Probabilistic Prediction for Binary Treatment Choice: with focus on personalized medicine

TL;DR

This paper applies statistical decision theory to improve binary treatment choices in personalized medicine, focusing on as-if optimization of illness probabilities to enhance decision-making performance.

Contribution

It introduces a novel approach using maximum regret and as-if optimization for treatment decision rules based on estimated illness probabilities.

Findings

Demonstrates the effectiveness of decision-theoretic methods in treatment choice.

Highlights limitations of existing prediction approaches in clinical decision-making.

Proposes a coherent framework for personalized treatment decisions.

Abstract

This paper extends my research applying statistical decision theory to treatment choice with sample data, using maximum regret to evaluate the performance of treatment rules. The specific new contribution is to study as-if optimization using estimates of illness probabilities in clinical choice between surveillance and aggressive treatment. Beyond its specifics, the paper sends a broad message. Statisticians and computer scientists have addressed conditional prediction for decision making in indirect ways, the former applying classical statistical theory and the latter measuring prediction accuracy in test samples. Neither approach is satisfactory. Statistical decision theory provides a coherent, generally applicable methodology.