An Inverse-Selection-Probability Weighted Approach for Real-World Progression-Free Survival With Left-Truncated Clinico-Genomic Data

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.