Working With Convex Responses: Antifragility From Finance to Oncology

TL;DR

This paper introduces a framework applying risk analysis and the concept of antifragility, derived from finance, to optimize dosing and interventions in oncology by analyzing nonlinear dose-response relationships.

Contribution

It extends stochastic analysis techniques from finance to medicine, defining antifragility mathematically and linking nonlinear responses to clinical risk management.

Findings

Defined antifragility as a property of convex responses.

Linked dose-response convexity to statistical properties.

Proposed relations between dosage, condition severity, and iatrogenics.

Abstract

We extend techniques and learnings about the stochastic properties of nonlinear responses from finance to medicine, particularly oncology where it can inform dosing and intervention. We define antifragility. We propose uses of risk analysis to medical problems, through the properties of nonlinear responses (convex or concave). We 1) link the convexity/concavity of the dose-response function to the statistical properties of the results; 2) define "antifragility" as a mathematical property for local beneficial convex responses and the generalization of "fragility" as its opposite, locally concave in the tails of the statistical distribution; 3) propose mathematically tractable relations between dosage, severity of conditions, and iatrogenics. In short we propose a framework to integrate the necessary consequences of nonlinearities in evidence-based oncology and more general clinical risk management.