Benyam Muluneh

Background: Oral anticancer medications are standard care for cancer. Medication adherence influences health outcomes, but valid, reliable measures assessing self-reported medication adherence are limited. Psychometric properties of the PROMIS ^® Medication Adherence Scale (PMAS) were evaluated in patients prescribed oral anticancer medications for multiple myeloma. Methodology: This was a secondary analysis from a longitudinal observational study examining medication adherence, symptoms, quality of life, and financial hardship among individuals prescribed oral anticancer medications for multiple myeloma. PMAS measured self-reported medication adherence. Self-report and medical record data assessed participant characteristics and adherence correlates. Objective medication adherence indices were generated from continuous electronic event monitored data. Internal consistency reliability was estimated using Cronbach’s alpha. Dimensionality was assessed with confirmatory factor analysis. Construct validity was assessed considering adherence correlates. Spearman rank-order correlations summarized associations between PMAS and electronic event monitored data. Results: PMAS items had limited variability, with high adherence over time. Reliability of PMAS scores was adequate (T1 α = 0.82, 95% CI: 0.75, 0.87; T2 α = 0.84, 95% CI: 0.78, 0.89). Confirmatory factor analysis fit was better for sub-scales (Medication Beliefs and Knowledge and Medication Taking Behaviors) than Total scale, particularly for the Medication Beliefs and Knowledge subscale. Adherence correlates were observed as expected between PMAS and age, self-reported cognitive function, symptom severity, and depression. Weak/moderate positive associations were found between PMAS and electronic event monitored data. Conclusions: Reliability, two-factor dimensionality, and construct validity were supported, with some evidence of concurrent criterion validity with electronic event monitored adherence data. Additional validation testing is needed to support findings. Evidence supports the feasibility of longitudinal oral anticancer medication adherence assessment monitoring using PMAS.

Cancer registries allow for the collection of key data associated with patient demographics, diagnoses, and treatments, making them an essential tool for research aimed to improve the quality of care in patients with cancer. Previously, a pediatric cancer registry did not exist in Ethiopia, and prevented the collection of such data. In order to address this gap, a group of oncology clinicians and implementation scientists from the University of North Carolina worked in collaboration with all five pediatric cancer centers within Ethiopia to identify barriers and facilitators associated with a piloted REDCap registry, provide necessary training, and perform workflow analyses to create one integrated process for a pediatric cancer registry. Based on these analyses, deficiencies in the current process were identified and standard operating procedures were developed for each cancer center to guide data collection. These advancements led to the launch of Ethiopia’s first pediatric hospital based cancer registry.

1. Leukemia treatment has undergone a major transformation over the past two decades [1]. Historically, care for the major leukemia subtypes relied heavily on parenteral cytotoxic therapies deliver...

Dose modifications (DMs) are common in hematology and oncology, yet their causal effect on clinical outcomes remains uncertain. Conventional survival analyses may yield biased results since DM is a time‐varying exposure and is often confounded by patient frailty and disease severity. To address these challenges, we applied a causal inference framework to real‐world data from 130 patients with chronic lymphocytic leukemia treated with ibrutinib to estimate the effect of DM on clinical outcomes. A Bayesian prognostic risk (PR) model was developed to characterize patients' baseline risk, incorporating patient‐level clinical trial data from RESONATE‐2 as prior information. Baseline (b‐sIPW) and time‐updated stabilized inverse probability weights (tu‐sIPW) were developed to improve covariate balance between patients with and without DM and to account for timing‐varying confounding. Baseline Cox models were compared with longitudinal Cox models in which DM was specified as a time‐varying exposure. In baseline Cox analyses, DM appeared to be associated with shorter progression‐free survival (PFS). However, in longitudinal Cox models accounting for time‐varying exposure and confounding using stabilized inverse probability weighting, the estimated DM effect was attenuated and no longer statistically significant. These findings suggest that the apparent detrimental effect of DM on PFS is potentially explained by baseline and time‐varying confounding, and improper handling of time‐varying exposure, highlighting the importance of causal inference methods when evaluating DM causal effects in oncology therapies.

