--- title: "Selection bias modeling using observed data. | Sutter Health" description: "To use inverse probability weighting and externally obtained bias parameters to perform internal adjustment of selection bias in studies lacking covariate data." url: "https://www.sutterhealth.org/research/publications/selection-bias-modeling-using-observed-data-1055312921" --- 1. [Home](https://www.sutterhealth.org/) / 2. [Research](https://www.sutterhealth.org/research) / 3. [Research publications](https://www.sutterhealth.org/research/publications) / 4. Selection bias modeling using observed data. # Selection bias modeling using observed data augmented with imputed record-level probabilities. Thompson CA & Arah OA. _Ann Epidemiol. pii: S1047-2797(14)00364-0. doi: 10.1016/j.annepidem.2014.07.014. \[Epub ahead of print\]_ 2014-08-12 ## Abstract PURPOSE: Selection bias is a form of systematic error that can be severe in compromised study designs such as case-control studies with inappropriate selection mechanisms or follow-up studies that suffer from extensive attrition. External adjustment for selection bias is commonly undertaken when such bias is suspected, but the methods used can be overly simplistic, if not unrealistic, and fail to allow for simultaneous adjustment of associations of the exposure and covariates with the outcome, when of interest. Internal adjustment for selection bias via inverse probability weighting allows bias parameters to vary with the levels of covariates but has only been formalized for longitudinal studies with covariate data on patients up until loss to follow-up. METHODS: We demonstrate the use of inverse probability weighting and externally obtained bias parameters to perform internal adjustment of selection bias in studies lacking covariate data on unobserved participants. RESULTS: The "true" or selection-adjusted odds ratio for the association between exposure and outcome was successfully obtained by analyzing only data on those in the selected stratum (i.e., responders) weighted by the inverse probability of their being selected as function of their observed covariate data. CONCLUSIONS: This internal adjustment technique using user-supplied bias parameters and inverse probability weighting for selection bias can be applied to any type of observational study. [PubMed abstract](http://www.ncbi.nlm.nih.gov/pubmed/25175700) ## Research topics - [Biostatistics](https://www.sutterhealth.org/research-search-results?query=%22%5C%22Biostatistics%5C%22%22) - [Comparative Effectiveness](https://www.sutterhealth.org/research-search-results?query=%22%5C%22Comparative+Effectiveness%5C%22%22) ## Related publications [View all publications](https://www.sutterhealth.org/research-search-results?verticalKey=research_publication) - Publication ### [Minimran: a robust online system to implement minimization in randomized clinical trials.](https://www.sutterhealth.org/research/publications/minimran-online-do-minimization-rct-1055312971) Xiao L, Ma J. JSM Proc Biopharm Section. Alexandria VA: American Statistical Association 2014; 1094-1100. 2014 Aug 06 - Publication ### [Effectiveness of a group-based lifestyle change program versus usual care: an electronic health record, propensity score-matched cohort study](https://www.sutterhealth.org/research/publications/effectiveness-of-a-group-based-lifestyle-change-program-1055312802) Robert J. Romanelli, Hsiao-Ching Huang, Sylvia Sudat, Alice R. Pressman, Kristen M. J. Azar Am J Prev Med. 2020 Dec;59(6):850-859. doi: 10.1016/j.amepre.2020.07.002. Epub 2020 Nov 18 - Publication ### [Bar charts enhance Bland-Altman plots when value ranges are limited.](https://www.sutterhealth.org/research/publications/bar-charts-enhance-bland-altman-plots-1055312551) Smith MW, Ma J, Stafford RS. J Clin Epidemiol. 63(2):180-4. doi: 10.1016/j.jclinepi.2009.06.001. Epub 2009 Aug 27. 2010 Feb 01 - Publication ### [Comparison of dynamic block randomization and minimization in randomized trials: a simulation study.](https://www.sutterhealth.org/research/publications/block-randomization-minimization-randomized-trials-1055312663) Xiao L, Lavori PW, Wilson SR, Ma J. Clin Trials. 8(1):59-69. doi: 10.1177/1740774510391683. 2011 Feb 01 - Publication ### [Algorithm for balancing both continuous and categorical covariates in randomized controlled trials](https://www.sutterhealth.org/research/publications/algorithm-balancing-covariates-rcts-1055312558) Xiao L, Yank V, Ma J. Comput Methods Programs Biomed. 108(3):1185-90. doi: 10.1016/j.cmpb.2012.06.001. Epub 2012 Jun 22. 2012 Dec 01 [View all publications](https://www.sutterhealth.org/research-search-results?verticalKey=research_publication) ```json {"@context":"https://schema.org","@type":"MedicalOrganization","name":"Sutter Health","@id":"https://www.sutterhealth.org/#medicalorganization","url":"https://www.sutterhealth.org","logo":"https://www.sutterhealth.org/static/images/sutter-health-logo.svg","address":{"@type":"PostalAddress","streetAddress":"2200 River Plaza Drive","addressLocality":"Sacramento","addressRegion":"CA","postalCode":"95833","addressCountry":"US"},"areaServed":{"@type":"AdministrativeArea","name":"California"},"sameAs":["https://www.facebook.com/SutterHealth","https://www.instagram.com/sutterhealth/","https://x.com/SutterHealth","https://www.youtube.com/sutterhealth","https://www.linkedin.com/company/sutter-health","https://en.wikipedia.org/wiki/Sutter_Health"]} ``` ```json {"@context":"https://schema.org","@type":"BreadcrumbList","@id":"https://www.sutterhealth.org/research/publications/selection-bias-modeling-using-observed-data-1055312921#breadcrumblist","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://www.sutterhealth.org/"},{"@type":"ListItem","position":2,"name":"Research","item":"https://www.sutterhealth.org/research"},{"@type":"ListItem","position":3,"name":"Research publications","item":"https://www.sutterhealth.org/research/publications"},{"@type":"ListItem","position":4,"name":"Selection bias modeling using observed data."}]} ``` ```json {"@context":"https://schema.org","@type":"MedicalScholarlyArticle","@id":"https://www.sutterhealth.org/research/publications/selection-bias-modeling-using-observed-data-1055312921#medicalscholarlyarticle","name":"Selection bias modeling using observed data augmented with imputed record-level probabilities.","headline":"Selection bias modeling using observed data.","abstract":"PURPOSE: Selection bias is a form of systematic error that can be severe in compromised study designs such as case-control studies with inappropriate selection mechanisms or follow-up studies that suffer from extensive attrition. External adjustment for selection bias is commonly undertaken when such bias is suspected, but the methods used can be overly simplistic, if not unrealistic, and fail to allow for simultaneous adjustment of associations of the exposure and covariates with the outcome, when of interest. Internal adjustment for selection bias via inverse probability weighting allows bias parameters to vary with the levels of covariates but has only been formalized for longitudinal studies with covariate data on patients up until loss to follow-up. METHODS: We demonstrate the use of inverse probability weighting and externally obtained bias parameters to perform internal adjustment of selection bias in studies lacking covariate data on unobserved participants. RESULTS: The \"true\" or selection-adjusted odds ratio for the association between exposure and outcome was successfully obtained by analyzing only data on those in the selected stratum (i.e., responders) weighted by the inverse probability of their being selected as function of their observed covariate data. 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