# Communication-Efficient Distributed Estimation and Inference for Cox's Model

arXiv: https://arxiv.org/abs/2302.12111 · HTML: https://mlsanity.com/paper/2302.12111/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 48 references that are in the map.
- First posted on arXiv 23 Feb 2023.

## Builds on (most cited first, 14 of 14)

- [Communication Efficient Distributed Optimization using an Approximate Newton-type Method](https://mlsanity.com/paper/1312.7853/) (2013)
- [Confidence Intervals and Hypothesis Testing for High-Dimensional Regression](https://mlsanity.com/paper/1306.3171/) (2013)
- [Efficient Distributed Learning with Sparsity](https://mlsanity.com/paper/1605.07991/) (2016)
- [Communication-Efficient Accurate Statistical Estimation](https://mlsanity.com/paper/1906.04870/) (2019)
- [A General Theory of Hypothesis Tests and Confidence Regions for Sparse High Dimensional Models](https://mlsanity.com/paper/1412.8765/) (2014)
- [Distributed High-dimensional Regression Under a Quantile Loss Function](https://mlsanity.com/paper/1906.05741/) (2019)
- [Communication-efficient Algorithms for Distributed Stochastic Principal Component Analysis](https://mlsanity.com/paper/1702.08169/) (2017)
- [Linear Hypothesis Testing in Dense High-Dimensional Linear Models](https://mlsanity.com/paper/1610.02987/) (2016)
- [Testing and Confidence Intervals for High Dimensional Proportional Hazards Model](https://mlsanity.com/paper/1412.5158/) (2014)
- [A Flexible Framework for Hypothesis Testing in High-dimensions](https://mlsanity.com/paper/1704.07971/) (2017)
- [Distributed Feature Screening via Componentwise Debiasing](https://mlsanity.com/paper/1903.03810/) (2019)
- [Confidence intervals for high-dimensional Cox models](https://mlsanity.com/paper/1803.01150/) (2018)
- [Statistical Inference for Cox Proportional Hazards Models with a Diverging Number of Covariates](https://mlsanity.com/paper/2106.03244/) (2021)
- [Factor-Augmented Regularized Model for Hazard Regression](https://mlsanity.com/paper/2210.01067/) (2022)

## Abstract (arXiv)

Motivated by multi-center biomedical studies that cannot share individual data due to privacy and ownership concerns, we develop communication-efficient iterative distributed algorithms for estimation and inference in the high-dimensional sparse Cox proportional hazards model. We demonstrate that our estimator, even with a relatively small number of iterations, achieves the same convergence rate as the ideal full-sample estimator under very mild conditions. To construct confidence intervals for linear combinations of high-dimensional hazard regression coefficients, we introduce a novel debiased method, establish central limit theorems, and provide consistent variance estimators that yield asymptotically valid distributed confidence intervals. In addition, we provide valid and powerful distributed hypothesis tests for any coordinate element based on a decorrelated score test. We allow time-dependent covariates as well as censored survival times. Extensive numerical experiments on both simulated and real data lend further support to our theory and demonstrate that our communication-efficient distributed estimators, confidence intervals, and hypothesis tests improve upon alternative methods.