Publications by authors named "A Rogatko"

We present a Bayesian adaptive design for dose finding in oncology trials with application to a first-in-human trial. The design is based on the escalation with overdose control principle and uses an intermediate grade 2 toxicity in addition to the traditional binary indicator of dose-limiting toxicity (DLT) to guide the dose escalation and de-escalation. We model the dose-toxicity relationship using the proportional odds model.

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Purpose: Regulatory guidance suggests capturing patient-reported overall side effect impact in cancer trials. We examined whether the Functional Assessment of Cancer Therapy (FACT) GP5 item ("I am bothered by side effects of treatment") post-neoadjuvant chemotherapy/radiotherapy differed between oxaliplatin vs. non- oxaliplatin arms in the National Surgical Adjuvant Breast and Bowel Project (NSABP) R-04 trial of stage II-III rectal cancer patients.

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Purpose: Longitudinal patient tolerability data collected as part of randomized controlled trials are often summarized in a way that loses information and does not capture the treatment experience. To address this, we developed an interactive web application to empower clinicians and researchers to explore and visualize patient tolerability data.

Methods: We used adverse event (AE) data (Common Terminology Criteria for Adverse Events) and patient-reported outcomes (PROs) from the NSABP-B35 phase III clinical trial, which compared anastrozole with tamoxifen for breast cancer-free survival, to demonstrate the tools.

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Unlabelled: Predicting an individual's risk of treatment discontinuation is critical for the implementation of precision chemoprevention. We developed partly conditional survival models to predict discontinuation of tamoxifen or anastrozole using patient-reported outcome (PRO) data from postmenopausal women with ductal carcinoma in situ enrolled in the NSABP B-35 clinical trial. In a secondary analysis of the NSABP B-35 clinical trial PRO data, we proposed two models for treatment discontinuation within each treatment arm (anastrozole or tamoxifen treated patients) using partly conditional Cox-type models with time-dependent covariates.

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Article Synopsis
  • - The Stroke Preclinical Assessment Network (SPAN) was created to address concerns about the reliability of preclinical testing for new stroke treatments, following recent failures in clinical trials.
  • - SPAN conducted a rigorous multi-laboratory trial using various animal models to assess candidate treatments in a controlled manner, ensuring aspects like treatment masking and randomization were properly implemented.
  • - By following a standardized protocol across six labs and successfully enrolling a large number of animals, SPAN aims to enhance reproducibility in preclinical research, potentially applying its framework to other medical research areas.
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