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| http://dx.doi.org/10.2105/AJPH.2018.304440 | DOI Listing |
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The necessity of validity diagnostics when drawing causal inferences from observational data: lessons from a multi-database evaluation of the risk of non-infectious uveitis among patients exposed to Remicade.
BMC Med Res Methodol
December 2024
Janssen Research & Development LLC, Global Epidemiology Organization, Raritan, NJ, USA.
Background: Autoimmune disorders have primary manifestations such as joint pain and bowel inflammation but can also have secondary manifestations such as non-infectious uveitis (NIU). A regulatory health authority raised concerns after receiving spontaneous reports for NIU following exposure to Remicade, a biologic therapy with multiple indications for which alternative therapies are available. In assessment of this clinical question, we applied validity diagnostics to support observational data causal inferences.
View Article and Find Full Text PDFBayesian Latent Class Models for Evaluating the Validity of Claim-based Definitions of Disease Outcomes.
Ann Clin Epidemiol
October 2024
Center of Medical Statistics, Minato-Ku, Tokyo, Japan.
Background: Large electronic databases have been widely used in recent years; however, they can be susceptible to bias due to incomplete information. To address this, validation studies have been conducted to assess the accuracy of disease diagnoses defined in databases. However, such studies may be constrained by potential misclassification in references and the interdependence between diagnoses from the same data source.
View Article and Find Full Text PDFThe effect of misclassification on sample size for one and two-sample tests with binary endpoints.
J Biopharm Stat
December 2024
Department of Biostatistics, University of Veterinary Medicine Budapest, Budapest, Hungary.
In recent years, an increasing number of publications on the analysis of binary data have applied methods that take misclassification into account. However, potential misclassification is often ignored in study design due to the lack of sample size formulas or software. This may lead to a considerable loss of power in studies that only account for misclassification at the analysis stage.
View Article and Find Full Text PDFMisclassification of Opioid-involvement in Drug-related Overdose Deaths in the United States: A Scoping Review.
Ann Epidemiol
December 2024
Department of Epidemiology, Columbia University Mailman School of Public Health, 722 W 168(th) St, New York, NY, 10032 United States. Electronic address:
Purpose: Most drug-related deaths in the United States (US) in 2022 involved opioids. However, methodological challenges in overdose surveillance may contribute to underestimation of opioid involvement in the overdose crisis. This scoping review aimed to synthesize existing literature to examine the breadth and contributing sources of misclassification of opioid-related overdose deaths.
View Article and Find Full Text PDFRisks of grade reclassification among patients with Gleason grade group 1 prostate cancer and PI-RADS 5 findings on prostate MRI.
Urol Oncol
December 2024
Department of Urology, Yale School of Medicine, New Haven, CT; Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, CT; Yale Cancer Outcomes, Public Policy and Effectiveness Research Center, New Haven, CT. Electronic address:
Background And Objective: As most Prostate Imaging Reporting and Data System (PI-RADS) 5 lesions on MRI harbor Gleason grade (GG) group ≥2 disease on biopsy, optimal management of patients with imaging-biopsy discordance remains unclear. To estimate grade misclassification, we evaluated the incidence of Gleason upgrading among patients with GG1 disease in the setting of a PI-RADS 5 lesion.
Methods: We conducted a single-institution retrospective analysis to identify patients with GG1 prostate cancer on fusion biopsy with MRI demonstrating ≥1 PI-RADS 5 lesion.
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