multibias

R Package for Multi-Bias Analysis in Causal Inference

https://github.com/pcbrendel/multibias

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R Package for Multi-Bias Analysis in Causal Inference

README.Rmd

---
output: github_document
---



```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# multibias 


[![R-CMD-check](https://github.com/pcbrendel/multibias/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/pcbrendel/multibias/actions/workflows/R-CMD-check.yaml)
[![cranlogs](https://cranlogs.r-pkg.org/badges/multibias)](https://cran.r-project.org/package=multibias)


## Overview

The multibias package is used to adjust for multiple biases in causal inference when working with observational data. Bias here refers to the case when the associational estimate of effect does not equal the causal estimate of effect:

$$(P(Y=1|X=1,C=0) / P(Y=1|X=0,C=0)) \neq (P(Y^{X=1}=1) / P(Y^{X=0}=1))$$

The `multibias_adjust()` function outputs odds ratio estimates adjusted for any combination of: uncontrolled confounding (**uc**), exposure misclassification (**em**), outcome misclassification (**om**), and selection bias (**sel**).

The package also includes several dataframes that are useful for validating the bias adjustment methods. Each dataframe contains different combinations of bias as identified by the same prefixing system. For each bias combination, there is a dataframe with incomplete information (as would be encountered in the real world) (e.g., `df_uc`) and a dataframe with complete information that was used to derive the biased data (e.g., `df_uc_source`).

## Installation

``` r
# install from CRAN
install.packages("multibias")

# install from github using devtools
# library("devtools")
devtools::install_github("pcbrendel/multibias")
```

## Getting started

1. Represent the observed causal data as a `data_observed` object. Here you
provide the data, specify the key variables, and list the biases present in
the data. See list below for the different bias combinations that multibias
can handle.
2. Obtain one of the two sources for bias adjustment:
   1. Bias parameters - via the `bias_params` object. Values for these
   parameters could come from the literature, validation data, or expert
   opinion. Each parameter can be represented as a single value or as a
   probability distribution. See the `bias_params` documentation for the
   full bias models.
   2. Validation dataframe - via the `data_validation` object. The purpose of
   validation data is to use an external data source to transport the necessary
   causal relationships that are missing in the observed data.
3. Run `multibias_adjust()` using the above inputs to obtain the bias-adjusted
exposure-outcome odds ratio and confidence interval.
4. Visualize a Forest Plot of the observed effect estimate against various
bias-adjusted estimates via `multibias_plot()`.

### Possible bias adjustments

**Single Bias**

* exposure misclassification
* outcome misclassification
* selection bias
* uncontrolled confounding

**Multiple Biases**

* exposure misclassification & selection bias
* exposure misclassification & outcome misclassification
* outcome misclassification & selection bias
* uncontrolled confounding & exposure misclassificaiton
* uncontrolled confounding & outcome misclassification
* uncontrolled confounding & selection bias
* uncontrolled confounding, exposure misclassification, & selection bias
* uncontrolled confounding, outcome misclassification, & selection bias

## Resources

* Brendel PB, Torres AZ, Arah OA, Simultaneous adjustment of uncontrolled confounding, selection bias and misclassification in multiple-bias modelling, *International Journal of Epidemiology*, Volume 52, Issue 4, Pages 1220–1230. [https://doi.org/10.1093/ije/dyad001](https://doi.org/10.1093/ije/dyad001)
* [Applying Quantitative Bias Analysis to Epidemiologic Data](https://link.springer.com/book/10.1007/978-0-387-87959-8)

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