An Inverse-Selection-Probability Weighted Approach for Real-World Progression-Free Survival With Left-Truncated Clinico-Genomic Data.
- 1Tempus AI, Inc, Chicago, Illinois, USA.
Abstract
Next-generation sequencing (NGS) has become a vital tool in precision medicine, revolutionizing cancer diagnosis and treatment. However, in real-world studies where NGS defines study entry, data are often left-truncated because patients who do not survive until sequencing are excluded. Consequently, standard survival analysis techniques can result in biased, overestimated survival probabilities. While the commonly used risk set adjustment method addresses this bias, it excludes patients experiencing progression prior to sequencing, reducing statistical efficiency and power. We introduce a novel weighted approach utilizing the inverse of sample selection probability to include all observed patients. We also developed a computationally efficient, user-friendly method for calculating weights and estimating variance. The performance of the proposed method is evaluated through comprehensive simulation studies and further demonstrated using real-world data of metastatic breast cancer patients. The proposed weighted approach, utilizing inverse sample selection probabilities, ensures accurate parameter estimates and reliable inference.
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BibTeX
@article{liu2026inverseselectionprobability,
title = {{An Inverse-Selection-Probability Weighted Approach for Real-World Progression-Free Survival With Left-Truncated Clinico-Genomic Data.}},
author = {Yan Liu and Seung Won Hyun and Chithra Sangli and Kyle Beauchamp and Farahnaz Islam and Dongho Shin},
journal = {Pharmaceutical statistics},
year = {2026},
month = oct,
doi = {10.1002/pst.70145},
url = {https://europepmc.org/article/MED/42950850},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - An Inverse-Selection-Probability Weighted Approach for Real-World Progression-Free Survival With Left-Truncated Clinico-Genomic Data.
AU - Yan Liu
AU - Seung Won Hyun
AU - Chithra Sangli
AU - Kyle Beauchamp
AU - Farahnaz Islam
AU - Dongho Shin
PY - 2026
DA - 2026/10/28
JO - Pharmaceutical statistics
DO - 10.1002/pst.70145
UR - https://europepmc.org/article/MED/42950850
AB - Next-generation sequencing (NGS) has become a vital tool in precision medicine, revolutionizing cancer diagnosis and treatment. However, in real-world studies where NGS defines study entry, data are often left-truncated because patients who do not survive until sequencing are excluded. Consequently, standard survival analysis techniques can result in biased, overestimated survival probabilities. While the commonly used risk set adjustment method addresses this bias, it excludes patients experiencing progression prior to sequencing, reducing statistical efficiency and power. We introduce a novel weighted approach utilizing the inverse of sample selection probability to include all observed patients. We also developed a computationally efficient, user-friendly method for calculating weights and estimating variance. The performance of the proposed method is evaluated through comprehensive simulation studies and further demonstrated using real-world data of metastatic breast cancer patients. The proposed weighted approach, utilizing inverse sample selection probabilities, ensures accurate parameter estimates and reliable inference.
SN - 1539-1604
SN - 1539-1612
ER -