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

arXiv: https://arxiv.org/abs/2211.12983 · HTML: https://mlsanity.com/paper/2211.12983/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 28 references that are in the map.
- First posted on arXiv 23 Nov 2022.

## Builds on (most cited first, 5 of 5)

- [Detecting causal associations in large nonlinear time series datasets](https://mlsanity.com/paper/1702.07007/) (2017)
- [Extending inferences from a randomized trial to a new target population](https://mlsanity.com/paper/1805.00550/) (2018)
- [Combining Experts' Causal Judgments](https://mlsanity.com/paper/2005.10180/) (2020)
- [Directed Acyclic Graphs and causal thinking in clinical risk prediction modeling](https://mlsanity.com/paper/2002.09414/) (2020)
- [Automated causal inference in application to randomized controlled clinical trials](https://mlsanity.com/paper/2201.05773/) (2022)

## Abstract (arXiv)

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.