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Yuta Nonomiya

Publications and source records attributed to Yuta Nonomiya.

2 recordsLinked to original sources

Small-study effects on the hierarchical summary ROC curve: latent accuracy and threshold trends in meta-analysis of diagnostic test accuracy

Diagnostic meta-analyses commonly assess small-study effects using the Deeks test, which tests for a study-size trend in the log diagnostic odds ratio. Under the hierarchical summary receiver operating characteristic (HSROC) model, this quantity reflects both latent accuracy and threshold unless the curve is symmetric. A threshold trend can therefore generate a log diagnostic odds ratio trend without a latent accuracy trend, or conceal an existing one. We develop a likelihood-ratio test of the latent accuracy trend under a common-shape binomial HSROC model. Its null hypothesis states that the summary curve is unchanged across study sizes. A log diagnostic odds ratio test using the same binomial fit provides a comparison with a different null hypothesis. In simulations, the proposed test remained near its nominal level under its null and had higher power than Deeks with a latent accuracy trend alone. In a colorectal cancer review of faecal immunochemical tests, it detected a negative latent accuracy trend obscured on the log diagnostic odds ratio scale by an opposing threshold contribution. All three tests detected size trends for impedance plethysmography in deep-vein thrombosis, where the fitted curve was nearly symmetric. The proposed test distinguishes changes in the summary curve from changes in operating position.

stat.ME↗

Nesting or resolution? Facility proxies for variance estimation versus confounding adjustment in claims data without facility identifiers

In claims databases, facility identifiers are often removed during de-identification, leaving only coarse attributes such as bed size. Yet facility information is needed for variance estimation, confounding adjustment and assessment of treatment availability. We examined whether accreditation-gated billing items can build strata standing for hospitals, and how to construct a facility proxy for each use. In 1,730,736 adults undergoing surgery (Medical Data Vision database, Japan, 2019-2023), we built marker-pattern strata from 66 accreditation-gated receipt codes (markers) and evaluated each grouping against a true facility identifier in an insurer-based database (DeSC Healthcare) and in simulation. The variance explained by the marker-pattern strata decreased with the extent to which a variable was patient- rather than hospital-determined ($R^2$ 0.921 for an accreditation item, 0.036 for sex). For variance estimation, the supplied-attribute cells, which can merge but never split hospitals, kept the type I error rate at 4.8% with a wild cluster bootstrap, against 5.4% for the true identifier and 39% when clustering was ignored; every finer marker-based grouping did worse. For adjustment, evaluated with real facility-level confounders and simulated null exposures, the marker pattern removed 51% of the confounding related to accreditation-linked facility characteristics, against 8% for the cells. The proportion of patients in groups mixing hospitals with and without the relevant accreditation was 49% for the marker pattern against 98% for the cells. No single facility proxy serves every purpose: nesting governs variance estimation, and resolution governs adjustment and treatment availability. Without a facility identifier, the supplied-attribute cells can carry the standard errors, and marker-pattern strata are worth considering for the other two uses.

stat.ME↗