# Efficient estimation of accelerated lifetime models under length-biased sampling

## TL;DR

This paper investigates efficient methods for estimating accelerated lifetime models in length-biased sampling scenarios, demonstrating that naive estimators can be fully efficient when covariate distribution is unspecified.

## Contribution

It shows that in length-biased sampling, naive estimators based on conditional likelihood are fully efficient if the covariate distribution is unspecified.

## Key findings

- Naive estimators are fully efficient under certain conditions.
- The invariance of the AFT model under length-biased sampling.
- Efficiency depends on covariate distribution assumptions.

## Abstract

In prevalent cohort studies where subjects are recruited at a cross-section, the time to an event may be subject to length-biased sampling, with the observed data being either the forward recurrence time, or the backward recurrence time, or their sum. In the regression setting, it has been shown that the accelerated failure time model for the underlying event time is invariant under these observed data set-ups and can be fitted using standard methodology for accelerated failure time model estimation, ignoring the length-bias. However, the efficiency of these estimators is unclear, owing to the fact that the observed covariate distribution, which is also length-biased, may contain information about the regression parameter in the accelerated life model. We demonstrate that if the true covariate distribution is completely unspecified, then the naive estimator based on the conditional likelihood given the covariates is fully efficient.