Statistical methods for dynamic treatment regimes
B Chakraborty, EE Moodie - Springer-Verlag. doi, 2013 - Springer
This book was written to summarize and describe the state of the art of statistical methods
developed to address questions of estimation and inference for dynamic treatment regimes …
developed to address questions of estimation and inference for dynamic treatment regimes …
Machine learning for improving high‐dimensional proxy confounder adjustment in healthcare database studies: An overview of the current literature
Purpose Supplementing investigator‐specified variables with large numbers of empirically
identified features that collectively serve as 'proxies' for unspecified or unmeasured factors …
identified features that collectively serve as 'proxies' for unspecified or unmeasured factors …
Regularization and confounding in linear regression for treatment effect estimation
Regularization and Confounding in Linear Regression for Treatment Effect Estimation Page 1
Bayesian Analysis (2018) 13, Number 1, pp. 163–182 Regularization and Confounding in Linear …
Bayesian Analysis (2018) 13, Number 1, pp. 163–182 Regularization and Confounding in Linear …
Covariate selection with group lasso and doubly robust estimation of causal effects
The efficiency of doubly robust estimators of the average causal effect (ACE) of a treatment
can be improved by including in the treatment and outcome models only those covariates …
can be improved by including in the treatment and outcome models only those covariates …
The future of causal inference
The past several decades have seen exponential growth in causal inference approaches
and their applications. In this commentary, we provide our top-10 list of emerging and …
and their applications. In this commentary, we provide our top-10 list of emerging and …
The change in estimate method for selecting confounders: A simulation study
Background The change in estimate is a popular approach for selecting confounders in
epidemiology. It is recommended in epidemiologic textbooks and articles over significance …
epidemiology. It is recommended in epidemiologic textbooks and articles over significance …
Doubly robust matching estimators for high dimensional confounding adjustment
Valid estimation of treatment effects from observational data requires proper control of
confounding. If the number of covariates is large relative to the number of observations, then …
confounding. If the number of covariates is large relative to the number of observations, then …
Data science in environmental health research
Abstract Purpose of Review Data science is an exploding trans-disciplinary field that aims to
harness the power of data to gain information or insights on researcher-defined topics of …
harness the power of data to gain information or insights on researcher-defined topics of …
Framework for identifying drug repurposing candidates from observational healthcare data
Objective Observational medical databases, such as electronic health records and
insurance claims, track the healthcare trajectory of millions of individuals. These databases …
insurance claims, track the healthcare trajectory of millions of individuals. These databases …
AteMeVs: An R package for the estimation of the average treatment effect with measurement error and variable selection for confounders
LP Chen, GY Yi - Plos one, 2024 - journals.plos.org
In causal inference, the estimation of the average treatment effect is often of interest. For
example, in cancer research, an interesting question is to assess the effects of the …
example, in cancer research, an interesting question is to assess the effects of the …