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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))≠(P(YX=1=1)/P(YX=0=1))(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 adjust() family of functions output odds ratio estimates adjusted for any combination of: uncontrolled confounding (uc), exposure misclassification (em), outcome misclassification (om), and selection bias (sel). For example, adjust_uc_om_sel() simultaneously adjusts for uncontrolled confounding, outcome misclassificaiton, and selection bias.

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 

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

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

Getting started

  1. Determine the desired biases to adjust for in your observational data for a given exposure-outcome effect and identify the corresponding adjust() function (see tables below).
  2. Obtain one of the two sources for bias adjustment:
    1. 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.
    2. 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.
  3. Run the adjust() function after inputting:
    1. The observed data as a data_observed object
    2. The bias parameters or validation data as data_validation object
    3. The level of the bias-adjusted effect estimate confidence interval
  4. The adjust() function will output the bias-adjusted exposure-outcome odds ratio and confidence interval.

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

Resources