effect_measures: Declare an effect measure In EValue: Sensitivity Analyses for Unmeasured Confounding and Other Biases in Observational Studies and Meta-Analyses

Description

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`).

Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11``` ```RR(est) OR(est, rare) HR(est, rare) RD(est) OLS(est, sd) MD(est) ```

Arguments

 `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 `HR()` and `OR()`; see Details. `sd` The standard deviation of the outcome (or residual standard deviation). Used only for `OLS()`; see Details.

Details

The conversion functions use these objects to convert between effect measures when necessary to calculate E-values. Read more about the conversions in Table 2 of VanderWeele TJ, Ding P. Sensitivity Analysis in Observational Research: Introducing the E-Value. 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 E-value for the confidence interval treats `sd` as known, not estimated.

Value

An object of classes "estimate" and the measure of interest, containing the effect estimate and any other attributes to be used in future calculations.

Examples

 ``` 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 E-value 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))) ```

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