Causal Analysis of the TOPCAT Trial: Spironolactone for Preserved Cardiac Function Heart Failure

TL;DR

This paper applies causal discovery methods to the TOPCAT trial data, revealing regional discrepancies and subgroup-specific causal effects of spironolactone, providing a nuanced understanding of the trial's inconclusive overall results.

Contribution

It introduces the use of causal discovery with domain knowledge to analyze clinical trial data, uncovering regional differences and subgroup effects not evident in traditional analysis.

Findings

Regional discrepancies in diagnosis and treatment protocols.

Significant causal effects in specific subgroups.

Enhanced understanding of treatment efficacy through causal analysis.

Abstract

We describe the results of applying causal discovery methods on the data from a multi-site clinical trial, on the Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist (TOPCAT). The trial was inconclusive, with no clear benefits consistently shown for the whole cohort. However, there were questions regarding the reliability of the diagnosis and treatment protocol for a geographic subgroup of the cohort. With the inclusion of medical context in the form of domain knowledge, causal discovery is used to demonstrate regional discrepancies and to frame the regional transportability of the results. Furthermore, we show that, globally and especially for some subgroups, the treatment has significant causal effects, thus offering a more refined view of the trial results.