[HTML][HTML] Framework for the treatment and reporting of missing data in observational studies: the treatment and reporting of missing data in observational studies …
Missing data are ubiquitous in medical research. Although there is increasing guidance on
how to handle missing data, practice is changing slowly and misapprehensions abound …
how to handle missing data, practice is changing slowly and misapprehensions abound …
Bayesian regression tree models for causal inference: Regularization, confounding, and heterogeneous effects (with discussion)
This paper presents a novel nonlinear regression model for estimating heterogeneous
treatment effects, geared specifically towards situations with small effect sizes …
treatment effects, geared specifically towards situations with small effect sizes …
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 …
A comparison of Bayesian and Monte Carlo sensitivity analysis for unmeasured confounding
LC McCandless, P Gustafson - Statistics in medicine, 2017 - Wiley Online Library
Bias from unmeasured confounding is a persistent concern in observational studies, and
sensitivity analysis has been proposed as a solution. In the recent years, probabilistic …
sensitivity analysis has been proposed as a solution. In the recent years, probabilistic …
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 …
[HTML][HTML] Machine learning to identify and understand key factors for provider-patient discussions about smoking
We sought to identify key determinants of the likelihood of provider-patient discussions
about smoking and to understand the effects of these determinants. We used data on 3666 …
about smoking and to understand the effects of these determinants. We used data on 3666 …
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 …
Estimation of causal effects of multiple treatments in healthcare database studies with rare outcomes
The preponderance of large-scale healthcare databases provide abundant opportunities for
comparative effectiveness research. Evidence necessary to making informed treatment …
comparative effectiveness research. Evidence necessary to making informed treatment …