Nonparametric Bounds for Evaluating the Clinical Utility of Treatment Rules

TL;DR

This paper develops nonparametric bounds to evaluate the expected outcomes of new clinical treatment rules in observational studies with unobserved confounders, providing robust tools for assessing their potential impact.

Contribution

It introduces two novel strategies for deriving bounds on treatment outcomes that account for hidden confounding, extending to incorporate instrumental variables and outcome differences.

Findings

Bounds are effective in simulation scenarios.

Incorporating IVs narrows the bounds.

Application to peanut allergy prevention illustrates practical utility.

Abstract

Evaluating the value of new clinical treatment rules based on patient characteristics is important but often complicated by hidden confounding factors in observational studies. Standard methods for estimating the average patient outcome if a new rule were universally adopted typically rely on strong, untestable assumptions about these hidden factors. This paper tackles this challenge by developing nonparametric bounds - a range of plausible values - for the expected outcome under a new rule, even with unobserved confounders present. We propose and investigate two main strategies for derivation of these bounds. We extend these techniques to incorporate Instrumental Variables (IVs), which can help narrow the bounds, and to directly estimate bounds on the difference in expected outcomes between the new rule and an existing clinical guideline. In simulation studies we compare the performance and width of bounds generated by the reduction and conditioning strategies in different scenarios. The methods are illustrated with a real-data example about prevention of peanut allergy in children. Our bounding frameworks provide robust tools for assessing the potential impact of new clinical treatment rules when unmeasured confounding is a concern.