Sensitivity Analyses for Unmeasured Confounders

This paper aimed to provide methods and tools to implement sensitivity to unmeasured confounder analyses appropriate for various research settings.

Bias formulas for sensitivity analysis of unmeasured confounding for ...

Uncontrolled confounding in observational studies gives rise to biased effect estimates. Sensitivity analysis techniques can be useful in assessing the ...

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 ...

Conducting sensitivity analysis for unmeasured confounding in ...

Abstract. In this article, we introduce the evalue package, which performs sensitivity analyses for unmeasured confounding in observational studies using.

Sensitivity Analysis for Unmeasured Confounding in Meta-Analyses

If a well-designed meta-analysis yields a low value of T ^ ( r , q ) or G ^ ( r , q ) and thus is relatively sensitive to unmeasured confounding ...

Sensitivity Analysis via the Proportion of Unmeasured Confounding

To gauge the consequences of departures from the no-unmeasured-confounding assumption, a sensitivity analysis generally posits the existence of an unmeasured ...

Sensitivity analysis via the proportion of unmeasured confounding

In this paper, we take a novel approach whereby the sensitivity parameter is the "proportion of unmeasured confounding:" the proportion of units ...

Sensitivity Analyses for Unmeasured Confounders - ResearchGate

PDF | Purpose of Review This review expands on sensitivity analyses for unmeasured confounding techniques, demonstrating state-of-the-art methods as.

Sensitivity analyses to estimate the potential impact of unmeasured ...

To analyse how sensitive the association between the exposure and the outcome is to unmeasured confounding, the characteristics of the ...

Sensitivity analysis for the effects of multiple unmeasured confounders

The purpose of this study was to assess the impact of multiple (possibly weak) unmeasured confounders.

Conducting sensitivity analysis for unmeasured confounding in ...

evalue computes E-values for point estimates (and optionally, confidence limits) for several common outcome types, including risk and rate ...

Sensitivity Analyses for Unmeasured Confounding: This Is the Way

In this chapter, we review existing sensitivity analysis approaches and argue for a growing consensus of best practice principles.

An R package for sensitivity analyses for unmeasured confounders

There are several related methods for conducting sensitivity analyses for unmeasured confounders (Bross, 1966; Cinelli & Hazlett, 2020; ...

A note on a sensitivity analysis for unmeasured confounding, and ...

Unmeasured confounding is one of the most important threats to the validity of observational studies. In this paper we scrutinize a recently ...

Sensitivity Analysis in Observational Research: Introducing the E ...

Motivation: Observational studies that attempt to assess causality between a treatment and an outcome may be subject to unmeasured confounding.

Sensitivity Analysis for Unmeasured Confounding in Meta-Analyses

Title:Sensitivity Analysis for Unmeasured Confounding in Meta-Analyses ... Abstract:Random-effects meta-analyses of observational studies can ...

Sensitivity analysis for the effects of multiple unmeasured confounders

Our study, based on empirical data, shows that the bias caused by multiple unmeasured confounders can be substantial.

Sensitivity analyses to estimate the potential impact of unmeasured ...

Conclusions In every non-randomized study on causal associations the robust- ness of the results with respect to unmeasured confounding can, and ...

Sensitivity to Unmeasured Confounding - YouTube

sensitivity to unmeasured confounding analyses quantitatively address the primary concern of any observational study, we would expect every ...

Bayesian sensitivity analysis for unmeasured confounding in ...

We consider Bayesian sensitivity analysis for unmeasured confounding in observational studies where the association between a binary ...