Evidence map›Paper›PMID 42493754›Full record

ArticleOncology and therapy2026

Clinicians' Perspectives on the Impact of Different Attributes of Androgen Receptor Pathway Inhibitors on Prostate Cancer Treatment Preferences and Compliance in Mexico.

Mario Escobar Gómez, Ana Maria Rodríguez-Leboeuf, Selene Camargo-Correa, Ida Caterina García-Appendini, Arti Dhar, Isabela Rivas, Juan Guillermo Ariza, Pedro A Madero-Morales

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Article in Oncology and therapy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Mario Escobar GómezDepartment of Oncology, Hospital General de Mexico "Dr. Eduardo Liceaga", Mexico City, Mexico. marioescobargmez@yahoo.com.mx.ORCID http://orcid.org/0009-0000-3063-8388
Ana Maria Rodríguez-LeboeufIQVIA PCS, Barcelona, Spain.
Selene Camargo-CorreaIQVIA PCS, Barcelona, Spain.
Ida Caterina García-AppendiniIQVIA RWI, Mexico City, Mexico.
Arti DharAstellas Medical Affairs, Singapore, Singapore.
Isabela RivasAstellas Medical Affairs, Mexico City, Mexico.
Juan Guillermo ArizaAstellas Medical Affairs, Singapore, Singapore.
Pedro A Madero-MoralesDepartment of Urology, Universidad Autónoma de Nuevo León, Monterrey, Mexico.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionAndrogen receptor pathway inhibitors (ARPIs) are a critical prostate cancer (PC) treatment option. Previous research has suggested that the physical attributes of ARPI medications (e.g., pill formulation, pill shape, pill size, dosing) may influence patients' treatment satisfaction, adherence, and persistence, which may impact patient outcomes and quality of life. The objective of this study was to describe Mexican clinical experts' perspectives on how different attributes of pill forms of ARPIs may affect treatment preferences and compliance in patients with PC.

methodsThis study employed a mixed-methods sequential design. In the first phase, we conducted a targeted literature review (TLR) to frame discussions with clinical experts. In phase 2, we conducted a structured expert elicitation (SEE) comprised of a survey, semi-structured individual interviews, and two focus groups with oncologists and urologists to gather insights about the influence of different ARPI attributes on patient preferences and compliance. In the third phase, we constructed a series of probabilistic mathematical models to analyze the likelihood of patients' treatment preferences and adherence.

resultsThe TLR identified pill size, shape, number, frequency, and form as key attributes that influence patients' experiences and treatment compliance. Simpler regimens and smaller, easier-to-swallow formulations were consistently linked to greater comfort and adherence. In the SEE, participants ranked dosing frequency and pill count as the factors with the greatest influence on patient adherence when efficacy was comparable. During the interviews and focus groups, participants highlighted the importance of ≤ 2 pills/day, once-daily dosing, and small pill size alternatives. Findings from the Monte Carlo models were consistent with the earlier phases, supporting the importance of patient-preferred formulations.

conclusionsFindings from this mixed-methods study indicate that patient-preferred formulations may support better treatment adherence and support consideration of patient preferences in treatment decision-making between clinicians and patients to optimize adherence and outcomes in PC management.

Indexed as

AdherenceAndrogen receptor pathway inhibitorsMonte Carlo simulationPatient preferencePersistencePill burdenProstate cancerQualitative researchSolid-oral formulation

Identifiers

PMID42493754
PMCPMC13575021

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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.