{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T04:13:26Z","timestamp":1772165606792,"version":"3.50.1"},"reference-count":15,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,3,21]],"date-time":"2024-03-21T00:00:00Z","timestamp":1710979200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,3,21]],"date-time":"2024-03-21T00:00:00Z","timestamp":1710979200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Res Methodol"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Misclassification bias (MB) is the deviation of measured from true values due to incorrect case assignment. This study compared MB when cystectomy status was determined using administrative database codes vs. predicted cystectomy probability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We identified every primary cystectomy-diversion type at a single hospital 2009\u20132019. We linked to claims data to measure true association of cystectomy with 30 patient and hospitalization factors. Associations were also measured when cystectomy status was assigned using billing codes and by cystectomy probability from multivariate logistic regression model with covariates from administrative data. MB was the difference between measured and true associations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      500 people underwent cystectomy (0.12% of 428 677 hospitalizations). Sensitivity and positive predictive values for cystectomy codes were 97.1% and 58.6% for incontinent diversions and 100.0% and 48.4% for continent diversions, respectively. The model accurately predicted cystectomy-incontinent diversion (c-statistic [C] 0.999, Integrated Calibration Index [ICI] 0.000) and cystectomy-continent diversion (C:1.000, ICI 0.000) probabilities. MB was significantly lower when model-based predictions was used to impute cystectomy-diversion type status using for both incontinent cystectomy (F\u2009=\u200912.75;\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u2009.0001) and continent cystectomy (F\u2009=\u200911.25;\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u2009.0001).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>A model using administrative data accurately returned the probability that cystectomy by diversion type occurred during a hospitalization. Using this model to impute cystectomy status minimized MB. Accuracy of administrative database research can be increased by using probabilistic imputation to determine case status instead of individual codes.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12874-024-02199-1","type":"journal-article","created":{"date-parts":[[2024,3,21]],"date-time":"2024-03-21T07:02:30Z","timestamp":1711004550000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Development of the multivariate administrative data cystectomy model and its impact on misclassification bias"],"prefix":"10.1186","volume":"24","author":[{"given":"James","family":"Ross","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luke T.","family":"Lavallee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Duane","family":"Hickling","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8390-0930","authenticated-orcid":false,"given":"Carl","family":"van Walraven","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,3,21]]},"reference":[{"key":"2199_CR1","unstructured":"Catalogue of Bias Collaboration, Spencer EA, Mahtani KR, Brassey J, Heneghan C. Misclassification bias. Badenoch D, Heneghan C, Nunan D, editors. 2018. Centre for Evidence-Based Medicine. 4\u201321\u20132022. Ref Type: Online Source. https:\/\/catalogofbias.org\/biases\/misclassification-bias\/."},{"issue":"10","key":"2199_CR2","doi-asserted-by":"publisher","first-page":"1054","DOI":"10.1016\/j.jclinepi.2011.01.001","volume":"64","author":"C van Walraven","year":"2011","unstructured":"van Walraven C, Bennett C, Forster AJ. Administrative database research infrequently uses validated diagnostic or procedural codes. J Clin Epidemiol. 2011;64(10):1054\u20139.","journal-title":"J Clin Epidemiol"},{"issue":"5","key":"2199_CR3","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1016\/j.urpr.2016.09.011","volume":"4","author":"H Tan","year":"2017","unstructured":"Tan H, Clarke R, Chamie K, et al. Development and Validation of an Automated Method to Identify Patients Undergoing Radical Cystectomy for Bladder Cancer Using Natural Language Processing. Urology Practice. 2017;4(5):365\u201372.","journal-title":"Urology Practice"},{"issue":"4","key":"2199_CR4","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1016\/j.urolonc.2021.09.013","volume":"40","author":"TD Lyon","year":"2022","unstructured":"Lyon TD, Faraj KS, Brennan E, Tyson MD, Boorjian SA, Spaulding AC. Defining radical cystectomy using the ICD-10 procedure coding system. Urologic Oncology: Seminars and Original Investigations. 2022;40(4):165.","journal-title":"Urologic Oncology: Seminars and Original Investigations"},{"issue":"52","key":"2199_CR5","doi-asserted-by":"publisher","first-page":"e28223","DOI":"10.1097\/MD.0000000000028223","volume":"100","author":"A Adamczyk","year":"2021","unstructured":"Adamczyk A, Grammatopoulos G, van Walraven C. Minimizing misclassification bias with a model to identify acetabular fractures using health administrative data: A cohort study. Medicine (Baltimore). 