# evalues.MD: Compute E-value for a difference of means and its confidence... In EValue: Sensitivity Analyses for Unmeasured Confounding and Other Biases in Observational Studies and Meta-Analyses

## Description

Returns a data frame containing point estimates, the lower confidence limit, and the upper confidence limit on the risk ratio scale (through an approximate conversion) as well as E-values for the point estimate and the confidence interval limit closer to the null.

## Usage

 `1` ```evalues.MD(est, se = NA, true = 0, ...) ```

## Arguments

 `est` The point estimate as a standardized difference (i.e., Cohen's d) `se` The standard error of the point estimate `true` The true standardized mean difference to which to shift the observed point estimate. Typically set to 0 to consider a null true effect. `...` Arguments passed to other methods.

## Details

Regarding the continuous outcome, the function uses the effect-size conversions in Chinn (2000) and VanderWeele (2017) to approximately convert the mean difference between the exposed versus unexposed groups to the odds ratio that would arise from dichotomizing the continuous outcome.

For example, if resulting E-value is 2, this means that unmeasured confounder(s) would need to double the probability of a subject's being exposed versus not being exposed, and would also need to double the probability of being high versus low on the outcome, in which the cutoff for "high" versus "low" is arbitrary subject to some distributional assumptions (Chinn, 2000).

## References

Chinn, S (2000). A simple method for converting an odds ratio to effect size for use in meta-analysis. Statistics in Medicine, 19(22), 3127-3131.

VanderWeele, TJ (2017). On a square-root transformation of the odds ratio for a common outcome. Epidemiology, 28(6), e58.

## Examples

 ```1 2``` ```# compute E-value if Cohen's d = 0.5 with SE = 0.25 evalues.MD(.5, .25) ```

EValue documentation built on Oct. 28, 2021, 9:10 a.m.