Key result
In a methodological case study of non-elderly adults undergoing aortic valve repair, the estimated excess mortality HR was 2.70 (2.43 when accounting for modeled events) compared to the general population.
Why the study?
Existing excess hazard and cure models commonly require patient-level data, creating a need for methods to estimate excess mortality using aggregated data.
Hazard Ratio: 2.7
The golden section search algorithm within a microsimulation framework offers an efficient, accessible method to estimate time-varying and residual excess mortality hazard ratios using only aggregated data.
May inform long-term risk counseling after aortic valve repair; supports microsimulation for time-dependent excess mortality from aggregated data.
Excess mortality in a population with a disease can be defined as extra mortality observed compared to the matched-general population, and expressed as a hazard ratio (HR). While excess hazard models and cure models exist, these commonly need patient-level data. In this paper, we show a method for estimation of excess mortality with aggregated data. We detail the estimation of excess mortality in a microsimulation framework using the golden section search algorithm. This iterative algorithm identifies a local minimum within a predefined search interval. The overall excess mortality and residual excess (remaining mortality after accounting for modelled events) is estimated by calibrating microsimulation-derived survival curves to observed patient-sample survival curves. R code is provided. We apply the method to a study of aortic valve repair in non-elderly adults. The method enabled efficient estimation of excess mortality, expressed as overall and time-dependent hazard ratios through a piecewise function. The estimated excess mortality HR in the case-study was 2.70 without accounting from modelled events and 2.43 while accounting for modelled events, and the HR increased over time (1.23; 2.32; 5.50 at 0–60 months, 61–120 months and 121–188 months, respectively). This microsimulation framework provides an accessible, efficient method for estimating time-varying excess mortality hazard ratios, allowing for estimation of residual excess mortality, which excludes mortality due to modelled events, using only aggregated input data.
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Rhellab et al. (2026) studied Aortic valve disease. Aortic valve repair vs. Matched general population was evaluated on Excess mortality (HR 2.70). In a methodological case study of non-elderly adults undergoing aortic valve repair, the estimated excess mortality HR was 2.70 (2.43 when accounting for modeled events) compared to the general population.
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