{"id":32199388,"url":"https://github.com/pcbrendel/multibias","last_synced_at":"2025-10-22T03:14:45.194Z","repository":{"id":189282727,"uuid":"169159248","full_name":"pcbrendel/multibias","owner":"pcbrendel","description":"R Package for Multi-Bias Analysis in Causal Inference","archived":false,"fork":false,"pushed_at":"2025-06-17T02:37:18.000Z","size":10343,"stargazers_count":2,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"master","last_synced_at":"2025-09-08T15:26:59.025Z","etag":null,"topics":["causal-inference","causal-models","epidemiology","r"],"latest_commit_sha":null,"homepage":"http://www.paulbrendel.com/multibias/","language":"R","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/pcbrendel.png","metadata":{"files":{"readme":"README.Rmd","changelog":"NEWS.md","contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2019-02-04T22:23:23.000Z","updated_at":"2025-06-15T18:12:22.000Z","dependencies_parsed_at":"2023-08-19T06:35:31.799Z","dependency_job_id":"413e9dd8-cdc3-432e-addb-4bee80f62c33","html_url":"https://github.com/pcbrendel/multibias","commit_stats":null,"previous_names":["pcbrendel/multibias"],"tags_count":18,"template":false,"template_full_name":null,"purl":"pkg:github/pcbrendel/multibias","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pcbrendel%2Fmultibias","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pcbrendel%2Fmultibias/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pcbrendel%2Fmultibias/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pcbrendel%2Fmultibias/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/pcbrendel","download_url":"https://codeload.github.com/pcbrendel/multibias/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pcbrendel%2Fmultibias/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":280371872,"owners_count":26319521,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-10-22T02:00:06.515Z","response_time":63,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["causal-inference","causal-models","epidemiology","r"],"created_at":"2025-10-22T03:14:43.775Z","updated_at":"2025-10-22T03:14:45.185Z","avatar_url":"https://github.com/pcbrendel.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"---\noutput: github_document\n---\n\n\u003c!-- README.md is generated from README.Rmd. Please edit that file --\u003e\n\n```{r, include = FALSE}\nknitr::opts_chunk$set(\n  collapse = TRUE,\n  comment = \"#\u003e\",\n  fig.path = \"man/figures/README-\",\n  out.width = \"100%\"\n)\n```\n\n# multibias \u003cimg src=\"man/figures/logo.png\" align=\"right\" /\u003e\n\n\u003c!-- badges: start --\u003e\n[![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)\n[![cranlogs](https://cranlogs.r-pkg.org/badges/multibias)](https://cran.r-project.org/package=multibias)\n\u003c!-- badges: end --\u003e\n\n## Overview\n\nThe 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:\n\n$$(P(Y=1|X=1,C=0) / P(Y=1|X=0,C=0)) \\neq (P(Y^{X=1}=1) / P(Y^{X=0}=1))$$\n\nThe `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**).\n\nThe 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`).\n\n## Installation\n\n``` r\n# install from CRAN\ninstall.packages(\"multibias\")\n\n# install from github using devtools\n# library(\"devtools\")\ndevtools::install_github(\"pcbrendel/multibias\")\n```\n\n## Getting started\n\n1. Represent the observed causal data as a `data_observed` object. Here you\nprovide the data, specify the key variables, and list the biases present in\nthe data. See list below for the different bias combinations that multibias\ncan handle.\n2. Obtain one of the two sources for bias adjustment:\n   1. Bias parameters - via the `bias_params` object. Values for these\n   parameters could come from the literature, validation data, or expert\n   opinion. Each parameter can be represented as a single value or as a\n   probability distribution. See the `bias_params` documentation for the\n   full bias models.\n   2. Validation dataframe - via the `data_validation` object. The purpose of\n   validation data is to use an external data source to transport the necessary\n   causal relationships that are missing in the observed data.\n3. Run `multibias_adjust()` using the above inputs to obtain the bias-adjusted\nexposure-outcome odds ratio and confidence interval.\n4. Visualize a Forest Plot of the observed effect estimate against various\nbias-adjusted estimates via `multibias_plot()`.\n\n### Possible bias adjustments\n\n**Single Bias**\n\n* exposure misclassification\n* outcome misclassification\n* selection bias\n* uncontrolled confounding\n\n**Multiple Biases**\n\n* exposure misclassification \u0026 selection bias\n* exposure misclassification \u0026 outcome misclassification\n* outcome misclassification \u0026 selection bias\n* uncontrolled confounding \u0026 exposure misclassificaiton\n* uncontrolled confounding \u0026 outcome misclassification\n* uncontrolled confounding \u0026 selection bias\n* uncontrolled confounding, exposure misclassification, \u0026 selection bias\n* uncontrolled confounding, outcome misclassification, \u0026 selection bias\n\n## Resources\n\n* 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)\n* [Applying Quantitative Bias Analysis to Epidemiologic Data](https://link.springer.com/book/10.1007/978-0-387-87959-8)","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpcbrendel%2Fmultibias","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpcbrendel%2Fmultibias","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpcbrendel%2Fmultibias/lists"}