Moving From Individualized Risk-Based Prevention to Benefit-Based Prevention: Estimating Individualized Life-Years...

The current bedrock of precision prevention is selecting high-risk individuals for screening or other prevention services under the assumption that those at highest risk would have the highest benefit from prevention services. However, this may not hold when disease risk and competing mortality are highly correlated. In such cases, risk-based prevention may preferentially select older individuals with multiple comorbidities who would have substantially reduced life-years gainable from the service and increased risks of harm from any resulting surgical procedures. For such prevention services, we propose a benefit-based selection strategy in which individuals are selected according to their expected gain in life-years (i.e., difference in mean survival time with and without the prevention service). We estimate the expected gain in life-years for individuals in a target screening population by combining data from a randomized trial, which may not be population-representative, and data from a population-representative survey that has larger sample size, more covariates, and longer follow-up time to evaluate mortality than the trial. We derive the Taylor-linearized variances for the estimated expected gain in life-years that take into account the randomness due to both trial and survey sample. We show that benefit-based selection of ever-smokers for lung-cancer screening can identify individuals with more favorable benefit-harm trade-offs compared to risk-based selection. Using simulation studies, we examine the conditions in which one strategy may be preferable over the other.