evalues.OR.RdReturns 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.
evalues.OR(est, lo = NA, hi = NA, rare = NA, true = 1, ...)
| 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. |
# 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)#> point lower upper #> RR 0.9273618 0.8660254 0.9949874 #> E-values 1.3689529 NA 1.0761939## Example 2 ## Hsu and Small (2013 Biometrics) Data ## sensitivity analysis after log-linear or logistic regression head(lead)#> id smoking lead age male edu.lt9 edu.9to11 edu.hischl edu.somecol #> 1 41493 1 FALSE 77 0 0 1 0 0 #> 2 41502 1 FALSE 29 1 0 0 1 0 #> 3 41512 1 FALSE 80 0 0 0 0 1 #> 4 41545 1 FALSE 40 0 1 0 0 0 #> 5 41556 1 FALSE 38 1 0 1 0 0 #> 6 41558 1 FALSE 50 0 0 1 0 0 #> edu.college edu.unknown income income.mis white black mexicanam otherhispan #> 1 0 0 1.57 0 1 0 0 0 #> 2 0 0 3.41 0 0 0 1 0 #> 3 0 0 1.24 0 1 0 0 0 #> 4 0 0 1.27 0 0 0 1 0 #> 5 0 0 1.24 0 1 0 0 0 #> 6 0 0 1.22 0 0 1 0 0 #> otherrace #> 1 0 #> 2 0 #> 3 0 #> 4 0 #> 5 0 #> 6 0## log linear model -- obtain the conditional risk ratio lead.loglinear = glm(lead ~ ., family = binomial(link = "log"), data = lead[,-1])#> Warning: glm.fit: algorithm did not convergeest = summary(lead.loglinear)$coef["smoking", c(1, 2)] RR = exp(est[1]) lowerRR = exp(est[1] - 1.96*est[2]) upperRR = exp(est[1] + 1.96*est[2]) evalues.RR(RR, lowerRR, upperRR)#> point lower upper #> RR 2.466433 1.672663 3.63689 #> E-values 4.368237 2.733388 NA## logistic regression -- obtain the conditional odds ratio lead.logistic = glm(lead ~ ., family = binomial(link = "logit"), data = lead[,-1])#> Warning: glm.fit: algorithm did not convergeest = summary(lead.logistic)$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)#> point lower upper #> RR 1.643167 1.317079 2.049988 #> E-values 2.671189 1.963313 NA