Patient‐reported outcomes (PROs) are now considered essential components of clinical development and regulatory assessment. Despite their widespread collection, PRO data remain under‐utilized, with analyses often limited to descriptive summaries or single time‐point comparisons that fail to capture the richness, longitudinal structure, and patient‐centric meaning rooted within these measures. In this tutorial, we have outlined key considerations for aligning fit‐for‐purpose PRO measures with clearly articulated research questions and estimands, and applying analytically robust methods that respect the bounded, noisy, and often incomplete nature of PRO data. Through working examples, we demonstrated how pharmacometric approaches, such as bounded outcome score modeling and item response theory—nonlinear mixed effects modeling, can enable more efficient use of all available data, improve sensitivity to treatment effects, and provide interpretable longitudinal trajectories that are directly relevant to clinical decision‐making. Importantly, pharmacometric approaches allow PROs to move beyond exploratory endpoints to becoming integral tools for understanding treatment tolerability, differentiating therapies with similar efficacy, and predicting patient‐centric treatment outcomes. Looking forward, realizing the full potential of PROs will require early integration of patient‐reported outcome measures (PROMs) into clinical development programs, careful consideration of assessment timing and frequency, and continued collaboration among clinicians, statisticians, pharmacometricians, and regulators. Ultimately, integrating PROs in drug development as one of the clinical outcome assessments is a necessary step toward ensuring that drug development and clinical decision‐making more accurately reflect what matters most to patients: how they feel, how they function, and how treatments affect their quality of life.

Background: Oral anti-cancer treatment is commonly used, and non-adherence can negatively affect outcomes. The Oral Oncolytic Non-Adherence Estimator (ORACLE) was designed to prospectively categorize patients into risk levels by considering constructs such as self-efficacy, health literacy, perception of treatment, quality of life, social support, and regimen complexity. Validating this estimator is essential. Methods: A content validation survey evaluated representativeness, clarity, and factor structure of each construct in ORACLE. A multidisciplinary panel of 8 content experts and 4 lay experts completed the survey. Results were analyzed to evaluate inter-rater agreement, content validity index (CVI), and factorial validity index (FVI). Post-survey workgroup discussions reviewed results and considered tool adjustments. Results: All 12 invited experts completed the survey. The average CVI for content experts was 0.88 for representativeness and 0.98 for clarity. The average CVI for lay experts was 0.95 for clarity. The average FVI was 0.88, with self-efficacy and negative perception constructs yielding the lowest scores (0.63 and 0.75, respectively). ORACLE revisions addressed survey results and expert feedback on health literacy, social support, and regimen complexity. Conclusions: Strong CVI for content and lay experts (94% and 95%) and strong FVI for content experts (98%) verify that the tool is understandable and addresses non-adherence issues, establishing validity of construct clarity and representativeness. Content validity of the first pre-treatment oral oncolytic non-adherence risk estimator was established, providing a reliable tool to identify patients at higher risk of non-adherence.

Introduction: Cancer and Leukemia Group B (CALGB) 10403 is a pediatric-inspired treatment protocol for adolescent and young adult (AYA) acute lymphoblastic leukemia (ALL) patients. During Course V maintenance, patients receive mercaptopurine (6MP) and oral methotrexate (MTX), which require frequent laboratory monitoring and dose adjustments. The purpose of this study was to characterize the impact of pharmacist intervention on the safe implementation of Course V. Methods: This single-center, retrospective chart review included AYA patients with B- or T-cell ALL who initiated treatment with CALGB 10403 at UNCMC between April 2014 and April 2024. The primary endpoint was the rate of pharmacist-driven 6MP and MTX holds per protocol during Course V. The key secondary endpoint was the rate of pharmacist-driven 6MP and MTX dose adjustments during Course V. Results: All 18 patients required chemotherapy dose adjustments. Pharmacists led 6MP and MTX dose interruptions in 25 of 35 (71.4%) instances and dose modifications in 85 of the 140 (60.7%) instances. With the addition of a second pharmacist in the leukemia clinic, the monthly rate of pharmacist-led dose holds and modifications for 6MP and MTX increased by over four-fold. Conclusion: Pharmacists played a significant role in the safe administration of 6MP and MTX, particularly with the addition of a second clinic pharmacist. This study demonstrates the value of pharmacist involvement in optimizing patient safety during treatment of ALL.

Patient engagement (PE) refers to the active partnership between researchers and patients throughout the research process. Here, we introduce a novel framework that embeds PE directly into quantitative modeling workflows, including model scoping and question framing, covariate selection, dose and regimen optimization, and interpretation of model outputs. Practical approaches such as feedback sessions and “data parties” demonstrate how integrating PE can systematically inform modeling decisions and ensure analyses remain patient‐centered.