Data validation in multinational observational studies with error-prone data: applying an optimal validation sampling strategy in a study of Kaposi sarcoma and HIV

Abstract

Abstract A large multi-center study was conducted to investigate factors associated with Kaposi sarcoma (KS) among people with HIV (PWH). The study used routinely collected (eg, electronic health record) data from 257 429 PWH in Latin America and East Africa. Although the routinely collected data contain rich information on key clinical and demographic variables, previous chart reviews of these datasets have raised some concerns about their accuracy. While validating all data is impractical, a subset of participants’ records can be validated and then combined with the error-prone data using techniques developed for measurement error and missing data problems to obtain consistent estimators and valid inference. A key step is thus choosing which records to validate, particularly with a rare outcome such as KS. Validated records should be informative for the research questions, while keeping the selection probabilistic to have results generalizable for the study population. We describe an optimal multi-wave validation procedure to internally validate 1000 patient records to maximize precision of parameters of interest and better understand the incidence and prevalence of KS among PWH. We also describe challenges encountered with implementing the optimal validation design in a complex setting with a rare outcome and multiple study sites across two continents.