Contextualizing E-values for Interpretable Sensitivity to Unmeasured Confounding Analyses

11/13/2020
by   Lucy D'Agostino McGowan, et al.
0

The strength of evidence provided by epidemiological and observational studies is inherently limited by the potential for unmeasured confounding. Researchers should present a quantified sensitivity to unmeasured confounding analysis that is contextualized by the study's observed covariates. VanderWeele and Ding's E-value provides an easily calculated metric for the magnitude of the hypothetical unmeasured confounding required to render the study's result inconclusive. We propose the Observed Covariate E-value to contextualize the sensitivity analysis' hypothetical E-value within the actual impact of observed covariates, individually or within groups. We introduce a sensitivity analysis figure that presents the Observed Covariate E-values, on the E-value scale, next to their corresponding observed bias effects, on the original scale of the study results. This observed bias plot allows easy comparison of the hypothetical E-values, Observed Covariate E-values, and observed bias effects. We illustrate the methods with a specific example and provide a supplemental appendix with modifiable code that teaches how to implement the method and create a publication quality figure.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
12/05/2019

Sensitivity Analysis via the Proportion of Unmeasured Confounding

In observational studies, identification of ATEs is generally achieved b...
research
05/06/2020

Multiple-bias sensitivity analysis using bounds

Unmeasured confounding, selection bias, and measurement error are well-k...
research
11/15/2017

An Extended Sensitivity Analysis for Heterogeneous Unmeasured Confounding

The conventional model for assessing insensitivity to hidden bias in pai...
research
02/07/2023

Sensitivity analysis for incomplete data via unmeasured confounding

We present a method to analyze sensitivity of frequentist inferences to ...
research
06/30/2023

Design Sensitivity and Its Implications for Weighted Observational Studies

Sensitivity to unmeasured confounding is not typically a primary conside...
research
05/18/2023

Cumulative differences between paired samples

The simplest, most common paired samples consist of observations from tw...
research
08/02/2022

Variance-based sensitivity analysis for weighting estimators result in more informative bounds

Weighting methods are popular tools for estimating causal effects; asses...

Please sign up or login with your details

Forgot password? Click here to reset