Bootstrap-Aggregated Method-of-Moments Estimation of the Copula Correlation Parameter for Marginal Survival Inference under Dependent Censoring

TL;DR

This paper introduces a bootstrap-aggregated generalized method-of-moments approach to estimate copula correlation parameters in dependent censored survival data, improving stability and accuracy over traditional likelihood methods.

Contribution

It proposes a novel stable estimation technique using bootstrap-aggregation and simulated annealing for copula parameters in dependent survival data.

Findings

The method provides accurate estimates with low mean absolute error.

Bootstrap confidence intervals achieve proper empirical coverage.

Application to clinical trial data demonstrates practical utility.

Abstract

In dependently censored survival data, the usual assumption of independent censoring or an incorrect specification of the correlation between the event and censoring times can bias marginal survival inference. Likelihood-based estimation of this dependence can be numerically unstable with large variance, and practical alternatives are limited. The proposed method uses generalized method-of-moments to estimate the copula correlation parameter of a Normal, Clayton, Gumbel, or Frank copula that links exponential, Weibull, or log-normal marginal survival times. Bootstrap-aggregation of simulated annealing is employed over candidate correlation ranges to obtain stable estimates. Simulations assess accuracy and uncertainty via mean absolute error, bootstrap confidence intervals, and empirical coverage. The method is applied to a double-blind randomized clinical trial with dependent censoring from early patient dropouts, where accurate marginal survival inference is needed to estimate the effect of a treatment on patient survival.