evalues.OR {EValue}

Compute E-value for an odds ratio and its confidence interval limits

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 if needed when outcome is common) as well as E-values for the point estimate and the confidence interval limit closer to the null.

Usage

evalues.OR(est, lo = NA, hi = NA, rare = NA, true = 1, ...)

Arguments

est

The point estimate

lo

The lower limit of the confidence interval

hi

The upper limit of the confidence interval

rare

1 if outcome is rare (<15 percent at end of follow-up); 0 if outcome is not rare (>15 percent at end of follow-up)

true

The true OR to which to shift the observed point estimate. Typically set to 1 to consider a null true effect.

...

Arguments passed to other methods.

Examples

# compute E-values for OR = 0.86 with CI: [0.75, 0.99]
# for a common outcome
evalues.OR(0.86, 0.75, 0.99, rare = FALSE)

## Example 2
## Hsu and Small (2013 Biometrics) Data
## sensitivity analysis after log-linear or logistic regression

head(lead)

## log linear model -- obtain the conditional risk ratio
lead.loglinear = glm(lead ~ ., family = binomial(link = "log"),
                         data = lead[,-1])
est = summary(lead.loglinear)$coef["smoking", c(1, 2)]

RR       = exp(est[1])
lowerRR  = exp(est[1] - 1.96*est[2])
upperRR  = exp(est[1] + 1.96*est[2])
evalues.RR(RR, lowerRR, upperRR)

## logistic regression -- obtain the conditional odds ratio
lead.logistic = glm(lead ~ ., family = binomial(link = "logit"),
                        data = lead[,-1])
est = summary(lead.logistic)$coef["smoking", c(1, 2)]

OR       = exp(est[1])
lowerOR  = exp(est[1] - 1.96*est[2])
upperOR  = exp(est[1] + 1.96*est[2])
evalues.OR(OR, lowerOR, upperOR, rare=FALSE)