# Collaborative Inference for Accelerated Failure Time Model Using Clinical Center‐Level Summary Statistics

## TL;DR

This paper introduces a collaborative method for analyzing survival data from multiple clinical centers using the AFT model, improving efficiency and robustness.

## Contribution

A novel distributed inference method for parametric AFT models using center-level summary statistics is proposed.

## Key findings

- The method allows for flexible integrative analysis of survival data without requiring proportional hazards assumptions.
- Simulation experiments and a kidney transplantation case study demonstrate the method's effectiveness.
- A distributed likelihood ratio test is developed to assess model fit under generalized gamma distributions.

## Abstract

Multi‐center clinical research offers numerous advantages, including the ability to obtain a larger combined sample size and to reduce center‐specific insufficiency and imbalance in event rates, ultimately leading to more robust and generalizable findings. This paper develops a collaborative analytic framework for survival data analysis using summary statistics and the Accelerated Failure Time (AFT) model, a popular alternative to the Cox proportional hazards model for the analysis of time‐to‐event data. The AFT model directly accounts for the effects of the covariates on times to event, rather than through hazard functions, with no proportionality assumption required compared to the Cox model. Given that it bypasses the construction of partial likelihood, it gives rise to more flexibility in integrative analyses of survival data collected from multiple clinical sites. Our proposed distributed inference method focuses on a class of parametric AFT models with Weibull, log‐normal, and log‐logistic distributions for time‐to‐event outcomes, with a distributed likelihood ratio test established under the generalized gamma distribution to assess the goodness‐of‐fit across different candidate parametric AFT models. We present large‐sample properties for the proposed distributed method and illustrate its performance through simulation experiments and a real‐world data example on kidney transplantation.