Trial reportPharmaceutical research2018
A Pharmacometric Analysis of Patient-Reported Outcomes in Breast Cancer Patients Through Item Response Theory.
Trial report in Pharmaceutical research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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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.
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Who cites it
11 citing papers in PubMed.
- Improved Decision-Making Confidence Using Item-Based Pharmacometric Model: Illustration with a Phase II Placebo-Controlled Trial.The AAPS journal · 2021Trial
- Detecting placebo and drug effects on Parkinson's disease symptoms by longitudinal item-score models.CPT: pharmacometrics & systems pharmacology · 2021Trial
- An item response theory based integrated model of headache, nausea, photophobia, and phonophobia in migraine patients.Journal of pharmacokinetics and pharmacodynamics · 2018Trial
- Comparison of the power and type 1 error of total score models for drug effect detection in clinical trials.Journal of pharmacokinetics and pharmacodynamics · 2024Article
- Comparison of Two Methods for Determining Item Characteristic Functions and Latent Variable Time-Course for Pharmacometric Item Response Models.The AAPS journal · 2024Article
- A disease model predicting placebo response and remission status of patients with ulcerative colitis using modified Mayo score.Clinical and translational science · 2023Article
- Psychometric parameters of food allergy quality of life during an allergen immunotherapy trial.Allergy · 2022Article
- Item Response Theory Modeling of the International Prostate Symptom Score in Patients with Lower Urinary Tract Symptoms Associated with Benign Prostatic Hyperplasia.The AAPS journal · 2020Article
- Integrated Item Response Theory Modeling of Multiple Patient-Reported Outcomes Assessing Lower Urinary Tract Symptoms Associated with Benign Prostatic Hyperplasia.The AAPS journal · 2020Article
- A longitudinal item response model for Aberrant Behavior Checklist (ABC) data from children with autism.Journal of pharmacokinetics and pharmacodynamics · 2020Observational
- Supervised Multidimensional Item Response Theory Modeling of Pediatric Iatrogenic Withdrawal Symptoms.CPT: pharmacometrics & systems pharmacology · 2019Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeAn item response theory (IRT) pharmacometric framework is presented to characterize Functional Assessment of Cancer Therapy-Breast (FACT-B) data in locally-advanced or metastatic breast cancer patients treated with ado-trastuzumab emtansine (T-DM1) or capecitabine-plus-lapatinib.
methodsIn the IRT model, four latent well-being variables, based on FACT-B general subscales, were used to describe the physical, social/family, emotional and functional well-being. Each breast cancer subscale item was reassigned to one of the other subscales. Longitudinal changes in FACT-B responses and covariate effects were investigated.
resultsThe IRT model could describe both item-level and subscale-level FACT-B data. Non-Asian patients showed better baseline social/family and functional well-being than Asian patients. Moreover, patients with Eastern Cooperative Oncology Group performance status of 0 had better baseline physical and functional well-being. Well-being was described as initially increasing or decreasing before reaching a steady-state, which varied substantially between patients and subscales. T-DM1 exposure was not related to any of the latent variables. Physical well-being worsening was identified in capecitabine-plus-lapatinib-treated patients, whereas T-DM1-treated patients typically stayed stable.
conclusionThe developed framework provides a thorough description of FACT-B longitudinal data. It acknowledges the multi-dimensional nature of the questionnaire and allows covariate and exposure effects to be evaluated on responses.
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