Development of the multivariate administrative data cystectomy model and its impact on misclassification bias
Abstract Background 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. Methods We identified every primary c...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2024-03-01
|
| Series: | BMC Medical Research Methodology |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12874-024-02199-1 |
| _version_ | 1797247238145048576 |
|---|---|
| author | James Ross Luke T. Lavallee Duane Hickling Carl van Walraven |
| author_facet | James Ross Luke T. Lavallee Duane Hickling Carl van Walraven |
| author_sort | James Ross |
| collection | DOAJ |
| description | Abstract Background 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. Methods We identified every primary cystectomy-diversion type at a single hospital 2009–2019. 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. Results 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 = 12.75; p < .0001) and continent cystectomy (F = 11.25; p < .0001). Conclusions 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. |
| first_indexed | 2024-04-24T19:55:31Z |
| format | Article |
| id | doaj.art-f516b773283b41549a4a09b3ea58bf30 |
| institution | Directory Open Access Journal |
| issn | 1471-2288 |
| language | English |
| last_indexed | 2024-04-24T19:55:31Z |
| publishDate | 2024-03-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Research Methodology |
| spelling | doaj.art-f516b773283b41549a4a09b3ea58bf302024-03-24T12:24:30ZengBMCBMC Medical Research Methodology1471-22882024-03-0124111210.1186/s12874-024-02199-1Development of the multivariate administrative data cystectomy model and its impact on misclassification biasJames Ross0Luke T. Lavallee1Duane Hickling2Carl van Walraven3Department of Surgery, University of OttawaDepartment of Surgery, University of OttawaDepartment of Surgery, University of OttawaDepartment of Medicine / Department of Epidemiology & Community Medicine, University of OttawaAbstract Background 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. Methods We identified every primary cystectomy-diversion type at a single hospital 2009–2019. 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. Results 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 = 12.75; p < .0001) and continent cystectomy (F = 11.25; p < .0001). Conclusions 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.https://doi.org/10.1186/s12874-024-02199-1CystectomyUrinary diversionPredictive modelMisclassification biasBootstrap imputationAdministrative data |
| spellingShingle | James Ross Luke T. Lavallee Duane Hickling Carl van Walraven Development of the multivariate administrative data cystectomy model and its impact on misclassification bias BMC Medical Research Methodology Cystectomy Urinary diversion Predictive model Misclassification bias Bootstrap imputation Administrative data |
| title | Development of the multivariate administrative data cystectomy model and its impact on misclassification bias |
| title_full | Development of the multivariate administrative data cystectomy model and its impact on misclassification bias |
| title_fullStr | Development of the multivariate administrative data cystectomy model and its impact on misclassification bias |
| title_full_unstemmed | Development of the multivariate administrative data cystectomy model and its impact on misclassification bias |
| title_short | Development of the multivariate administrative data cystectomy model and its impact on misclassification bias |
| title_sort | development of the multivariate administrative data cystectomy model and its impact on misclassification bias |
| topic | Cystectomy Urinary diversion Predictive model Misclassification bias Bootstrap imputation Administrative data |
| url | https://doi.org/10.1186/s12874-024-02199-1 |
| work_keys_str_mv | AT jamesross developmentofthemultivariateadministrativedatacystectomymodelanditsimpactonmisclassificationbias AT luketlavallee developmentofthemultivariateadministrativedatacystectomymodelanditsimpactonmisclassificationbias AT duanehickling developmentofthemultivariateadministrativedatacystectomymodelanditsimpactonmisclassificationbias AT carlvanwalraven developmentofthemultivariateadministrativedatacystectomymodelanditsimpactonmisclassificationbias |