ArticleCureus2025
Development of a Bayesian Hierarchical Model for Medical Resource-Limited Settings: Prediction of Treatment Efficacy in Breast Cancer Patients in Kenya.
Article in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundBreast cancer remains a leading public health challenge in Kenya, where treatment outcomes are shaped by the complex interplay between institutional variability and patient-specific biological factors. Studies have demonstrated that Bayesian hierarchical models can effectively capture such multi-level interactions, improving prediction accuracy and clinical applicability. Building on this framework, an enhanced Bayesian hierarchical model incorporating biologically plausible interaction offers additional insight into how patient biology and healthcare delivery context jointly influence outcomes. In resource-limited settings, such a model is essential for guiding targeted treatment allocation, reducing outcome disparities, and optimizing scarce healthcare resources.
methodsWe conducted a retrospective cohort analysis of 284 breast cancer patients treated across 12 major cancer treatment centers in Kenya between 2018 and 2022. The primary outcome was pathological complete response to neoadjuvant chemotherapy (NAC). Three progressive Bayesian hierarchical models were developed, culminating in an enhanced model (M₃) that incorporated institutional variation, key clinical predictors, and biologically plausible interaction effects to capture complex stage-subtype relationships. Posterior inference was performed using Markov Chain Monte Carlo simulation, with comprehensive validation across multiple scenarios. A total of 48,000 simulations were completed.
resultsHigher clinical stage was associated with larger odds ratios (ORs) for the modeled outcome in the hormone receptors (HR)- reference group (Stage III vs I, OR 9.81), and HR positivity had a smaller OR (0.37), with interaction terms trending against HR positivity at higher stage but with intervals including the null. Predicted pathological complete response (pCR) remained highest in HR negative (HR-)/human epidermal growth factor receptors 2 (HER
conclusionThe enhanced Bayesian hierarchical model (M₃) integrates complex patient-tumor interactions with institutional variation, delivering well-calibrated, robust, and clinically interpretable predictions of pCR. In resource-limited settings, such models can guide targeted treatment allocation, reduce outcome disparities, and optimize the use of scarce oncology resources.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.