Propensity scores in pharmacoepidemiology: beyond the horizon
JW Jackson, I Schmid, EA Stuart - Current epidemiology reports, 2017 - Springer
Abstract Purpose of Review Propensity score methods have become commonplace in
pharmacoepidemiology over the past decade. Their adoption has confronted formidable …
pharmacoepidemiology over the past decade. Their adoption has confronted formidable …
Association between acute kidney injury and norepinephrine use following cardiac surgery: a retrospective propensity score-weighted analysis
Background Excess exposure to norepinephrine can compromise microcirculation and
organ function. We aimed to assess the association between norepinephrine exposure and …
organ function. We aimed to assess the association between norepinephrine exposure and …
Variable selection for causal mediation analysis using LASSO-based methods
Z Ye, Y Zhu, DL Coffman - Statistical methods in medical …, 2021 - journals.sagepub.com
Causal mediation effect estimates can be obtained from marginal structural models using
inverse probability weighting with appropriate weights. In order to compute weights …
inverse probability weighting with appropriate weights. In order to compute weights …
Evaluation of propensity score methods for causal inference with high-dimensional covariates
Q Gao, Y Zhang, H Sun, T Wang - Briefings in Bioinformatics, 2022 - academic.oup.com
In recent work, researchers have paid considerable attention to the estimation of causal
effects in observational studies with a large number of covariates, which makes the …
effects in observational studies with a large number of covariates, which makes the …
A model averaging approach for estimating propensity scores by optimizing balance
Many approaches, including traditional parametric modeling and machine learning
techniques, have been proposed to estimate propensity scores. This paper describes a new …
techniques, have been proposed to estimate propensity scores. This paper describes a new …
Classical regression and predictive modeling
RJ Cook, KA Lee, BWY Lo, RL Macdonald - World Neurosurgery, 2022 - Elsevier
Background With the advent of personalized and stratified medicine, there has been much
discussion about predictive modeling and the role of classical regression in modern medical …
discussion about predictive modeling and the role of classical regression in modern medical …
High-dimensional generalized propensity score with application to omics data
Q Gao, Y Zhang, J Liang, H Sun… - Briefings in …, 2021 - academic.oup.com
Propensity score (PS) methods are popular when estimating causal effects in non-
randomized studies. Drawing causal conclusion relies on the unconfoundedness …
randomized studies. Drawing causal conclusion relies on the unconfoundedness …
Using conventional and machine learning propensity score methods to examine the effectiveness of 12-step group involvement following inpatient addiction treatment
Background Continuing care following inpatient addiction treatment is an important
component in the continuum of clinical services. Mutual help, including 12-step groups like …
component in the continuum of clinical services. Mutual help, including 12-step groups like …
A kernel-based metric for balance assessment
An important goal in causal inference is to achieve balance in the covariates among the
treatment groups. In this article, we introduce the concept of distributional balance …
treatment groups. In this article, we introduce the concept of distributional balance …
Propensity score specification for optimal estimation of average treatment effect with binary response
JA Craycroft, J Huang, M Kong - Statistical Methods in …, 2020 - journals.sagepub.com
Propensity score methods are commonly used in statistical analyses of observational data to
reduce the impact of confounding bias in estimations of average treatment effect. While the …
reduce the impact of confounding bias in estimations of average treatment effect. While the …