Using tree-based machine learning for health studies: literature review and case series
Tree-based machine learning methods have gained traction in the statistical and data
science fields. They have been shown to provide better solutions to various research …
science fields. They have been shown to provide better solutions to various research …
Artificial intelligence for dementia prevention
INTRODUCTION A wide range of modifiable risk factors for dementia have been identified.
Considerable debate remains about these risk factors, possible interactions between them …
Considerable debate remains about these risk factors, possible interactions between them …
Estimating heterogeneous survival treatment effect in observational data using machine learning
Methods for estimating heterogeneous treatment effect in observational data have largely
focused on continuous or binary outcomes, and have been relatively less vetted with …
focused on continuous or binary outcomes, and have been relatively less vetted with …
Variable selection with missing data in both covariates and outcomes: Imputation and machine learning
Variable selection in the presence of both missing covariates and outcomes is an important
statistical research topic. Parametric regression are susceptible to misspecification, and as a …
statistical research topic. Parametric regression are susceptible to misspecification, and as a …
Estimating heterogeneous survival treatment effects of lung cancer screening approaches: A causal machine learning analysis
ABSTRACT The National Lung Screening Trial (NLST) found that low-dose computed
tomography (LDCT) screening provided lung cancer (LC) mortality benefit compared to …
tomography (LDCT) screening provided lung cancer (LC) mortality benefit compared to …
A flexible approach for causal inference with multiple treatments and clustered survival outcomes
When drawing causal inferences about the effects of multiple treatments on clustered
survival outcomes using observational data, we need to address implications of the …
survival outcomes using observational data, we need to address implications of the …
[HTML][HTML] A flexible sensitivity analysis approach for unmeasured confounding with multiple treatments and a binary outcome with application to SEER-Medicare lung …
In the absence of a randomized experiment, a key assumption for drawing causal inference
about treatment effects is the ignorable treatment assignment. Violations of the ignorability …
about treatment effects is the ignorable treatment assignment. Violations of the ignorability …
A new method for clustered survival data: Estimation of treatment effect heterogeneity and variable selection
L Hu - Biometrical Journal, 2024 - Wiley Online Library
We recently developed a new method random‐intercept accelerated failure time model with
Bayesian additive regression trees (riAFT‐BART) to draw causal inferences about …
Bayesian additive regression trees (riAFT‐BART) to draw causal inferences about …
A flexible approach for assessing heterogeneity of causal treatment effects on patient survival using large datasets with clustered observations
Personalized medicine requires an understanding of treatment effect heterogeneity.
Evolving toward causal evidence for scenarios not studied in randomized trials necessitates …
Evolving toward causal evidence for scenarios not studied in randomized trials necessitates …
A flexible approach for variable selection in large-scale healthcare database studies with missing covariate and outcome data
Background Prior work has shown that combining bootstrap imputation with tree-based
machine learning variable selection methods can provide good performances achievable on …
machine learning variable selection methods can provide good performances achievable on …