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

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 ...

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

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 ...

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

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 ...

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

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

Monte Carlo Simulation Approaches for Quantitative Bias Analysis

Quantitative bias analysis can be used to empirically assess how far study estimates are from the truth (i.e., an estimate that is free of bias).

Chapter 8: Assessing risk of bias in a randomized trial

If prognostic factors influence the intervention group to which participants are assigned then the estimated effect of intervention will be biased by ' ...

Quantitative bias analysis methods for summary-level epidemiologic ...

Quantitative bias analysis (QBA) methods can be used to evaluate the impact of biases on observational study results. However, little is known about the full ...

Application of a Web-based Tool for Quantitative Bias Analysis

For an analysis of exposure misclassification, there are four relevant bias parameters: sensitivity, specificity, positive predictive value (PPV), and negative ...

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 ...

Assessing risk of bias due to missing evidence in a meta-analysis

A thorough assessment of selective non-reporting or under-reporting of results in the studies identified is likely to be the most valuable. Because the number ...

Use of quantitative bias analysis to evaluate single‐arm trials with ...

Bias analysis is used to estimate a bias-adjusted treatment effect and quantify uncertainty using confidence intervals (CIs) or simulation ...

Unmeasured confounding in nonrandomized studies: quantitative ...

Quantitative bias analyses are a group of methods that have been developed in the epidemiological literature to quantify the impact of unmeasured confounding.

Bias in simulation training for healthcare professions: a scoping review

The type of simulation training most prevalent in the articles was simulated patient (SP) methodology. The results show that biases can be ...

Quantitative Assessment of Systematic Bias: A Guide for Researchers

Quantitative bias analysis is a set of methodological techniques that, when applied to observational data, can provide important context to aid ...

Probabilistic Sensitivity Analysis of Misclassification

Probabilistic sensitivity analysis is a quantitative method to account for uncertainty in the true values of bias parameters, and to simulate the effects of ...

Sensitivity Analysis and Bias Analysis - SpringerLink

The discrepancies between the statistical model parameters and the underlying target effects are often called systematic errors, biases, or bias sources. Large ...

Application of quantitative bias analysis for unmeasured ...

Quantitative bias analysis (QBA) methods provide a means to quantify this uncertainty but have not been widely used in the HTA setting, ...

Applying quantitative bias analysis to estimate the plausible effects ...

Selection bias is a concern when designing cluster randomised controlled trials (c-RCT). Despite addressing potential issues at the design ...