Detecting critical treatment effect bias in small subgroups

TL;DR

This paper introduces a statistical method to benchmark observational treatment effect estimates against randomized trials, focusing on small subgroups to identify potential biases in observational data.

Contribution

It presents a novel statistical test and bias estimation approach for subgroup analysis, enhancing the reliability of observational studies in clinical decision-making.

Findings

The method successfully detects treatment effect biases in real-world data.

It provides valid lower bounds on maximum bias for subgroups.

Validation aligns with established medical knowledge.

Abstract

Randomized trials are considered the gold standard for making informed decisions in medicine, yet they often lack generalizability to the patient populations in clinical practice. Observational studies, on the other hand, cover a broader patient population but are prone to various biases. Thus, before using an observational study for decision-making, it is crucial to benchmark its treatment effect estimates against those derived from a randomized trial. We propose a novel strategy to benchmark observational studies beyond the average treatment effect. First, we design a statistical test for the null hypothesis that the treatment effects estimated from the two studies, conditioned on a set of relevant features, differ up to some tolerance. We then estimate an asymptotically valid lower bound on the maximum bias strength for any subgroup in the observational study. Finally, we validate our benchmarking strategy in a real-world setting and show that it leads to conclusions that align with established medical knowledge.