# nominalRR: Calculate adjusted relative risks under nominal exposure... In youjin1207/logisticRR: Adjusted Relative Risk from Logistic Regression

## Description

When response variable is binary and exposure variable is categorical this function derives adjusted relative risks conditional on fixed other confounders' value from logistic regression.

## Usage

 ```1 2``` ```nominalRR(formula, basecov = NULL, comparecov = NULL, fixcov = NULL, data, boot = FALSE, n.boot = 100) ```

## Arguments

 `formula` a formula term that is passed into `glm()` having a form of `response ~ terms` where `response` is binary response vector and `terms` is a collection of terms connected by `'+'`. The first term of predictors will be used as a predictor of interest to calculate relative risks with respect to response variable. `basecov` a baseline value of exposure variable. Defaults to the first level. `comparecov` a value of exposure variable for comparison. Defaults to the first level. `fixcov` a data frame of fixed value for each of adjusted confounders. If there is no confounder other than an exposure variable of interest, `fixcov` = `NULL`; if `fixcov` is missing for covariates, they are all set to `0` (for numerical covariates) or first levels (for factor covariates). `data` a data frame containing response variable and all the terms used in `formula`. `boot` a logical value whether bootstrap samples are generated or not. Defaults to `FALSE`. `n.boot` if `boot = TRUE`, the number of bootstrap samples. Defaults to `100`.

## Value

 `fit` an object of class `glm`. `RR` (conditional) relative risk in response under exposure at baseline (`basecov`) and `comparecov`. `delta.var` estimated variance of relative risk (`RR`) using Delta method. `boot.rr` if `boot = TRUE`, a vector of `RR`'s using bootstrap samples. `boot.var` estimated sampled variance using bootstraps if `boot = TRUE`. `fix.cov` a data frame of fixed value for each of adjsuted confounders.

Youjin Lee

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11``` ```n <- 500 set.seed(1234) W <- rbinom(n, 1, 0.3); W[sample(1:n, n/3)] = 2 dat <- as.data.frame(W) dat\$X <- sample( c("low", "medium", "high"), size = n, replace = TRUE) dat\$Y <- ifelse(dat\$X == "low", rbinom(n, 1, plogis(W + 0.5)), ifelse(dat\$X == "medium", rbinom(n, 1, plogis(W + 0.2)), rbinom(n, 1, plogis(W - 0.4)) )) dat\$X <- as.factor(dat\$X) result <- nominalRR(Y ~ X + W, basecov = "low", comparecov = "high", data = dat, boot = TRUE, n.boot = 200) ```

youjin1207/logisticRR documentation built on March 16, 2020, 3:37 a.m.