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An R-Based Landscape Validation of a Competing Risk Model

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Estimating Attributable Life Expectancy Under the Proportional Mean Residual Life Model.

Yixin Wang¹, Ying Qing Chen¹

¹Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, U.S.A.

Statistics in Biosciences

|November 22, 2024

Summary

This study introduces "attributable life expectancy" to quantify the impact of modifiable risk factors on population longevity. This new metric offers a valuable public health perspective beyond traditional risk calculations.

Keywords:

Excess life expectancy Population research Residual life regression Time-to-event

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Area of Science:

Epidemiology
Biostatistics
Public Health

Background:

Population attributable fraction (PAF) traditionally measures excess risk from modifiable factors.
Focusing on excess life expectancy offers a more direct measure for mortality-focused public health concerns.

Purpose of the Study:

To introduce and define "attributable life expectancy" as a novel metric.
To develop a model-based approach for estimating attributable life expectancy.
To assess the performance of the proposed method through simulations and real-world data.

Main Methods:

Development of a novel quantity: attributable life expectancy.
Application of the Oakes-Dasu proportional mean residual life model.
Asymptotic properties established for statistical inference.

Main Results:

A new method for calculating population-level life expectancy gains from risk factor modification was developed.
The method was validated using Monte Carlo simulations.
The approach was applied to analyze smoking cessation's impact on mortality in an Asian cohort.

Conclusions:

Attributable life expectancy provides a valuable metric for public health research, complementing traditional risk assessments.
The proposed model-based approach offers a robust method for estimating population-level life expectancy impacts.
This research has implications for understanding and addressing modifiable risk factors in population health.