ArticleAnnals of epidemiology2022
Generalizability of heterogeneous treatment effects based on causal forests applied to two randomized clinical trials of intensive glycemic control.
Article in Annals of epidemiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed, 21 citations in OpenAlex.
- Causal Forests in Practice: Lessons on Detecting Heterogeneous Treatment Effects in a Randomized Controlled Trial of a Healthy Food Subsidy Program in Canada.The Journal of nutrition · 2026Trial
- A novel digital twin strategy to examine the implications of randomized clinical trials for real-world populations.NPJ digital medicine · 2026Article
- Investigation of risk factors for osteoporosis with a focus on hypertension and estimation of the causal effect of hypertension on osteoporosis using causal forest.Hypertension research : official journal of the Japanese Society of Hypertension · 2025Article
- Precision subclassification of type 2 diabetes: a systematic review.Communications medicine · 2023Article
- Comparison of causal forest and regression-based approaches to evaluate treatment effect heterogeneity: an application for type 2 diabetes precision medicine.BMC medical informatics and decision making · 2023Article
- Heterogeneous treatment effects of Cerebrolysin as an early add-on to reperfusion therapy:Frontiers in pharmacology · 2023Article
- A permutation procedure to detect heterogeneous treatment effects in randomized clinical trials while controlling the type I error rate.Clinical trials (London, England) · 2022Article
- Heterogeneous treatment effects in social policy studies: An assessment of contemporary articles in the health and social sciences.Annals of epidemiology · 2022Article
- Heterogeneous treatment effects of intensive glycemic control on major adverse cardiovascular events in the ACCORD and VADT trials: a machine-learning analysis.Cardiovascular diabetology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 6 institutions in 1 country.
Funding
Abstract
Purpose Machine learning is an attractive tool for identifying heterogeneous treatment effects (HTE) of interventions but generalizability of machine learning derived HTE remains unclear. We examined generalizability of HTE detected using causal forests in two similarly designed randomized trials in type II diabetes patients. Methods We evaluated published HTE of intensive versus standard glycemic control on all-cause mortality from the Action to Control Cardiovascular Risk in Diabetes study (ACCORD) in a second trial, the Veterans Affairs Diabetes Trial (VADT). We then applied causal forests to VADT, ACCORD, and pooled data from both studies and compared variable importance and subgroup effects across samples. Results HTE in ACCORD did not replicate in similar subgroups in VADT, but variable importance was correlated between VADT and ACCORD (Kendall's tau-b 0.75). Applying causal forests to pooled individual-level data yielded seven subgroups with similar HTE across both studies, ranging from risk difference of all-cause mortality of -3.9% (95% CI -7.0, -0.8) to 4.7% (95% CI 1.8, 7.5). Conclusions Machine learning detection of HTE subgroups from randomized trials may not generalize across study samples even when variable importance is correlated. Pooling individual-level data may overcome differences in study populations and/or differences in interventions that limit HTE generalizability.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.