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 (e.g., \(`P(Y=1|X=1,C=0) / P(Y=1|X=0,C=0)`\)) does not equal the causal estimate of effect (e.g., \(`P(Y^{X=1}=1) / P(Y^{X=0}=1)`\)). The underlying methods are explained in the article:

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

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

Single bias adjustments:

Function | Adjusts for |
---|---|

`adjust_em()` |
exposure misclassification |

`adjust_om()` |
outcome misclassification |

`adjust_sel()` |
selection bias |

`adjust_uc()` |
uncontrolled confounding |

Multiple bias adjustments:

Function | Adjusts for |
---|---|

`adjust_em_sel()` |
exposure misclassification & selection bias |

`adjust_em_om` |
exposure misclassification & outcome misclassification |

`adjust_om_sel()` |
outcome misclassification & selection bias |

`adjust_uc_em()` |
uncontrolled confounding & exposure misclassificaiton |

`adjust_uc_om()` |
uncontrolled confounding & outcome misclassification |

`adjust_uc_sel()` |
uncontrolled confounding & selection bias |

`adjust_uc_em_sel()` |
uncontrolled confounding, exposure misclassification, & selection bias |

`adjust_uc_om_sel()` |
uncontrolled confounding, outcome misclassification, & selection bias |

The package also includes several dataframes that are useful for
demonstrating and validating the bias adjustment methods. Each dataframe
contains different combinations of bias as identified by the same
prefixing system (e.g., **uc** for uncontrolled
confounding). 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`

).

If you are new to bias analysis, check out Applying Quantitative Bias Analysis to Epidemiologic Data or visit my website. For examples, see the vignette.

```{r, eval = FALSE} # install from CRAN install.packages(“multibias”)

devtools::install_github(“pcbrendel/multibias”) ```

- Determine the desired biases to adjust for in your observational
data for a given exposure-outcome effect and identify the corresponding
`adjust`

function. - Obtain one of the two sources for bias adjustment:
- Bias parameters. These values 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
`adjust`

function documentation. - Validation dataframe. 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.

- Bias parameters. These values 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
- Run the
`adjust`

function after inputting:- The observed data as a
`data_observed`

object - The bias parameters or validation data as
`data_validation`

object - The level of the bias-adjusted effect estimate confidence interval

- The observed data as a
- The
`adjust`

function will output the bias-adjusted exposure-outcome odds ratio and confidence interval.