Standardizing Patient-Reported Outcomes Assessment in Cancer Clinical Trials: A Patient-Reported Outcomes Measurement Information System Initiative

病人报告结果测量信息系统 医学 社会心理的 生活质量(医疗保健) 应对(心理学) 临床试验 苦恼 计算机化自适应测验 患者报告的结果 梅德林 医疗保健 临床心理学 物理疗法 心理测量学 护理部 精神科 病理 政治学 法学 经济 经济增长
作者
Sofia F. Garcia,David Cella,Steven B. Clauser,Kathryn E. Flynn,Thomas E. Lad,Jin‐Shei Lai,Bryce B. Reeve,Ashley Wilder Smith,Arthur A. Stone,Kevin P. Weinfurt
出处
期刊:Journal of Clinical Oncology [Lippincott Williams & Wilkins]
卷期号:25 (32): 5106-5112 被引量:394
标识
DOI:10.1200/jco.2007.12.2341
摘要

Patient-reported outcomes (PROs), such as symptom scales or more broad-based health-related quality-of-life measures, play an important role in oncology clinical trials. They frequently are used to help evaluate cancer treatments, as well as for supportive and palliative oncology care. To be most beneficial, these PROs must be relevant to patients and clinicians, valid, and easily understood and interpreted. The Patient-Reported Outcomes Measurement Information System (PROMIS) Network, part of the National Institutes of Health Roadmap Initiative, aims to improve appreciably how PROs are selected and assessed in clinical research, including clinical trials. PROMIS is establishing a publicly available resource of standardized, accurate, and efficient PRO measures of major self-reported health domains (eg, pain, fatigue, emotional distress, physical function, social function) that are relevant across chronic illnesses including cancer. PROMIS is also developing measures of self-reported health domains specifically targeted to cancer, such as sleep/wake function, sexual function, cognitive function, and the psychosocial impacts of the illness experience (ie, stress response and coping; shifts in self-concept, social interactions, and spirituality). We outline the qualitative and quantitative methods by which PROMIS measures are being developed and adapted for use in clinical oncology research. At the core of this activity is the formation and application of item banks using item response theory modeling. We also present our work in the fatigue domain, including a short-form measure, as a sample of PROMIS methodology and work to date. Plans for future validation and application of PROMIS measures are discussed.

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