Speaker
Description
Relative survival techniques are often used to assess excess mortality in a specific study population by splitting observed mortality into background and excess components. These methods have been widely used to estimate cancer-specific mortality without the need for precise cause-of-death data for cancer patients. However, applying these techniques to other settings, such as pandemics or other temporal crises, presents important challenges. Standard relative survival methods assume excess hazards are always positive and that background mortality is specified externally, based solely on demographic factors and independent of the study data. In contrast, pandemics, however, may involve periods of negative excess hazards due to the protective effects of public health measures, as well as substantial heterogeneity in background mortality across subgroups. Moreover, unlike conventional applications where the study population is a small subset of the reference population, in pandemic and other temporal crisis settings the background and study populations represent the same group observed at different time points (e.g. pre-pandemic vs. pandemic periods).
To address these challenges, we propose a novel flexible parametric additive hazards model that simultaneously incorporates pre-pandemic and pandemic data, using penalized splines to estimate time-dependent covariate effects, with the crisis period included as a covariate. This unified framework for excess-mortality estimation accommodates negative excess hazards and integrates relevant risk factors directly into the background mortality hazard, defined as the pre-pandemic hazard and estimated from the data. In addition, the use of penalized splines is less prone to overfitting that the classical non-parametric Aalen’s method. We investigated two estimation strategies: one adapting the least-squares approach used in Aalen’s method to the penalized spline setting, and the other based on penalized likelihood maximization. The performance of the two approaches is compared through a simulation study based on the COVID-19 pandemic and scenarios mimicking possible pandemic conditions.
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