The influence of unmeasured confounding on the MR Steiger approach
Sign Up to like & getrecommendations! Published in 2022 at "Genetic Epidemiology"
DOI: 10.1002/gepi.22442
Abstract: The Mendelian Randomization (MR) Steiger approach is used to determine the direction of a possible causal effect between two phenotypes (Hemani et al., 2017). For two phenotypes, denoted phenotype 1 and 2, the MR Steiger… read more here.
Keywords: phenotype; unmeasured confounding; steiger approach;
Tuning Random Forests for Causal Inference under Cluster-Level Unmeasured Confounding.
Sign Up to like & getrecommendations! Published in 2022 at "Multivariate behavioral research"
DOI: 10.1080/00273171.2021.1994364
Abstract: Recently, there has been growing interest in using machine learning methods for causal inference due to their automatic and flexible ability to model the propensity score and the outcome model. However, almost all the machine… read more here.
Keywords: causal inference; level unmeasured; unmeasured confounding; cluster level ... See more keywords
Adjusting for Unmeasured Confounding Variables in Dynamic Networks
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Control Systems Letters"
DOI: 10.1109/lcsys.2022.3233701
Abstract: This letter presents a technique to identify a certain transfer function in a dynamic network when the input and the output of the transfer function are influenced by an unmeasured confounding variable. It is assumed… read more here.
Keywords: confounding; confounding variables; unmeasured confounding; transfer function ... See more keywords
Unifying instrumental variable and inverse probability weighting approaches for inference of causal treatment effect and unmeasured confounding in observational studies
Sign Up to like & getrecommendations! Published in 2020 at "Statistical Methods in Medical Research"
DOI: 10.1177/0962280220971835
Abstract: Confounding is a major concern when using data from observational studies to infer the causal effect of a treatment. Instrumental variables, when available, have been used to construct bound estimates on population average treatment effects… read more here.
Keywords: inverse probability; treatment; effect; probability weighting ... See more keywords
How unmeasured confounding in a competing risks setting can affect treatment effect estimates in observational studies
Sign Up to like & getrecommendations! Published in 2019 at "BMC Medical Research Methodology"
DOI: 10.1186/s12874-019-0808-7
Abstract: BackgroundAnalysis of competing risks is commonly achieved through a cause specific or a subdistribution framework using Cox or Fine & Gray models, respectively. The estimation of treatment effects in observational data is prone to unmeasured… read more here.
Keywords: treatment; effect; competing risks; event ... See more keywords
Analysis approaches to address treatment nonadherence in pragmatic trials with point-treatment settings: a simulation study
Sign Up to like & getrecommendations! Published in 2022 at "BMC Medical Research Methodology"
DOI: 10.1186/s12874-022-01518-8
Abstract: Background Two-stage least square [2SLS] and two-stage residual inclusion [2SRI] are popularly used instrumental variable (IV) methods to address medication nonadherence in pragmatic trials with point treatment settings. These methods require assumptions, e.g., exclusion restriction,… read more here.
Keywords: nonadherence pragmatic; unmeasured confounding; treatment; nonadherence ... See more keywords
Estimating Bias Due to Unmeasured Confounding in Oral Health Epidemiology.
Sign Up to like & getrecommendations! Published in 2020 at "Community dental health"
DOI: 10.1922/cdh_specialissuemittinty06
Abstract: Confounding can make an association seem bigger when the true effect is smaller or vice-versa and it can also make it appear negative when it may actually be positive. In short, both the direction and… read more here.
Keywords: health; estimating bias; due unmeasured; bias due ... See more keywords
Unmeasured confounding in nonrandomized studies: quantitative bias analysis in health technology assessment.
Sign Up to like & getrecommendations! Published in 2022 at "Journal of comparative effectiveness research"
DOI: 10.2217/cer-2022-0029
Abstract: Evidence generated from nonrandomized studies (NRS) is increasingly submitted to health technology assessment (HTA) agencies. Unmeasured confounding is a primary concern with this type of evidence, as it may result in biased treatment effect estimates,… read more here.
Keywords: unmeasured confounding; health technology; quantitative bias; technology assessment ... See more keywords
SENSITIVITY ANALYSIS FOR UNMEASURED CONFOUNDING IN COARSE STRUCTURAL NESTED MEAN MODELS.
Sign Up to like & getrecommendations! Published in 2018 at "Statistica Sinica"
DOI: 10.5705/ss.202016.0133
Abstract: Coarse Structural Nested Mean Models (SNMMs, Robins (2000)) and G-estimation can be used to estimate the causal effect of a time-varying treatment from longitudinal observational studies. However, they rely on an untestable assumption of no… read more here.
Keywords: mean models; coarse structural; nested mean; sensitivity ... See more keywords
Sensitivity Analysis for Unmeasured Confounding: E-Values for Observational Studies
Sign Up to like & getrecommendations! Published in 2017 at "Annals of Internal Medicine"
DOI: 10.7326/m17-1485
Abstract: In their current article in Annals, VanderWeele and Ding (1) introduce the E-value as a simple measure of the potential for bias arising from unmeasured confounders in observational studies. Bias often poses a greater threat… read more here.
Keywords: value; sensitivity; bias; observational studies ... See more keywords