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Activity Number: 122 - Novel Statistical Methods in the Analysis of Big Data
Type: Topic Contributed
Date/Time: Monday, July 29, 2019 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Computing
Abstract #303084
Title: Online Updating of Survival Analysis
Author(s): Elizabeth Schifano* and Jing Wu and Ming-Hui Chen and Jun Yan
Companies: University of Connecticut and University of Rhode Island and University of Connecticut and University of Connecticut
Keywords: Cox model; Data compression; Data stream; Piecewise constant baseline hazard

When large amounts of survival data arrive in streams, conventional estimation methods may become computationally infeasible since they require storage of all the risk sets at each accumulation point. We develop online updating methods for carrying out survival analysis under the Cox proportional hazards models. Specifically, we propose online-updating estimators as well as their corresponding standard errors for both the regression coefficients and the baseline hazard function. An extensive simulation study is conducted to investigate the empirical performance of the proposed estimators. A large colon cancer data set from the Surveillance, Epidemiology, and End Results (SEER) program is analyzed to further demonstrate the proposed methodologies.

Authors who are presenting talks have a * after their name.

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