Conditional validation sampling for consistent risk estimation with binary outcome data subject to misclassification

A Capture-Recapture-based Ascertainment Probability Weighting Method for Effect Estimation With Under-ascertained Outcomes

Outcome under-ascertainment, characterized by the incomplete identification or reporting of cases, poses a substantial challenge in epidemiologic research. While capture-recapture methods can estimate unknown case numbers, their role in estimating exposure effects in observational studies is not well established. This paper presents an ascertainment probability weighting framework that integrates capture-recapture and propensity score weighting. We propose a nonparametric estimator of effects on binary outcomes that combines exposure propensity scores with data from two conditionally independent outcome measurements to simultaneously adjust for confounding and under-ascertainment. Demonstrating its practical application, we apply the method to estimate the relationship between health care work and coronavirus disease 2019 testing in a Swedish region. We find that ascertainment probability weighting greatly influences the estimated association compared to conventional inverse probability weighting, underscoring the importance of accounting for under-ascertainment in studies with limited outcome data coverage. We conclude with practical guidelines for the method's implementation, discussing its strengths, limitations, and suitable scenarios for application.

Core concepts in pharmacoepidemiology: Validation of health outcomes of interest within real-world healthcare databases

Real-world healthcare data, including administrative and electronic medical record databases, provide a rich source of data for the conduct of pharmacoepidemiologic studies but carry the potential for misclassification of health outcomes of interest (HOIs). Validation studies are important ways to quantify the degree of error associated with case-identifying algorithms for HOIs and are crucial for interpreting study findings within real-world data. This review provides a rationale, framework, and step-by-step approach to validating case-identifying algorithms for HOIs within healthcare databases. Key steps in validating a case-identifying algorithm within a healthcare database include: (1) selecting the appropriate health outcome; (2) determining the reference standard against which to validate the algorithm; (3) developing the algorithm using diagnosis codes, diagnostic tests or their results, procedures, drug therapies, patient-reported symptoms or diagnoses, or some combinations of these parameters; (4) selection of patients and sample sizes for validation; (5) collecting data to confirm the HOI; (6) confirming the HOI; and (7) assessing the algorithm's performance. Additional strategies for algorithm refinement and methods to correct for bias due to misclassification of outcomes are discussed. The review concludes by discussing factors affecting the transportability of case-identifying algorithms and the need for ongoing validation as data elements within healthcare databases, such as diagnosis codes, change over time or new variables, such as patient-generated health data, are included in these data sources.

Postmyocardial Infarction Statin Exposure and the Risk of Stroke with Weighting for Outcome Misclassification

BACKGROUND

Observational healthcare data can be used for drug safety and effectiveness research. The use of inverse probability of treatment weights (IPW) reduces measured confounding under the assumption of accurate measurement of the outcome variable; however, many datasets suffer from systematic outcome misclassification.

METHODS

We introduced a modification to IPW to correct for the presence of outcome misclassification. To demonstrate the utility of these modified weights in realistic settings, we investigated postmyocardial infarction statin use and the 1-year risk of stroke in the Clinical Practice Research Datalink.

RESULTS

We computed an IPW-adjusted odds ratio (OR = 0.67; 95% confidence interval (CI) = 0.48, 0.93). We employed a technique to modify IPW for the presence of outcome misclassification using linked hospital records for outcome validation (modified IPW adjusted OR = 0.77; 95% CI = 0.52, 1.15) and compared the results with a meta-analysis of randomized controlled trials (RCTs) (pooled OR = 0.80; 95% CI = 0.74, 0.87). Finally, we present simulation studies to investigate the impact of model selection on bias reduction and variability.

CONCLUSION

Ignoring outcome misclassification yielded biased estimates whereas the use of the modified IPW approach produced encouraging results when compared with the meta-analytic RCT findings.