README.Rmd

---
output: github_document
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

# SIR-SMR

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```{r message=FALSE, warning=FALSE}
rm(list=ls())
library(kableExtra)
library(magrittr)
library(dplyr)
library(tidyverse)
library(popEpi)
library(DiagrammeR)
library(biostat3)
library(Epi)
```

# Overview


Standardized Incidence Ratio (SIR) or Standardized Mortality Ratio (SMR) are common measures of event occurrence in epidemiology. 
They are used to compare the occurrence of an event in a cohort to that observed in a given population called reference population.  

SIR and SMR help the investigator to have a global idea on the occurrence of the event of interest in the followed population (the cohort).

They are an indirect method of adjustment for a confounding factors (age, sex, calendar year etc.) that describes in numerical terms how the cohort average experience of the event during the follow-up compared with that of the reference population as a whole.

In this post I will show three ways to estimate a SIR / SMR by indirect standardization from your cohort using R.
For a complete view of the project please visite : [https://rpubs.com/antoine-kossi/sir-analysis-with-r](https://rpubs.com/antoine-kossi/sir-analysis-with-r)

To make a contribution, feel free to clone the rip and to share it.

To report a comment you can email me [antoine.kossi@hotmail.com](antoine.kossi@hotmail.com) or to create an issue on github.