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Activity Number: 307 - Advanced Survival Analysis Tools for Statistical Learning from Complex Scientific Studies
Type: Topic Contributed
Date/Time: Wednesday, August 5, 2020 : 10:00 AM to 11:50 AM
Sponsor: Lifetime Data Science Section
Abstract #311114
Title: Censored Linear Regression in the Presence or Absence of Auxiliary Survival Information
Author(s): Chiung-Yu Huang* and Ying Sheng and Yifei Sun and Detian Deng
Companies: University of California at San Francisco and University of California at San Francisco and Mailman School of Public Health, Columbia University and Google
Keywords: Accelerated failure time model; Auxiliary information; Generalized method of moments; Semiparametric estimation; Weighted log-rank estimating equation

There has been a rising interest in better exploiting auxiliary summary information from large databases in the analysis of smaller-scale studies which collect more comprehensive patient-level information. The purpose of this paper is twofold: firstly, we propose a novel approach to synthesize information from both the aggregate summary statistics and the individual-level data in censored linear regression. We show that the auxiliary information amounts to a system of non-smooth estimating equations and thus can be combined with the conventional weighted log-rank estimating equations by using the generalized method of moments (GMM) approach. The proposed methodology can be further extended to account for the potential inconsistency in information from different sources. Secondly, in the absence of auxiliary information, we propose to improve estimation efficiency by combining the overidentified weighted log-rank estimating equations with different weight functions via the GMM framework. To deal with the non-smooth GMM-type objective functions, we develop an asymptotics-guided algorithm for parameter and variance estimation. We establish the asymptotic normality of the proposed GMM-

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