Abstract：
Background:In administrative database research, misclassification bias can result from diagnostic codes that imperfectly represent the condition being studied. It is unclear how to correct for this bias. Methods:Severe renal failure and Colles' fracture status were determined in two distinct cohorts using gold standard methods. True disease prevalence and disease association with other covariables were measured and compared with results when disease status was determined using diagnostic codes. Differences ('misclassification bias') were then adjusted for using two methods: quantitative bias analysis (QBA) with bias parameters (code sensitivity and specificity) of varying accuracy; and disease status imputation using bootstrap methods and disease probability models. Results:Prevalences of severe renal failure (n = 50 074) and Colles' fracture (n = 5680) were 7.5% and 37.0%, respectively. Compared with true values, important bias resulted when diagnostic codes were used to measure disease prevalence and disease-covariable associations. QBA increased bias when population-based (vs strata-specific) bias parameters were used. QBA's ability to account for misclassification bias was most dependent upon deviations in code specificity. Bootstrap imputation accounted for misclassification bias, but this depended on disease model calibration. Conclusions:Extensive bias can result from using inaccurate diagnostic codes to determine disease status. This bias can be addressed with QBA using accurate bias parameter measures, or by bootstrap imputation using well-calibrated disease prediction models.
journal_name
Int J Epidemioljournal_title
International journal of epidemiologyauthors
Walraven CVdoi
10.1093/ije/dyx253subject
Has Abstractpub_date
2018-04-01 00:00:00pages
605-616issue
2eissn
0300-5771issn
1464-3685pii
4745802journal_volume
47pub_type
杂志文章abstract：:To develop a procedure for maximizing the discrimination of smoking status, the authors analysed parallel samples of thiocyanate and cotinine in serum, and carbon monoxide (CO) in expired air in a cohort of 145 male subjects aged 45-65 years. The sensitivity and specificity were 93% and 82%, 97% and 83%, and 98% and 1...
journal_title：International journal of epidemiology
pub_type： 杂志文章
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journal_title：International journal of epidemiology
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pub_type： 临床试验,杂志文章,随机对照试验
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