Application of causal forests to randomised controlled trial data to identify heterogeneous treatment effects: a case study
Abstract Background Classical approaches to subgroup analysis in randomised controlled trials (RCTs) to identify heterogeneous treatment effects (HTEs) involve testing the interaction between each pre-specified possible treatment effect modifier and the treatment effect. However, individual signific...
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2025-02-01
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| author | Eleanor Van Vogt Anthony C. Gordon Karla Diaz-Ordaz Suzie Cro |
| author_facet | Eleanor Van Vogt Anthony C. Gordon Karla Diaz-Ordaz Suzie Cro |
| author_sort | Eleanor Van Vogt |
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| description | Abstract Background Classical approaches to subgroup analysis in randomised controlled trials (RCTs) to identify heterogeneous treatment effects (HTEs) involve testing the interaction between each pre-specified possible treatment effect modifier and the treatment effect. However, individual significant interactions may not always yield clinically actionable subgroups, particularly for continuous covariates. Non-parametric causal machine learning approaches are flexible alternatives for estimating HTEs across many possible treatment effect modifiers in a single analysis. Methods We conducted a secondary analysis of the VANISH RCT, which compared the early use of vasopressin with norepinephrine on renal failure-free survival for patients with septic shock at 28 days. We used classical (separate tests for interaction with Bonferroni correction), data-adaptive (hierarchical lasso regression), and non-parametric causal machine learning (causal forest) methods to analyse HTEs for the primary outcome of being alive at 28 days. Causal forests comprise honest causal trees, which use sample splitting to determine tree splits and estimate treatment effects separately. The modal initial (root) splits of the causal forest were extracted, and the mean value was used as a threshold to partition the population into subgroups with different treatment effects. Results All three models found evidence of HTE with serum potassium levels. Univariable logistic regression OR 0.435 (95%CI [0.270, 0.683]. p = 0.0004), hierarchical lasso logistic regression standardised OR: 0.604 (95% CI 0.259, 0.701), lambda = 0.0049. Hierarchical lasso kept the interaction between the treatment and serum potassium, sodium level, minimum temperature, platelet count and presence of ischemic heart disease. The causal forest approach found some evidence of HTE (p = 0.124). When extracting root splits, the modal split was on serum potassium (mean applied threshold of 4.68 mmol/L). When dividing the patient population into subgroups based on the mean initial root threshold, risk differences in being alive at 28 days were 0.069 (95%CI [-0.032, 0.169]) and − 0.257 (95%CI [-0.368, -0.146]) with serum potassium ≤ 4.68 and > 4.68 respectively. Conclusions The causal forest agreed with the data-adaptive and classical method of subgroup analysis in identifying HTE by serum potassium. Whilst classical and data-adaptive methods may identify sources of HTE, they do not immediately suggest subgroup splits which are clinically actionable. The extraction of root splits in causal forests is a novel approach to obtaining data-derived subgroups, to be further investigated. |
| format | Article |
| id | doaj-art-dd229ec33eee4cf99e94bf1d6588e959 |
| institution | DOAJ |
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| spelling | doaj-art-dd229ec33eee4cf99e94bf1d6588e9592025-08-20T03:10:50ZengBMCBMC Medical Research Methodology1471-22882025-02-0125111310.1186/s12874-025-02489-2Application of causal forests to randomised controlled trial data to identify heterogeneous treatment effects: a case studyEleanor Van Vogt0Anthony C. Gordon1Karla Diaz-Ordaz2Suzie Cro3Imperial College LondonImperial College LondonUniversity College LondonImperial College LondonAbstract Background Classical approaches to subgroup analysis in randomised controlled trials (RCTs) to identify heterogeneous treatment effects (HTEs) involve testing the interaction between each pre-specified possible treatment effect modifier and the treatment effect. However, individual significant interactions may not always yield clinically actionable subgroups, particularly for continuous covariates. Non-parametric causal machine learning approaches are flexible alternatives for estimating HTEs across many possible treatment effect modifiers in a single analysis. Methods We conducted a secondary analysis of the VANISH RCT, which compared the early use of vasopressin with norepinephrine on renal failure-free survival for patients with septic shock at 28 days. We used classical (separate tests for interaction with Bonferroni correction), data-adaptive (hierarchical lasso regression), and non-parametric causal machine learning (causal forest) methods to analyse HTEs for the primary outcome of being alive at 28 days. Causal forests comprise honest causal trees, which use sample splitting to determine tree splits and estimate treatment effects separately. The modal initial (root) splits of the causal forest were extracted, and the mean value was used as a threshold to partition the population into subgroups with different treatment effects. Results All three models found evidence of HTE with serum potassium levels. Univariable logistic regression OR 0.435 (95%CI [0.270, 0.683]. p = 0.0004), hierarchical lasso logistic regression standardised OR: 0.604 (95% CI 0.259, 0.701), lambda = 0.0049. Hierarchical lasso kept the interaction between the treatment and serum potassium, sodium level, minimum temperature, platelet count and presence of ischemic heart disease. The causal forest approach found some evidence of HTE (p = 0.124). When extracting root splits, the modal split was on serum potassium (mean applied threshold of 4.68 mmol/L). When dividing the patient population into subgroups based on the mean initial root threshold, risk differences in being alive at 28 days were 0.069 (95%CI [-0.032, 0.169]) and − 0.257 (95%CI [-0.368, -0.146]) with serum potassium ≤ 4.68 and > 4.68 respectively. Conclusions The causal forest agreed with the data-adaptive and classical method of subgroup analysis in identifying HTE by serum potassium. Whilst classical and data-adaptive methods may identify sources of HTE, they do not immediately suggest subgroup splits which are clinically actionable. The extraction of root splits in causal forests is a novel approach to obtaining data-derived subgroups, to be further investigated.https://doi.org/10.1186/s12874-025-02489-2Causal machine learningSubgroup analysisCritical careTreatment effect heterogeneityCausal inferenceRandomised controlled trial |
| spellingShingle | Eleanor Van Vogt Anthony C. Gordon Karla Diaz-Ordaz Suzie Cro Application of causal forests to randomised controlled trial data to identify heterogeneous treatment effects: a case study BMC Medical Research Methodology Causal machine learning Subgroup analysis Critical care Treatment effect heterogeneity Causal inference Randomised controlled trial |
| title | Application of causal forests to randomised controlled trial data to identify heterogeneous treatment effects: a case study |
| title_full | Application of causal forests to randomised controlled trial data to identify heterogeneous treatment effects: a case study |
| title_fullStr | Application of causal forests to randomised controlled trial data to identify heterogeneous treatment effects: a case study |
| title_full_unstemmed | Application of causal forests to randomised controlled trial data to identify heterogeneous treatment effects: a case study |
| title_short | Application of causal forests to randomised controlled trial data to identify heterogeneous treatment effects: a case study |
| title_sort | application of causal forests to randomised controlled trial data to identify heterogeneous treatment effects a case study |
| topic | Causal machine learning Subgroup analysis Critical care Treatment effect heterogeneity Causal inference Randomised controlled trial |
| url | https://doi.org/10.1186/s12874-025-02489-2 |
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