Status : Verified
| Personal Name | Federizo, Adrian-Ver S. |
|---|---|
| Resource Title | Nonparametric Reference Limits for a Fixed Effects Model in an Individual Patient Data Meta-Analysis Framework |
| Date Issued | June 2026 |
| Abstract | The field of systematic review and meta-analysis allows researchers to combine multiple studies to obtain a more precise estimate of clinical phenomena. This research focuses on determining reference intervals, which provides a range of val-ues used by clinicians to classify subjects as healthy or not. Existing methods for estimating reference intervals in meta-analysis rely on strict parametric assump-tions, which lead to severe variance overestimation and artificially inflated interval lengths when study populations are skewed or heterogeneous. To resolve these limitations, this study proposes a nonparametric methodology based on Kernel Density Estimation integrated into a Mixture Distribution framework (KDE-MD) under a fixed-effects model, the preferred approach when only a few studies are available. By superimposing individual density curves rather than pooling their variances, the KDE-MD method successfully adapts to complex, mixed data struc-tures without requiring pre-specified distribution families. Furthermore, because non-parametric density estimation inherently risks finite-sample over-smoothing, a bootstrap coverage calibration procedure is introduced to dynamically adjust the nominal confidence level. Extensive simulation results demonstrate that the Bootstrap-Calibrated KDE-MD (BootKDE-MD) methodology effectively detects and corrects these finite-sample biases. Ultimately, the proposed method consis-tently guarantees the accurate nominal target coverage while yielding narrower interval lengths than traditional parametric alternatives. |
| Degree Course | Master of Science in Statistics |
| Language | English |
| Keyword | Meta-analysis; fixed-effects models; reference interval; prediction interval; kernel density estimation; bootstrap calibration |
| Material Type | Thesis/Dissertation |
Preliminary Pages
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Category : F - Regular work, i.e., it has no patentable invention or creation, the author does not wish for personal publication, there is no confidential information.
Access Permission : Open Access