A Parametric Approach to Relaxing the Independence Assumption in Relative Survival Analysis

TL;DR

This paper introduces a copula-based parametric method to relax the independence assumption in relative survival analysis, improving disease-specific survival estimates when cause of death data is unreliable.

Contribution

It develops a likelihood-based approach incorporating dependence modeling via copulas, along with a sensitivity analysis framework for relative survival estimation.

Findings

Method performs well in simulations

Application to French breast cancer data demonstrates utility

Provides more flexible survival estimates under dependence assumptions

Abstract

With known cause of death (CoD), competing risk survival methods are applicable in estimating disease-specific survival. Relative survival analysis may be used to estimate disease-specific survival when cause of death is either unknown or subject to misspecification and not reliable for practical usage. This method is popular for population-based cancer survival studies using registry data and does not require CoD information. The standard estimator is the ratio of all-cause survival in the cancer cohort group to the known expected survival from a general reference population. Disease-specific death competes with other causes of mortality, potentially creating dependence among the CoD. The standard ratio estimate is only valid when death from disease and death from other causes are independent. To relax the independence assumption, we formulate dependence using a copula-based model. Likelihood-based parametric method is used to fit the distribution of disease-specific death without CoD information, where the copula is assumed known and the distribution of other cause of mortality is derived from the reference population. We propose a sensitivity analysis, where the analysis is conducted across a range of assumed dependence structures. We demonstrate the utility of our method through simulation studies and an application to French breast cancer data.