simulation|based bias analysis to assess the impact of unmeasured ...

simulation-based bias analysis to assess the impact of unmeasured ...

We developed a simulation-based approach to assess the potential impact of unmeasured confounding during the study design stage.

A simulation-based bias analysis to assess the impact of ... - PubMed

An approach using simulated individual-level data was useful to explicitly convey the potential for bias due to unmeasured confounding while ...

simulation-based bias analysis to assess the impact of unmeasured ...

Unmeasured confounding is often raised as a source of potential bias during the design of nonrandomized studies, but quantifying such concerns is challenging.

(PDF) A simulation-based bias analysis to assess the impact of ...

Background: Unmeasured confounding is often raised as a source of potential bias during the design of non-randomized studies but quantifying ...

Rishi Desai posted on the topic | LinkedIn

In American Journal of Epidemiology, we describe a simulation-based approach for bias analysis to assess the impact of unmeasured ...

Assessing Techniques for Quantifying the Impact of Bias Due to an ...

We performed a quantitative bias analysis (QBA) of the “unmeasured” confounder, using external information to specify the bias parameters. Upper ...

Quantitative bias analysis in practice: review of software for ...

A QBA can be used to quantify the potential impact of unmeasured confounding on an exposure effect estimate or to quantify how much unmeasured ...

Unmeasured confounding in nonrandomized studies: quantitative ...

There are a variety of so-called quantitative bias analysis (QBA) methods that have been developed to assess the impact of confounding and other ...

A probabilistic bias analysis on the magnitude of unmeasured ...

Probabilistic bias analysis using Monte Carlo simulations (MCS) is an increasingly valuable tool to determine and quantify unmeasured confounding in ...

Quantifying the impact of unmeasured confounding in observational ...

The E value method deals with unmeasured confounding, a key source of bias in observational studies ... analysis for the confounders ...

Advancing Analytical Methodologies for Unmeasured Confounding ...

quantitative bias analysis and confounding. ... increase the use of quantitative sensitivity analyses to assess the potential impact of unmeasured confounding.

Assessing the impact of unmeasured confounders for credible ... - OSF

sively on quantitative bias analysis.18,19 ... biases inherent in non-interventional RWD-based studies, including unmeasured confounding.

A quantitative bias analysis to assess the impact of unmeasured ...

Hypothetical confounders were simulated into the dataset using Bernoulli trials based on pre-specified distributions of confounders within ...

Application of quantitative bias analysis for unmeasured ...

We have proposed in this study that QBA methods assessing the sensitivity of the treatment effect to unmeasured confounding could also be ...

Evaluating the Impact of Unmeasured Confounding with Internal ...

The quantitative assessment of the potential influence of unmeasured confounders in the analysis of observational data is rare, despite reliance on the “no ...

Assessing the impact of unmeasured confounders for credible and ...

... Probabilistic bias analysis using Monte Carlo simulations (MCS) is an increasingly valuable tool to determine and quantify unmeasured confounding in ...

Quantitative bias analysis in practice: Review of software for ...

Quantitative bias analysis (QBA) for unmeasured confounding is used to quantify the potential direction and impact of the bias. The adoption of ...

[PDF] The impact of residual and unmeasured confounding in ...

The authors use simulation studies and logistic regression analyses to investigate the size of the apparent exposure-outcome association that can occur when ...

A Quantitative Bias Analysis Framework for Real-World Comparative ...

We perform a simulation study to assess the precision and accuracy of the proposed framework in the presence of unmeasured confounding and a significant ...

Sensitivity Analysis Without Assumptions - Epidemiology

Sensitivity analysis provides an attractive way to partially circumvent this issue by assessing the potential influence of unmeasured confounding on causal ...