# episensr: Basic Sensitivity Analysis of Epidemiologic Results

The R package episensr allows to do basic sensitivity analysis of epidemiological results as described in Applying Quantitative Bias Analysis to Epidemiological Data by Timothy L. Lash, Matthew P. Fox, and Aliza K. Fink (ISBN: 978-0-387-87960-4, bias.analysis).

## Example

We will use a case-control study by Stang et al. on the relation between mobile phone use and uveal melanoma. The observed odds ratio for the association between regular mobile phone use vs. no mobile phone use with uveal melanoma incidence is 0.71 [95% CI 0.51-0.97]. But there was a substantial difference in participation rates between cases and controls (94% vs 55%, respectively) and so selection bias could have an impact on the association estimate. The 2X2 table for this study is the following:

regular use no use
cases 136 107
controls 297 165

We use the function selection as shown below.

library(episensr)

selection(matrix(c(136, 107, 297, 165),
dimnames = list(c("UM+", "UM-"), c("Mobile+", "Mobile-")),
nrow = 2, byrow = TRUE),
bias_parms = c(.94, .85, .64, .25))
#> --Observed data--
#>          Outcome: UM+
#>        Comparing: Mobile+ vs. Mobile-
#>
#>     Mobile+ Mobile-
#> UM+     136     107
#> UM-     297     165
#>
#>                                        2.5%     97.5%
#> Observed Relative Risk: 0.7984287 0.6518303 0.9779975
#>    Observed Odds Ratio: 0.7061267 0.5143958 0.9693215
#> ---
#>
#> Selection Bias Corrected Relative Risk: 1.483780
#>    Selection Bias Corrected Odds Ratio: 1.634608


The 2X2 table is provided as a matrix and selection probabilities given with the argument bias_parms, a vector with the 4 probabilities (guided by the participation rates in cases and controls) in the following order: among cases exposed, among cases unexposed, among noncases exposed, and among noncases unexposed. The output shows the observed 2X2 table, the observed odds ratio (and relative risk) followed by the corrected ones.

## Installation

You can get the latest release from CRAN:

install.packages('episensr')


Or install the development version from GitHub with devtools package:

devtools::install_github('dhaine/episensr', ref = "develop")