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. 2019 Apr;29(2):877-894.
doi: 10.5705/ss.202016.0449.

INFERENCE FOR LOW-DIMENSIONAL COVARIATES IN A HIGH-DIMENSIONAL ACCELERATED FAILURE TIME MODEL

Hao Chai  1 , Qingzhao Zhang  2 , Jian Huang  3 , Shuangge Ma  1   2
Affiliations

INFERENCE FOR LOW-DIMENSIONAL COVARIATES IN A HIGH-DIMENSIONAL ACCELERATED FAILURE TIME MODEL

Hao Chai et al. Stat Sin. 2019 Apr.

Abstract

Data with high-dimensional covariates are now commonly encountered. Compared to other types of responses, research on high-dimensional data with censored survival responses is still relatively limited, and most of the existing studies have been focused on estimation and variable selection. In this study, we consider data with a censored survival response, a set of low-dimensional covariates of main interest, and a set of high-dimensional covariates that may also affect survival. The accelerated failure time model is adopted to describe survival. The goal is to conduct inference for the effects of low-dimensional covariates, while properly accounting for the high-dimensional covariates. A penalization-based procedure is developed, and its validity is established under mild and widely adopted conditions. Simulation suggests satisfactory performance of the proposed procedure, and the analysis of two cancer genetic datasets demonstrates its practical applicability.

Keywords: AFT model; censored survival data; high-dimensional inference.

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References

    1. Bae J and Kim S (2003). The uniform central limit theorem for the Kaplan-Meier integral process. Bulletin of the Australian Mathematical Society 67, 467–480.
    1. Belloni A, Chernozhukov V and Hansen C (2014). Inference on treatment effects after selection among high-dimensional controls. The Review of Economic Studies 81, 608–650.
    1. Berk R, Brown L, Buja A, Zhang K and Zhao L (2013). Valid post-selection inference. The Annals of Statistics 41, 802–837.
    1. Buckley J and James I (1979). Linear regression with censored data. Biometrika 66, 429–436.
    1. Bühlmann P (2013). Statistical significance in high-dimensional linear models. Bernoulli 19, 1212–1242.

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