Open bcjaeger opened 1 week ago
Here is a preprint that reviews >100 papers that use causal random forests. It covers common methods and how results are presented in the manuscripts. https://arxiv.org/abs/2404.13356 2404.13356v1.pdf
Here are two papers from cardiovascular disease.
Inoue K, Seeman TE, Horwich T, Budoff MJ, Watson KE. Heterogeneity in the Association Between the Presence of Coronary Artery Calcium and Cardiovascular Events: A Machine-Learning Approach in the MESA Study. Circulation. 2023 Jan 10;147(2):132-141. doi: 10.1161/CIRCULATIONAHA.122.062626. Epub 2022 Oct 31. PMID: 36314118; PMCID: PMC9812415.
Baum A, Scarpa J, Bruzelius E, Tamler R, Basu S, Faghmous J. Targeting weight loss interventions to reduce cardiovascular complications of type 2 diabetes: a machine learning-based post-hoc analysis of heterogeneous treatment effects in the Look AHEAD trial. Lancet Diabetes Endocrinol. 2017 Oct;5(10):808-815. doi: 10.1016/S2213-8587(17)30176-6. Epub 2017 Jul 12. PMID: 28711469; PMCID: PMC5815373.
🎩 tip to @aldengross for this paper: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8564142/
key findings:
(1) There’s an association between apoe4 and dementia, (2) Alden and colleagues tested for effect modification by sex and race.
This paper is from 2021, so its references should include some other relevant recent work on apoe4 heterogeneity.
The paper could benefit very much from a paragraph in the discussion or intro that summarizes prior studies. For this issue, one person or a small team could
manuscript/refs.bib
filemanuscript/manuscript.Rmd
file that covers prior evidence