Induced smoothing for the semiparametric accelerated failure time model: asymptotics and extensions to clustered data

• 成果类型: Article
• 署名作者: Johnson, Lynn M.; Strawderman, Robert L.
• 署名单位: Cornell University; Cornell University
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asp025
• 发表日期: 2009
• 页码: 577590
• 关键词: linear-regression analysis; rank regression

摘要

This paper extends the induced smoothing procedure of Brown & Wang (2006) for the semiparametric accelerated failure time model to the case of clustered failure time data. The resulting procedure permits fast and accurate computation of regression parameter estimates and standard errors using simple and widely available numerical methods, such as the Newton-Raphson algorithm. The regression parameter estimates are shown to be strongly consistent and asymptotically normal; in addition, we prove that the asymptotic distribution of the smoothed estimator coincides with that obtained without the use of smoothing. This establishes a key claim of Brown & Wang (2006) for the case of independent failure time data and also extends such results to the case of clustered data. Simulation results show that these smoothed estimates perform as well as those obtained using the best available methods at a fraction of the computational cost.