Improved Correction of Misclassification Bias With Bootstrap Imputation

Walraven, Carl van

doi:10.1097/mlr.0000000000000787

Cited by 13 publications

(9 citation statements)

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“…Ideally, this "reference" ADA should return a probability of pneumonia instead of the dichotomous outcome (pneumonia present/not present) as this would also allow for the use of statistical methods like bootstrap imputation to accommodate for misclassification bias in future studies using such ADA. [31][32][33] Clearly, the development of such ADA would be logistically challenging and consume large amounts of time and resources. An alternative and more practical approach is that all pneumonia ADA studies report, at a minimum, the details of its ADA in enough detail that it can be replicated, disclose whether a reference standard was used, describe the details of the said reference standard, and report the pneumonia prevalence and ADA accuracy measured in a representative sample of their study population.…”

Section: Discussionmentioning

confidence: 99%

Accuracy of Administrative Database Algorithms for Hospitalized Pneumonia in Adults: a Systematic Review

Corrales–Medina

Walraven

2021

J GEN INTERN MED

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BACKGROUND: Administrative data algorithms (ADAs) to identify pneumonia cases are commonly used in the analysis of pneumonia burden, trends, etiology, processes of care, outcomes, health care utilization, cost, and response to preventative and therapeutic interventions. However, without a good understanding of the validity of ADAs for pneumonia case identification, an adequate appreciation of this literature is difficult. We systematically reviewed the quality and accuracy of published ADAs to identify adult hospitalized pneumonia cases. METHODS: We reviewed the Medline, EMBase, and Cochrane Central databases through May 2020. All studies describing ADAs for adult hospitalized pneumonia and at least one accuracy statistic were included. Investigators independently extracted information about the sampling frame, reference standard, ADA composition, and ADA accuracy. RESULTS: Thirteen studies involving 24 ADAs were analyzed. Compliance with a 38-item study-quality assessment tool ranged from 17 to 29 (median, 23; interquartile range [IQR], 20 to 25). Study setting, design, and ADA composition varied extensively. Inclusion criteria of most studies selected for high-risk populations and/or increased pneumonia likelihood. Reference standards with explicit criteria (clinical, laboratorial, and/or radiographic) were used in only 4 ADAs. Only 2 ADAs were validated (one internally and one externally). ADA positive predictive values ranged from 35.0 to 96.5% (median, 84.8%; IQR, 65.3 to 89.1%). However, these values are exaggerated for an unselected patient population because pneumonia prevalences in the study cohorts were very high (median, 66%; IQR, 46 to 86%). ADA sensitivities ranged from 31.3 to 97.8% (median, 65.1%; IQR 52.5-72.4). DISCUSSION: ADAs for identification of adult pneumonia hospitalizations are highly heterogeneous, poorly validated, and at risk for misclassification bias. Greater standardization in reporting ADA accuracy is required in studies using pneumonia ADA for case identification so that results can be properly interpreted.

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Section: Discussionmentioning

confidence: 99%

Accuracy of Administrative Database Algorithms for Hospitalized Pneumonia in Adults: a Systematic Review

Corrales–Medina

Walraven

2021

J GEN INTERN MED

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“…This seemingly paradoxical result – a very accurate case-probability model returning misclassified disease status when a probability cutpoint is used – has been illustrated in other studies. [ 18 20 ] These results highlight the need to use analytical methods, such as bootstrap imputation, that account for uncertainty of case ascertainment when using health administrative data. When case probability estimates from our AF model (Table 3 ) were applied using bootstrap imputation methods, prevalence estimates and measures of association with key variables were very close to true values (Fig.…”

Section: Discussionmentioning

confidence: 99%

“…The latter approach used bootstrap imputation methods. [ 18 19 20 ] Bootstrap imputation started by creating 1000 random bootstrap samples (with replacement) of the study cohort, each with a sample size identical to the original cohort. For each patient within each bootstrap sample, a uniformly distributed number between 0 and 1 was randomly selected; AF was imputed to be present if the random number was below the expected probability of AF for that patient (as determined from the AF model).…”

Section: Methodsmentioning

confidence: 99%

Minimizing misclassification bias with a model to identify acetabular fractures using health administrative data

2021

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Acetabular fractures (AFs) are relatively uncommon thereby limiting their study. Analyses using population-based health administrative data can return erroneous results if case identification is inaccurate (‘misclassification bias’). This study measured the impact of an AF prediction model based exclusively on administrative data upon misclassification bias. We applied text analytical methods to all radiology reports over 11 years at a large, tertiary care teaching hospital to identify all AFs. Using clinically-based variable selection techniques, a logistic regression model was created. We identified 728 AFs in 438,098 hospitalizations (15.1 cases/10,000 admissions). The International Classification of Disease, 10 th revision (ICD-10) code for AF (S32.4) missed almost half of cases and misclassified more than a quarter (sensitivity 51.2%, positive predictive value 73.0%). The AF model was very accurate (optimism adjusted R 2 0.618, c-statistic 0.988, calibration slope 1.06). When model-based expected probabilities were used to determine AF status using bootstrap imputation methods, misclassification bias for AF prevalence and its association with other variables was much lower than with International Classification of Disease, 10 th revision S32.4 (median [range] relative difference 1.0% [0%–9.0%] vs 18.0% [5.4%–75.0%]). Lone administrative database diagnostic codes are inadequate to create AF cohorts. The probability of AF can be accurately determined using health administrative data. This probability can be used in bootstrap imputation methods to importantly reduce misclassification bias.

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“…Several methods of assigning disease status based on a prediction model are available [13]. Some studies suggest that a "bootstrap imputation" method may produce the least biased estimates [18].…”

Section: Statistical Modellingmentioning

confidence: 99%

Estimating Disease Prevalence in Administrative Data

Kopeć

2022

CIM

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Purpose: Disease prevalence estimates from population-based administrative databases are often biased due to measurement (misclassification) errors. The purpose of this article is to review the methodology for estimating disease prevalence in administrative data, with a focus on bias correction. Source: Several approaches to bias correction in administrative data were reviewed and application of these methods was demonstrated using an example from the literature: physician claims and hospitalization data were employed to estimate diabetes prevalence in Ontario, Canada. Findings: Misclassification bias in prevalence estimates from administrative data can be reduced by developing and selecting an optimal algorithm for case identification, applying a bias correction formula, or using statistical modelling. An algorithm for which sensitivity equals positive predictive value provides an unbiased estimate of prevalence. Bias reduction methods generally require information about the measurement properties of the algorithm, such as sensitivity, specificity, or predictive value. These properties depend on disease type, prevalence, algorithm definition (including the observation window), and may vary by population and time. Prevalence estimates can be improved by applying multivariable disease prediction models. Conclusion: Frequency of a positive case identification algorithm in administrative data is generally not equivalent to disease prevalence. Although prevalence estimates can be corrected for bias using known measurement properties of the algorithm, these properties may be difficult to estimate accurately; therefore, disease prevalence estimates based on administrative data must be treated with caution.

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Improved Correction of Misclassification Bias With Bootstrap Imputation

Abstract: QBA can produce invalid results and increase misclassification bias. BI avoids invalid results and can importantly decrease misclassification bias when accurate disease probability estimates are used.

Cited by 13 publications

References 13 publications

Accuracy of Administrative Database Algorithms for Hospitalized Pneumonia in Adults: a Systematic Review

Accuracy of Administrative Database Algorithms for Hospitalized Pneumonia in Adults: a Systematic Review

Minimizing misclassification bias with a model to identify acetabular fractures using health administrative data

Estimating Disease Prevalence in Administrative Data

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