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Cystic fibrosis (CF) limits survival and negatively affects health-related quality of life (HRQOL). Cost-effectiveness analysis (CEA) may be used to make reimbursement decisions for new CF treatments; nevertheless, generic utility measures used in CEA, such as EQ-5D, are insensitive to meaningful changes in lung function and HRQOL in CF. Here we develop a new, CF disease–specific, preference-based utility measure based on the adolescent/adult version of the Cystic Fibrosis Questionnaire-Revised (CFQ-R), a widely used, CF-specific, patient-reported measure of HRQOL. Blinded CFQ-R data from 4 clinical trials (NCT02347657, NCT02392234, NCT01807923, and NCT01807949) were used to identify discriminating items for a classification system using psychometric (eg, factor and Rasch) analyses. Thirty-two health states were selected for a time trade-off (TTO) exercise with a representative sample of the UK general population. TTO utilities were used to estimate a preference-based scoring algorithm by regression analysis (tobit models with robust standard errors clustered on participants with censoring at −1). A classification system with 8 dimensions (CFQ-R-8 dimensions; physical functioning, vitality, emotion, role functioning, breathing difficulty, cough, abdominal pain, and body image) was generated. TTO was completed by 400 participants (mean age, 47.3 years; 49.8% female). Among the regression models evaluated, the tobit heteroscedastic–ordered model was preferred, with a predicted utility range from 0.236 to 1, no logical inconsistencies, and a mean absolute error of 0.032. The CFQ-R-8 dimensions is the first disease-specific, preference-based scoring algorithm for CF, enabling estimation of disease-specific utilities for CEA based on the well-validated and widely used CFQ-R. • Cost-effectiveness analysis (CEA) may guide reimbursement decisions for cystic fibrosis (CF) treatments; nevertheless, standard measures of utility used in CEA are insensitive to changes in lung function and health-related quality of life in CF. • We used blinded Cystic Fibrosis Questionnaire-Revised (CFQ-R) data from 4 clinical trials to develop the CFQ-R-8 dimensions (CFQ-R-8D), a new, CF disease–specific, preference-based utility measure with 8 dimensions (physical functioning, vitality, emotion, role functioning, breathing difficulty, cough, abdominal pain, and body image). • The CFQ-R-8D enables disease-specific utilities for use in CEA to be generated from the CFQ-R, a measure that is widely used in CF trials. [ABSTRACT FROM AUTHOR]

Objectives: Assessment of patient-reported outcomes (PROs) in oncology is of critical importance because it provides unique information that may also predict clinical outcomes.Methods: We conducted a systematic review of prognostic factor studies to examine the prognostic value of PROs for survival in cancer. A systematic literature search was performed in PubMed for studies published between 2013 and 2018. We considered any study, regardless of the research design, that included at least 1 PRO domain in the final multivariable prognostic model. The protocol (EPIPHANY) was published and registered in the International Prospective Register of Systematic Reviews (CRD42018099160).Results: Eligibility criteria selected 138 studies including 158 127 patients, of which 43 studies were randomized, controlled trials. Overall, 120 (87%) studies reported at least 1 PRO to be statistically significantly prognostic for overall survival. Lung (n = 41, 29.7%) and genitourinary (n = 27, 19.6%) cancers were most commonly investigated. The prognostic value of PROs was investigated in secondary data analyses in 101 (73.2%) studies. The EORTC QLQ-C30 questionnaire was the most frequently used measure, and its physical functioning scale (range 0-100) the most frequent independent prognostic PRO, with a pooled hazard ratio estimate of 0.88 per 10-point increase (95% CI 0.84-0.92).Conclusions: There is convincing evidence that PROs provide independent prognostic information for overall survival across cancer populations and disease stages. Further research is needed to translate current evidence-based data into prognostic tools to aid in clinical decision making. [ABSTRACT FROM AUTHOR]