本文提出一個可擴展的貝氏架構，用於處理具有左截斷與右設限的超高維存活分析中的變數選擇問題。基於部分秩相關估計量，我們提出一種準成對秩概似方法，並結合 sigmoid 平滑與邊界修正，以提升數值穩定性與計算效率。透過 Pólya-Gamma 資料擴增，所提出的準概似可以重新表達為一個擴增的邏輯斯迴歸模型，進而促進在全域－局部收縮先驗下的有效貝氏推論，用於稀疏變數選擇。此外，我們建立了重要的理論保證，包括後驗一致性以及高維設定下的變數選擇一致性。大量的模擬研究與真實資料應用結果顯示，與現有方法相比，所提出的方法在估計準確度與變數選擇表現上具有競爭力。;In this article， we develop a scalable Bayesian framework for variable selection in ultrahigh-dimensional survival analysis with left truncation and right censoring. Building upon the partial-rank correlation estimator， we propose a quasi-pairwise rank likelihood with sigmoid smoothing and boundary correction to improve numerical stability and computational efficiency. Through Pólya--Gamma augmentation， the proposed quasi-likelihood can be reformulated as an augmented logistic regression model， which facilitates efficient Bayesian inference under global--local shrinkage priors for sparse variable selection. We further establish important theoretical guarantees， including posterior consistency and variable selection consistency in high-dimensional settings. Extensive simulation studies and real-data applications demonstrate that the proposed method achieves competitive estimation accuracy and variable selection performance compared with existing approaches.
