Using public clinical trial reports to evaluate observational study methods

TL;DR

This paper introduces TrialVerify, a new framework that uses clinical trial reports as ground truth to evaluate and compare the accuracy of observational study methods in estimating causal effects.

Contribution

The paper presents TrialVerify, a novel approach leveraging clinical trial data to assess observational study methods' accuracy in causal inference.

Findings

Inverse propensity score weighting performs best among evaluated methods.

TrialVerify effectively estimates the precision and recall of observational methods.

The framework is publicly available for further evaluation and research.

Abstract

Observational studies are valuable for estimating the effects of various medical interventions, but are notoriously difficult to evaluate because the methods used in observational studies require many untestable assumptions. This lack of verifiability makes it difficult both to compare different observational study methods and to trust the results of any particular observational study. In this work, we propose TrialVerify, a new approach for evaluating observational study methods based on ground truth sourced from clinical trial reports. We process trial reports into a denoised collection of known causal relationships that can then be used to estimate the precision and recall of various observational study methods. We then use TrialVerify to evaluate multiple observational study methods in terms of their ability to identify the known causal relationships from a large national insurance claims dataset. We found that inverse propensity score weighting is an effective approach for accurately reproducing known causal relationships and outperforms other observational study methods. TrialVerify is made freely available for others to evaluate observational study methods.