episensr
Basic Sensitivity Analysis of Epidemiological Results
Basic sensitivity analysis of the observed relative risks adjusting for unmeasured confounding and misclassification of the exposure/outcome, or both. It follows the bias analysis methods and examples from the book by Lash T.L, Fox M.P, and Fink A.K. "Applying Quantitative Bias Analysis to Epidemiologic Data", ('Springer', 2009).
output: github_document
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). A similar function is available in Stata (episens).
License
This package is free and open source software, licensed under GPL2.
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: 1.634608The 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")News
episensr 0.9.3
- Update description of
parmsinconfounders,confounders.emm, andconfounders.poly - New function
confounders.array, sensitivity analysis for unmeasured confounders based on confounding imbalance among exposed and unexposed (Schneeweiss, 2006) - Deprecated
biasparameters removed from functionmultidimBias. Please usebias_parmsinstead. - New function
confounders.evalue, computing E-value to assess bias due to unmeasured confounder (VanderWeele and Ding, 2017) - New function
multiple.biasallowing to extract 2-by-2 table from anepisensrobject to feed another function (multiple bias analysis)
episensr 0.9.2
- Fix bug for distributions and computations of OR/RR in
probsens.conf - Update distributions in
probsens.irr.conf - Add example using
probsens.confin vignette
episensr 0.9.1
- Fix bug when using triangular distribution in
probsens.conffunction for prevalence of exposure among the non-exposed (as producing NaNs) (#1).
episensr 0.9.0
- Add
misclassification_covfor a misclassified covariate (confounder or effect modifier). - As such, this (wrongly) added option into
misclassificationin the previous version is now removed. - Add computation of confidence interval for odds ratio as per Chu et al. for exposure misclassification.
- Use
bias_parmsinstead ofbiasinmisclassificationfunction.
episensr 0.8.0
-
Fix bug when building 2-by-2 table.
-
Various formatting improvements in output of
confounders,confounders.emm,misclassificationandselectionfunctions. -
Standardize use of
bias_parms. -
Add vignette.
-
Selection bias factor now available in output of
selectionfunction. -
Add bootstrap option
episensr 0.7.2
- Fix 2-by-2 tables when variables are provided instead of a matrix.
episensr 0.7.1
- Fix R version dependency (R >= 3.2.0)
episensr 0.7.0
-
Harmonization of arguments across functions.
-
New distributions added to
probsensseries of functions: constant, logit-logistic, logit-normal, log-logistic, and log-normal. -
Probabilistic analysis of person-time data added with
probsens.irrfor exposure misclassification, andprobsens.irr.conffor unmeasured confounder. -
Sensitivity analysis to correct for selection bias caused by M bias with
mbiasfunction, including DAG plot and print function. -
Fix CI formatting.
-
NAMESPACE: add imports to
statsfunctions to avoid new R CMD CHECK warnings