Adjusted odds ratio conditional on potential confounders can be directly obtained from logistic regression. However, those adjusted odds ratios have been widely incorrectly interpreted as a relative risk. As relative risk is often of interest in public health, we provide a simple code to return adjusted relative risks from logistic regression model under potential confounders.

- Version: 0.2.0
- Maintainer : Youjin Lee ([email protected])
- Imports : stats, nnet

You can download the package by:

```
install.packages("logisticRR")
library(logisticRR)
```

or you can directly download the development version from author's Github

```
install.packages("devtools")
library(devtools)
install_github("youjin1207/logisticRR")
```

Here is a R vignettes for guidance. Or you can access to vignettes via:

```
install_github("youjin1207/logisticRR", build_vignettes = TRUE)
library(logisticRR)
vignette("logisticRR", package = "logisticRR")
```

```
n <- 500
set.seed(1234)
X <- rbinom(n, 1, 0.3)
W <- rbinom(n, 1, 0.3); W[sample(1:n, n/3)] = 2
Z <- rep(0, n)
Z[sample(1:n, n/2)] <- "female"; Z <- ifelse(Z == 0, "male", Z)
dummyZ <- ifelse(Z == "female", 1, 0)
Y <- rbinom(n, 1, plogis(X - W + 2*dummyZ))
dat <- as.data.frame(cbind(Y, X, W, Z))
dat$X <- as.numeric(dat$X); dat$X <- ifelse(dat$X == 2, 1, 0)
dat$Y <- as.numeric(dat$Y); dat$Y <- ifelse(dat$Y == 2, 1, 0)
dat$W <- as.factor(dat$W)
dat$Z <- as.factor(dat$Z)
```

```
simresult <- logisticRR(Y ~ X + W + Z, data = dat, boot = TRUE, n.boot = 200)
var(simresult$boot.rr)
simresult$delta.var
simresult$RR
```

```
nominalresult <- logisticRR(Y ~ W + X + Z, data = dat, boot = TRUE, n.boot = 200)
var(nominalresult$boot.rr)
nominalresult$delta.var
nominalresult$RR
```

When reponse variable takes more than two values, multinomial logistic regression is widely used to reveal association between the response variable and exposure variable. In that case, relative risk of each category compared to the reference category can be considered, conditional on other fixed covariates. Other than (adjusted) relative risk, relative risks ratio (RRR) is often of interest in multinomial logistic regression.

```
dat$multiY <- ifelse(dat$X == 1, rbinom(n, 1, 0.8) + dat$Y, rbinom(n, 1, 0.2) + dat$Y)
multiresult <- multiRR(multiY ~ X + W + Z, data = dat, boot = TRUE, n.boot = 1000)
apply(multiresult$boot.rr, 2, sd)
sqrt(multiresult$delta.var)
multiresult$RRR
multiresult$RR
```

Similar to the binary reponse, in multinomial logistic regression model, categorical exposure variable can be introduced; in this case, baseline value and comparative value of exposure variable should be specified.

```
multinresult <- multinRR(multiY ~ W + X + Z, data = dat, basecov = 0, comparecov = 1, boot = TRUE, n.boot = 1000)
apply(multinresult$boot.rr, 2, sd)
sqrt(multinresult$delta.var)
multinresult$RRR
multinresult$RR
```