Partial Verification Bias Correction Using Scaled Inverse Probability Resampling for Binary Diagnostic Tests

Abstract Diagnostic accuracy studies are crucial for evaluating new tests before their clinical application. These tests are compared against their respective gold standard tests, and accuracy measures such as sensitivity (Sn) and specificity (Sp) are often calculated. However, these studies frequently suffer from partial verification bias (PVB) due to selective verification of patients. PVB eventually leads to biased accuracy estimates in such studies. Among methods developed for PVB correction under the missing at random assumption for binary diagnostic tests, a bootstrap-based method known as the inverse probability bootstrap (IPB) was proposed. Despite showing low bias for estimating Sn and Sp, the IPB method exhibited higher standard errors than other PVB correction methods. This paper introduces two new methods: scaled inverse probability weighted resampling (SIPW) and scaled inverse probability weighted balanced resampling (SIPW-B), which build upon the IPB approach. Through simulations and clinical data, SIPW and SIPW-B were compared against IPB and other methods. The results demonstrated that the new methods outperformed IPB by showing lower bias and standard errors in Sn and Sp estimation. Specifically, SIPW-B outperformed IPB in Sn estimation, while SIPW performed better in Sp estimation, particularly when disease prevalence is low. These methods offer advantages such as complete data restoration and calculations independent of disease prevalence. Although computationally demanding, this limitation becomes less significant with the increasing power of modern computing resources. Competing Interest Statement The authors have declared no competing interest. Funding Statement Yes Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Footnotes - Unintentional duplicated texts were removed - Tables 1-2 expanded Data Availability The data and code can be found in https://github.com/wnarifin/sipw_in_pvb For simulated data, data generation method is described for replication of the simulation.