Estimating clinically meaningful changes for the Functional Assessment of Cancer Therapy--Prostate: results from a clinical trial of patients with metastatic hormone-refractory prostate cancer
- PMID: 18647260
- DOI: 10.1111/j.1524-4733.2008.00409.x
Estimating clinically meaningful changes for the Functional Assessment of Cancer Therapy--Prostate: results from a clinical trial of patients with metastatic hormone-refractory prostate cancer
Abstract
Objective: To determine clinically meaningful changes (CMCs) for the Functional Assessment of Cancer Therapy-Prostate (FACT-P).
Methods: We obtained data from a Phase III trial of atrasentan in metastatic hormone-refractory prostate cancer patients (n = 809). We determined anchor-based differences using Karnofsky Performance Status (KPS), bone alkaline phosphatase (BAP), hemoglobin, time to disease progression (TTP), adverse events (AE), and survival. One-third and one-half standard deviation and standard error of measurement (SEM) were used as distribution-based criteria for CMCs. Comparison across baseline FACT-P domains and derived scales [FACT-P total score, Trial Outcome Index (TOI) score, prostate cancer subscale (PCS) score, pain-related score, and FACT Advanced Prostate Symptom Index (FAPSI)] were conducted for KPS, BAP, and hemoglobin using Student's t tests. Twelve-week change scores were compared for TTP, AE, and survival using ANCOVA.
Results: CMCs were estimated as 6 to 10 for FACT-P total score, 5 to 9 for FACT-P TOI score, 2 to 3 for FACT-P PCS, 1 to 2 for the 4 PCS pain-related questions, and 2 to 3 for FAPSI. CMCs were also estimated using distribution-based criteria. Kappa statistics were computed to determine the degree of correspondence between the recommended guideline of 1.0 SEM and empirically derived standards. Most of the kappas for health-related quality of life domains and SEM standards had "substantial" to "almost perfect" concordance.
Conclusions: The significant relationship between clinical and quality of life data provides support for the use of CMCs to increase interpretability of FACT-P scores.
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