Using propensity scores to estimate effects of treatment initiation decisions: state of the science
Confounding can cause substantial bias in nonexperimental studies that aim to estimate
causal effects. Propensity score methods allow researchers to reduce bias from measured …
causal effects. Propensity score methods allow researchers to reduce bias from measured …
Matching methods for confounder adjustment: an addition to the epidemiologist's toolbox
Propensity score weighting and outcome regression are popular ways to adjust for observed
confounders in epidemiologic research. Here, we provide an introduction to matching …
confounders in epidemiologic research. Here, we provide an introduction to matching …
Redlines and greenspace: the relationship between historical redlining and 2010 greenspace across the United States
A Nardone, KE Rudolph… - Environmental health …, 2021 - ehp.niehs.nih.gov
Introduction: Redlining, a racist mortgage appraisal practice of the 1930s, established and
exacerbated racial residential segregation boundaries in the United States. Investment risk …
exacerbated racial residential segregation boundaries in the United States. Investment risk …
The effect of carbon fertilization on naturally regenerated and planted US forests
Over the last half century in the United States, the per-hectare volume of wood in trees has
increased, but it is not clear whether this increase has been driven by forest management …
increased, but it is not clear whether this increase has been driven by forest management …
G-computation, propensity score-based methods, and targeted maximum likelihood estimator for causal inference with different covariates sets: a comparative …
Controlling for confounding bias is crucial in causal inference. Distinct methods are currently
employed to mitigate the effects of confounding bias. Each requires the introduction of a set …
employed to mitigate the effects of confounding bias. Each requires the introduction of a set …
The current landscape in biostatistics of real-world data and evidence: causal inference frameworks for study design and analysis
As real-world data (RWD) become more readily available, the regulatory agencies, medical
product developers, and other key stakeholders have increasing interests in exploring the …
product developers, and other key stakeholders have increasing interests in exploring the …
Environmental noise and sleep and mental health outcomes in a nationally representative sample of urban US adolescents
Background: Environmental noise has been linked to negative health outcomes, like poor
sleep, poor mental health, and cardiovascular disease, and likely accounts for more than 1 …
sleep, poor mental health, and cardiovascular disease, and likely accounts for more than 1 …
Developing a targeted learning-based statistical analysis plan
Abstract The Targeted Learning estimation roadmap provides a rigorous framework for
developing a statistical analysis plan (SAP) for synthesizing evidence from randomized …
developing a statistical analysis plan (SAP) for synthesizing evidence from randomized …
Nearest neighbour propensity score matching and bootstrapping for estimating binary patient response in oncology: a Monte Carlo simulation
T Geldof, D Popovic, N Van Damme, I Huys… - Scientific reports, 2020 - nature.com
Nearest Neighbour (NN) propensity score (PS) matching methods are commonly used in
pharmacoepidemiology to estimate treatment response using observational data …
pharmacoepidemiology to estimate treatment response using observational data …
Heterogeneous treatment effect analysis based on machine‐learning methodology
Heterogeneous treatment effect (HTE) analysis focuses on examining varying treatment
effects for individuals or subgroups in a population. For example, an HTE‐informed …
effects for individuals or subgroups in a population. For example, an HTE‐informed …