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 …
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 …
Estimation of causal effects of multiple treatments in observational studies with a binary outcome
There is a dearth of robust methods to estimate the causal effects of multiple treatments
when the outcome is binary. This paper uses two unique sets of simulations to propose and …
when the outcome is binary. This paper uses two unique sets of simulations to propose and …
Tree‐based machine learning to identify and understand major determinants for stroke at the neighborhood level
Background Stroke is a major cardiovascular disease that causes significant health and
economic burden in the United States. Neighborhood community‐based interventions have …
economic burden in the United States. Neighborhood community‐based interventions have …
A scoping review of studies using observational data to optimise dynamic treatment regimens
Abstract Background Dynamic treatment regimens (DTRs) formalise the multi-stage and
dynamic decision problems that clinicians often face when treating chronic or progressive …
dynamic decision problems that clinicians often face when treating chronic or progressive …
Causality for Functional Longitudinal Data
A Ying - Causal Learning and Reasoning, 2024 - proceedings.mlr.press
Abstract “Treatment-confounder feedback” is the central complication to resolve in
longitudinal studies, to infer causality. The existing frameworks of identifying causal effects …
longitudinal studies, to infer causality. The existing frameworks of identifying causal effects …
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 …
Identifying and understanding determinants of high healthcare costs for breast cancer: a quantile regression machine learning approach
Background To identify and rank the importance of key determinants of high medical
expenses among breast cancer patients and to understand the underlying effects of these …
expenses among breast cancer patients and to understand the underlying effects of these …
Impact of discretization of the timeline for longitudinal causal inference methods
S Ferreira Guerra, ME Schnitzer, A Forget… - Statistics in …, 2020 - Wiley Online Library
In longitudinal settings, causal inference methods usually rely on a discretization of the
patient timeline that may not reflect the underlying data generation process. This article …
patient timeline that may not reflect the underlying data generation process. This article …