Description Usage Arguments Details Value Examples
These functions allow the user to declare that an estimate is a
certain type of effect measure: risk ratio (RR
), odds ratio (OR
),
hazard ratio (HR
), risk difference (RD
), linear regression coefficient
(OLS
), or mean standardized difference (MD
).
1 2 3 4 5 6 7 8 9 10 11 
est 
The effect estimate (numeric). 
rare 
Logical. Whether the outcome is sufficiently rare for use of risk
ratio approximates; if not, approximate conversions are used. Used only for

sd 
The standard deviation of the outcome (or residual standard
deviation). Used only for 
The conversion functions use these objects to convert between effect measures when necessary to calculate Evalues. Read more about the conversions in Table 2 of VanderWeele TJ, Ding P. Sensitivity Analysis in Observational Research: Introducing the EValue. Annals of Internal Medicine. 2017;167(4):268–75.
See also VanderWeele TJ. Optimal approximate conversions of odds ratios and hazard ratios to risk ratios. Biometrics. 2019 Jan 6;(September 2018):1–7.
For OLS()
, sd
must be specified. A true standardized mean difference
for linear regression would use sd
= SD( Y  X, C ), where Y is the
outcome, X is the exposure of interest, and C are any adjusted covariates.
See Examples for how to extract this from lm
. A conservative
approximation would instead use sd
= SD( Y ). Regardless, the
reported Evalue for the confidence interval treats sd
as known, not
estimated.
An object of classes "estimate" and the measure of interest, containing the effect estimate and any other attributes to be used in future calculations.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25  # Both odds ratios are 3, but will be treated differently in Evalue calculations
# depending on whether rare outcome assumption is reasonable
OR(3, rare = FALSE)
OR(3, rare = TRUE)
evalue(OR(3, rare = FALSE))
evalue(OR(3, rare = TRUE))
attributes(OR(3, rare = FALSE))
# If an estimate was constructed via conversion from another effect measure,
# we can see the history of a conversion using the summary() function
summary(toRR(OR(3, rare = FALSE)))
summary(toRR(OLS(3, sd = 1)))
# Estimating sd for an OLS estimate
# first standardizing conservatively by SD(Y)
data(lead)
ols = lm(age ~ income, data = lead)
est = ols$coefficients[2]
sd = sd(lead$age)
summary(evalue(OLS(est, sd)))
# now use residual SD to avoid conservatism
# here makes very little difference because income and age are
# not highly correlated
sd = summary(ols)$sigma
summary(evalue(OLS(est, sd)))

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