Confounding-aware disproportionality analysis reveals disease-inherent versus drug-attributable endocrine safety signals of immune checkpoint inhibitors

Kaya O. B., Yorulmaz Kaya R.

FRONTIERS IN PHARMACOLOGY, vol.17, pp.1-13, 2026 (SCI-Expanded, Scopus)

  • Publication Type: Article / Article
  • Volume: 17
  • Publication Date: 2026
  • Doi Number: 10.3389/fphar.2026.1868030
  • Journal Name: FRONTIERS IN PHARMACOLOGY
  • Journal Indexes: Natural Science Collection (ProQuest), Biological Science Database (ProQuest), Scopus, Science Citation Index Expanded (SCI-EXPANDED), BIOSIS, EMBASE, Directory of Open Access Journals
  • Page Numbers: pp.1-13
  • Gazi University Affiliated: Yes

Abstract

Background: Conventional disproportionality analysis assumes comparable

background event rates between case and control cohorts. In patients with

primary endocrine malignancies treated with immune checkpoint inhibitors

(ICIs), this assumption fails: disease-inherent hormonal dysregulation and

therapy-related sequelae inflate background event rates and may produce

signals misattributed to ICI exposure. No prior study has used tumor-type-

specific reference populations to separate drug-attributable from disease-

inherent signals in this setting.

Methods: Forty quarterly FAERS cycles (Q1 2015 to Q4 2024) were analyzed after

deduplication of 2,264,070 reports. In a pooled analysis, 329 ICI-treated

endocrine cancer cases were compared with 80,191 ICI-treated non-

endocrine cancer controls across six pre-specified endocrine immune-related

adverse event categories, with false discovery rate (FDR) correction. The central

step was a confounding-aware reanalysis comparing ICI-exposed patients of a

given tumor type against a reference cohort with the same malignancy but no ICI

exposure, using logistic regression adjusted for age and sex. The thyroid

carcinoma association was tested using reporter type, Firth penalized

regression, multiple imputation, and a tipping-point analysis.

Results: Pooled adrenal insufficiency did not reach the signal threshold after FDR

correction (ROR = 1.922; 95% CI 1.051–3.513; q = 0.143). This non-significance

concealed two opposing patterns. In ACC, adrenal insufficiency was the strongest

uncorrected subgroup signal yet was not associated with ICI exposure after

adjustment (OR = 0.58; 95% CI 0.09–1.95); with four exposed events and

12.6% power, this subgroup cannot confirm or exclude an association. In

thyroid carcinoma, ICI exposure showed markedly higher adrenal insufficiency

reporting odds (OR = 13.33; 95% CI 4.81–31.85), remaining positive under reporter

adjustment, Firth regression, and multiple imputation, with a tipping-point analysis

indicating only implausibly extreme confounding could nullify it.

Conclusion: Pooled pharmacovigilance can misclassify drug-attributable and

disease-inherent events. Tumor-type-specific reference modeling separates

them: ACC data are most consistent with disease-inherent pathophysiology,

whereas thyroid carcinoma shows a robust adrenal insufficiency signal. We

interpret the thyroid signal as hypothesis-generating, since differential endocrine

surveillance may contribute to its magnitude, and prospective validation is

warranted.


adrenocortical carcinoma, confounding bias, disproportionality analysis, FAERS, immune

checkpoint inhibitors, immune-related adverse events (irAE), pharmacovigilance,

thyroid carcinoma