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arxiv: 2604.20611 · v1 · submitted 2026-04-22 · 📊 stat.AP

Bayesian Inference for Incomplete 2x2 Diagnostic Tables

Sara Antonijevic , Danielle Sitalo , Brani Vidakovic This is my paper

Pith reviewed 2026-05-09 22:40 UTC · model grok-4.3

classification 📊 stat.AP
keywords Bayesian inferenceincomplete datadiagnostic accuracy2x2 contingency tablesmissing cell countssensitivity and specificityposterior inferencemedical statistics
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The pith

Hierarchical Bayesian models reconstruct missing cell counts in incomplete 2x2 diagnostic tables and deliver posterior inference for sensitivity, specificity, and related measures with uncertainty quantification.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper develops hierarchical Bayesian models to reconstruct incomplete 2x2 diagnostic tables when only partial information is available, such as a single test-outcome row or true and false positives plus total sample size. This addresses the common problem in medical research where denominators for diseased and non-diseased groups are missing, blocking direct calculation of diagnostic accuracy metrics. The models are demonstrated on a benchmark breast MRI study that is treated as partially observed to evaluate how well the missing entries and operating characteristics can be recovered. Posterior distributions are obtained for the missing counts and the diagnostic measures, including uncertainty even in weakly identified settings.

Core claim

Hierarchical Bayesian models can reconstruct incomplete 2x2 diagnostic tables under two common partial-reporting patterns, producing posterior inference for the missing cell counts together with associated diagnostic measures and uncertainty quantification, as shown by treating a complete breast MRI benchmark as partially observed under controlled missingness.

What carries the argument

Hierarchical Bayesian models with chosen priors that encode the two incomplete 2x2 table structures and allow sampling of the unobserved cells.

If this is right

  • Sensitivity, specificity, positive predictive value, and negative predictive value can be estimated with credible intervals from studies that report only one row or only positives plus total N.
  • Uncertainty quantification remains available even when the data are weakly identified due to missing denominators.
  • Reconstruction performance can be assessed in controlled settings by masking complete tables and comparing recovered values to the known ground truth.
  • The approach directly handles the two most frequent incomplete-reporting patterns encountered in diagnostic accuracy studies.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The method could allow previously excluded studies with incomplete tables to be included in meta-analyses of diagnostic tests.
  • In practice it might reduce selection bias in summaries of test performance by incorporating more real-world reports.
  • The same hierarchical structure could be extended to tables with additional missingness patterns or to multi-test diagnostic settings.

Load-bearing premise

The hierarchical Bayesian structure with the chosen priors recovers the missing cell counts without substantial bias on real incomplete diagnostic data.

What would settle it

Applying the models to the complete breast MRI benchmark after masking it to match the two incomplete scenarios and finding that the posterior means for the masked cells deviate substantially from the known true values would falsify the reconstruction claim.

read the original abstract

Incomplete reporting of diagnostic accuracy data remains a persistent problem in medical research. In many studies, only part of the 2x2 diagnostic table is reported, leaving denominators for diseased and non-diseased groups unknown and preventing direct calculation of sensitivity, specificity, predictive values, and related operating characteristics. To address this limitation, we develop hierarchical Bayesian models for reconstructing incomplete 2x2 diagnostic tables from such partial information. Two motivating scenarios are considered: one in which only a single test-outcome row is observed, and another in which true positives, false positives, and the total sample size are reported but the remaining cells are missing. The proposed models are illustrated on a benchmark breast MRI study with complete counts, treated as partially observed in order to assess reconstruction performance under controlled missingness. The framework yields posterior inference for the missing cell counts and associated diagnostic measures, together with uncertainty quantification in weakly identified settings.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Circularity Check

0 steps flagged

No significant circularity; standard hierarchical Bayesian reconstruction with external validation

full rationale

The paper defines hierarchical Bayesian models for incomplete 2x2 tables using standard priors on cell probabilities and binomial likelihoods, then performs posterior inference via MCMC. The benchmark applies artificial missingness to one complete dataset and compares recovered posteriors to known truth; this is an external check rather than a self-referential fit. No equations reduce a claimed prediction to a fitted input by construction, no uniqueness theorems are imported from self-citations, and no ansatz is smuggled via prior work. The central claims rest on the model specification and simulation study, which remain independent of the target quantities.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Based solely on the abstract, the models rest on standard Bayesian hierarchical modeling assumptions and priors for cell probabilities; no specific free parameters or invented entities are detailed.

axioms (1)
  • standard math Standard assumptions of Bayesian inference including proper prior distributions and likelihood specification for multinomial or binomial cell counts
    Hierarchical models for diagnostic tables inherently rely on these to enable posterior inference on missing cells.

pith-pipeline@v0.9.0 · 5455 in / 1190 out tokens · 29940 ms · 2026-05-09T22:40:43.352360+00:00 · methodology

discussion (0)

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Reference graph

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