Literature DB >> 30515908

Comparing external and internal validation methods in correcting outcome misclassification bias in logistic regression: A simulation study and application to the case of postsurgical venous thromboembolism following total hip and knee arthroplasty.

Jiayi Ni1, Kaberi Dasgupta1,2, Suzan R Kahn2,3, Denis Talbot4,5, Geneviève Lefebvre6, Lisa M Lix7, Greg Berry8, Mark Burman8, Ronald Dimentberg9, Yves Laflamme10, Alain Cirkovic11, Elham Rahme1,2.   

Abstract

PURPOSE: We assessed the validity of postsurgery venous thromboembolism (VTE) diagnoses identified from administrative databases and compared Bayesian and multiple imputation (MI) approaches in correcting for outcome misclassification in logistic regression models.
METHODS: Sensitivity and specificity of postsurgery VTE among patients undergoing total hip or knee replacement (THR/TKR) were assessed against chart review in six Montreal hospitals in 2009 to 2010. Administrative data on all THR/TKR Quebec patients in 2009 to 2010 were obtained. The performance of Bayesian external, Bayesian internal, and MI approaches to correct the odds ratio (OR) of postsurgery VTE in tertiary versus community hospitals was assessed using simulations. Bayesian external approach used prior information from external sources, while Bayesian internal and MI approaches used chart review.
RESULTS: In total, 17 319 patients were included, 2136 in participating hospitals, among whom 75 had VTE in administrative data versus 81 in chart review. VTE sensitivity was 0.59 (95% confidence interval, 0.48-0.69) and specificity was 0.99 (0.98-0.99), overall. The adjusted OR of VTE in tertiary versus community hospitals was 1.35 (1.12-1.64) using administrative data, 1.45 (0.97-2.19) when MI was used for misclassification correction, and 1.53 (0.83-2.87) and 1.57 (0.39-5.24) when Bayesian internal and external approaches were used, respectively. In simulations, all three approaches reduced the OR bias and had appropriate coverage for both nondifferential and differential misclassification.
CONCLUSION: VTE identified from administrative data had low sensitivity and high specificity. The Bayesian external approach was useful to reduce outcome misclassification bias in logistic regression; however, it required accurate specification of the misclassification properties and should be used with caution.
© 2018 John Wiley & Sons, Ltd.

Entities:  

Keywords:  Bayesian; external validation; misclassification; multiple imputation; odds ratio; pharmacoepidemiology; venous thromboembolism

Mesh:

Year:  2018        PMID: 30515908     DOI: 10.1002/pds.4693

Source DB:  PubMed          Journal:  Pharmacoepidemiol Drug Saf        ISSN: 1053-8569            Impact factor:   2.890


  3 in total

Review 1.  An Update on Venous Thromboembolism Rates and Prophylaxis in Hip and Knee Arthroplasty in 2020.

Authors:  Daniel C Santana; Ahmed K Emara; Melissa N Orr; Alison K Klika; Carlos A Higuera; Viktor E Krebs; Robert M Molloy; Nicolas S Piuzzi
Journal:  Medicina (Kaunas)       Date:  2020-08-19       Impact factor: 2.430

2.  Combining self-reported and objectively measured survey data to improve hypertension prevalence estimates: Portuguese experience.

Authors:  Irina Kislaya; Andreia Leite; Julian Perelman; Ausenda Machado; Ana Rita Torres; Hanna Tolonen; Baltazar Nunes
Journal:  Arch Public Health       Date:  2021-04-08

3.  Bias correction methods for test-negative designs in the presence of misclassification.

Authors:  A Endo; S Funk; A J Kucharski
Journal:  Epidemiol Infect       Date:  2020-09-08       Impact factor: 2.451

  3 in total

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