Divide and conquer for accelerated failure time model with massive time‐to‐event data

Abstract

Big data present new theoretical and computational challenges as well as tremendous opportunities in many fields. In health care research, we develop a novel divide‐and‐conquer (DAC) approach to deal with massive and right‐censored data under the accelerated failure time model, where the sample size is extraordinarily large and the dimension of predictors is large but smaller than the sample size. Specifically, we construct a penalized loss function by approximating the weighted least squares loss function by combining estimation results without penalization from all subsets. The resulting adaptive LASSO penalized DAC estimator enjoys the oracle property. Simulation studies demonstrate that the proposed DAC procedure performs well and also reduces the computation time with satisfactory performance compared with estimation results using the full data. Our proposed DAC approach is applied to a massive dataset from the Chinese Longitudinal Healthy Longevity Survey.

Subsampling approach for least squares fitting of semi-parametric accelerated failure time models to massive survival data

“…The divide-and-conquer strategy divides massive data into groups, processes them separately, and aggregates the results. This strategy has been applied to Cox models and accelerated failure time (AFT) models (Su et al, 2022). Carefully devised, the strategy facilitates the full LASSO path through a batch screening approach in the case of the ultrahigh-dimensional Cox model with sparse solutions at all predefined regularization parameters in Li et al (2022).…”