abstract readPreprint

In one paragraph

Article in bioRxiv : the preprint server for biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Shuai YuanDepartment of Biostatistics, University of North Carolina at Chapel Hill.ORCID 0009-0009-3528-7033

Fei ZouDepartment of Biostatistics, University of North Carolina at Chapel Hill.

Baiming ZouDepartment of Biostatistics, University of North Carolina at Chapel Hill.

Funding

Robust Computational and Data Analytic Tools for In-depth Understanding Postoperative Pain Mechanism with Enhanced Pain Management and Clinical Decision MakingR01LM014407 · NLM · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Baiming Zou · 2024 to 2026

$1.4M

Enhanced Machine Learning Tools for Complex Data Evaluation and Integration in Advancing Health OutcomesR01HL173044 · NHLBI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Baiming Zou, Fei Zou · 2025 to 2026

$1.3M

NHLBI NIH HHS R01 HL173044NLM NIH HHS R01 LM014407

6 · The paper itself

Abstract

Lung transplantation programs must decide when bilateral lung transplantation (BLT) offers meaningful functional benefit over single lung transplantation (SLT). Because donor and recipient characteristics jointly shape outcomes, the BLT-SLT contrast may differ across patients. However, analyzing observational registries poses a statistical challenge: apparent subgroup differences can be artifacts of complex confounding, while true heterogeneity can be missed or poorly quantified. Using a large national registry, we investigate whether the BLT effect varies across recipients and identify clinically relevant profiles of benefit using post-transplant lung function measured by forced expiratory volume in 1 second (FEV1). We develop

Indexed as

Causal inferenceHeterogeneous treatment effectsNeural networksOrgan allocationPrecision medicineScore test

Identifiers

PMID42039548

DOI10.64898/2026.04.13.718254

PMCPMC13105086

What OpenQuestion holds

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LicenceCC BY

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.