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arxiv: 2604.18898 · v2 · submitted 2026-04-20 · 📊 stat.AP

A Review of Statistical Methods for Spontaneous Reporting System Data Mining: Signal Detection and Beyond

Yihao Tan , Marianthi Markatou , Saptarshi Chakraborty This is my paper

Pith reviewed 2026-05-10 02:43 UTC · model grok-4.3

classification 📊 stat.AP
keywords spontaneous reporting systemssignal detectionpharmacovigilanceadverse eventsdata miningstatistical methodsdrug safetycontingency tables
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The pith

Contemporary statistical methods for spontaneous reporting system data support both binary signal detection and estimation of signal strength with uncertainty for drug safety.

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

This paper reviews statistical approaches to mining data from spontaneous reporting systems such as FAERS, EudraVigilance and VigiBase in order to identify associations between drugs and adverse events. It covers traditional methods that frame detection as a binary decision and more recent techniques that estimate signal strength along with uncertainty measures. The authors supply practical steps for building contingency tables from the aggregated counts that public databases release. A sympathetic reader would care because postmarketing surveillance depends on these tools to flag potential safety problems after drugs are approved. The review demonstrates the steps with opioid datasets drawn from real sources.

Core claim

The paper claims that a review of contemporary SRS data mining methods and their statistical underpinnings, paired with explicit guidance on constructing contingency tables from aggregated AE-drug counts, supplies a usable foundation for safety assessment across major pharmacovigilance databases.

What carries the argument

Statistical signal detection methods (including disproportionality analyses) together with the preprocessing step of building SRS contingency tables from publicly available aggregated counts.

Load-bearing premise

The selected contemporary methods and preprocessing steps using aggregated counts adequately represent current best practice and can be applied directly without further validation or dataset-specific adjustments.

What would settle it

An analysis of a confirmed drug-adverse event pair that produces materially weaker or stronger signals when the recommended preprocessing steps are omitted.

Figures

Figures reproduced from arXiv: 2604.18898 by Marianthi Markatou, Saptarshi Chakraborty, Yihao Tan.

Figure 1
Figure 1. Figure 1: Signal detection results on the FAERS-opioid–mental data obtained from pseudo-LRT (panel A), general [PITH_FULL_IMAGE:figures/full_fig_p013_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Nonparametric empirical Bayes (general-gamma) signal strength estimation results for the FAERS [PITH_FULL_IMAGE:figures/full_fig_p014_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Signal detection results on the VigiBase-opioid–mental data obtained from pseudo-LRT (panel A), general [PITH_FULL_IMAGE:figures/full_fig_p016_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Nonparametric empirical Bayes (general-gamma) signal strength estimation results for the vigiBase [PITH_FULL_IMAGE:figures/full_fig_p017_4.png] view at source ↗
read the original abstract

Postmarketing safety surveillance relies on data from spontaneous reporting systems (SRS) such as FAERS, EudraVigilance and VigiBase, and commonly uses SRS data mining methods to assess the associations between drugs and adverse events (AEs). Traditionally, these analyses have focused on signal detection framed as a binary decision problem, whereas more recent work has emphasized more nuanced inference involving signal strength estimation and uncertainty quantification. In this paper, we review contemporary SRS data mining approaches and their statistical underpinnings for safety assessment using data from major pharmacovigilance databases worldwide. In addition to methodological review, we provide practical guidance on data preprocessing for such analysis, including construction of SRS contingency tables using only aggregated AE-drug counts, as are publicly available from databases such as VigiBase and EudraVigilance. We illustrate the guidance via opioid-related datasets obtained from FAERS and VigiBase, complied with subsequent downstream SRS data analyses.

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.

Referee Report

0 major / 1 minor

Summary. The manuscript reviews contemporary statistical methods for mining spontaneous reporting system (SRS) data from databases such as FAERS, EudraVigilance, and VigiBase, covering traditional signal detection framed as binary decisions as well as more recent approaches to signal strength estimation and uncertainty quantification. It also supplies practical guidance on preprocessing steps to construct contingency tables from publicly available aggregated AE-drug counts and illustrates the guidance with opioid-related datasets from FAERS and VigiBase.

Significance. If the summaries of methods are accurate and the preprocessing guidance is internally consistent with the stated scope of public aggregated tables, the paper would serve as a useful reference for pharmacovigilance researchers seeking to move beyond binary signal detection toward nuanced inference while working with readily accessible data sources.

minor comments (1)
  1. [Abstract] Abstract, final sentence: the word 'complied' is almost certainly a typographical error and should read 'combined' to make the intended meaning clear.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their positive summary of our manuscript, accurate characterization of its scope, and recommendation for minor revision. The referee's assessment aligns well with our intent to provide both a methodological review and practical preprocessing guidance for SRS data mining.

Circularity Check

0 steps flagged

No significant circularity in this review paper

full rationale

This is a review paper summarizing existing SRS data mining methods from external literature and offering practical preprocessing guidance for aggregated counts from public databases. No new derivations, predictions, or equations are introduced that could reduce to the paper's own inputs by construction. The claims are descriptive and illustrative (e.g., opioid example as demonstration, not proof), with methods attributed to cited sources rather than self-referential fits or definitions. Any self-citations are incidental and non-load-bearing for novel results.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is a review paper; no new free parameters, axioms, or invented entities are introduced.

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Reference graph

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