2021;100(52):e28223.","journal-title":"Medicine (Baltimore)"},{"key":"2199_CR6","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1016\/j.jclinepi.2017.01.007","volume":"84","author":"C van Walraven","year":"2017","unstructured":"van Walraven C. Bootstrap imputation with a disease probability model minimizes bias from misclassification due to administrative database codes. J Clin Epidemiol. 2017;84:114\u201320.","journal-title":"J Clin Epidemiol"},{"issue":"1","key":"2199_CR7","doi-asserted-by":"publisher","first-page":"6","DOI":"10.2202\/1557-4679.1285","volume":"7","author":"PC Austin","year":"2011","unstructured":"Austin PC, Laupacis A. A tutorial on methods to estimating clinically and policy-meaningful measures of treatment effects in prospective observational studies: a review. Int J Biostat. 2011;7(1):6.","journal-title":"Int J Biostat"},{"issue":"7","key":"2199_CR8","doi-asserted-by":"publisher","first-page":"e39","DOI":"10.1097\/MLR.0000000000000787","volume":"56","author":"C van Walraven","year":"2018","unstructured":"van Walraven C. Improved correction of misclassification bias with bootstrap imputation. Med Care. 2018;56(7):e39\u201345.","journal-title":"Med Care"},{"key":"2199_CR9","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.m441","volume":"368","author":"RD Riley","year":"2020","unstructured":"Riley RD, Ensor J, Snell KIE, et al. Calculating the sample size required for developing a clinical prediction model. Br Med J. 2020;368: m441.","journal-title":"Br Med J"},{"issue":"12","key":"2199_CR10","doi-asserted-by":"publisher","first-page":"3464","DOI":"10.1016\/j.csda.2005.07.015","volume":"50","author":"W Sauerbrei","year":"2006","unstructured":"Sauerbrei W, Meier-Hirmer C, Benner A, Royston P. Multivariable regression model building by using fractional polynomials: Description of SAS, STATA and R programs. Comput Stat Data Anal. 2006;50(12):3464\u201385.","journal-title":"Comput Stat Data Anal"},{"issue":"21","key":"2199_CR11","doi-asserted-by":"publisher","first-page":"4051","DOI":"10.1002\/sim.8281","volume":"38","author":"PC Austin","year":"2019","unstructured":"Austin PC, Steyerberg EW. The Integrated Calibration Index (ICI) and related metrics for quantifying the calibration of logistic regression models. Stat Med. 2019;38(21):4051\u201365.","journal-title":"Stat Med"},{"key":"2199_CR12","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1007\/978-3-030-16399-0_5","volume-title":"Overfitting and optimism in prediction models. Clinical Prediction Models: A practical appoach to development, validation, and updating","author":"EW Steyerberg","year":"2019","unstructured":"Steyerberg EW. Overfitting and optimism in prediction models. Clinical Prediction Models: A practical appoach to development, validation, and updating. 2nd ed. New York: Springer; 2019. p. 95\u2013112.","edition":"2"},{"key":"2199_CR13","unstructured":"Richards J, Brown A, Homan C. The data quality study of the Canadian discharge abstract database. Proceedings of Statistics Canada Symposium: Acheiving Data Qualtiy in a Statistical Agency - a methodological perspective 2001. https:\/\/www150.statcan.gc.ca\/n1\/en\/pub\/11-522-x\/2001001\/session16\/6282-eng.pdf?st=-F2zSsb-."},{"key":"2199_CR14","first-page":"15","volume-title":"Analyzing receiver operating characteristic curves with SAS","author":"GM Single","year":"2007","unstructured":"Single GM, Predictor C. Analyzing receiver operating characteristic curves with SAS. Cary, N.C.: SAS Institute Inc.; 2007. p. 15\u201336."},{"issue":"2","key":"2199_CR15","doi-asserted-by":"publisher","first-page":"605","DOI":"10.1093\/ije\/dyx253","volume":"47","author":"C van Walraven","year":"2018","unstructured":"van Walraven C. A comparison of methods to correct for misclassification bias from administrative database diagnostic codes. Int J Epidemiol. 2018;47(2):605\u201316.","journal-title":"Int J Epidemiol"}],"container-title":["BMC Medical Research Methodology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12874-024-02199-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12874-024-02199-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12874-024-02199-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,21]],"date-time":"2024-03-21T07:04:34Z","timestamp":1711004674000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedresmethodol.biomedcentral.com\/articles\/10.1186\/s12874-024-02199-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,21]]},"references-count":15,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["2199"],"URL":"https:\/\/doi.org\/10.1186\/s12874-024-02199-1","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-3673205\/v1","asserted-by":"object"}]},"ISSN":["1471-2288"],"issn-type":[{"value":"1471-2288","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,21]]},"assertion":[{"value":"27 November 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 March 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 March 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The study was approved by the Ottawa Health Science Network Research Ethics Board (File: OHRI REB 20220112-01H).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"73"}}