Recommendations to address respondent burden associated with patient-reported outcome assessment.

Patient-reported outcomes (PROs) are increasingly used in healthcare research to provide evidence of the benefits and risks of interventions from the patient perspective and to inform regulatory decisions and health policy. The use of PROs in clinical practice can facilitate symptom monitoring, tailor care to individual needs, aid clinical decision-making and inform value-based healthcare initiatives. Despite their benefits, there are concerns that the potential burden on respondents may reduce their willingness to complete PROs, with potential impact on the completeness and quality of the data for decision-making. We therefore conducted an initial literature review to generate a list of candidate recommendations aimed at reducing respondent burden. This was followed by a two-stage Delphi survey by an international multi-stakeholder group. A consensus meeting was held to finalize the recommendations. The final consensus statement includes 19 recommendations to address PRO respondent burden in healthcare research and clinical practice. If implemented, these recommendations may reduce PRO respondent burden.

The Functional Assessment of Cancer Therapy Eight Dimension (FACT-8D), a Multi-Attribute Utility Instrument Derived From the Cancer-Specific FACT-General (FACT-G) Quality of Life Questionnaire: Development and Australian Value Set.

OBJECTIVES: To develop a cancer-specific multi-attribute utility instrument derived from the Functional Assessment of Cancer Therapy - General (FACT-G) health-related quality of life (HRQL) questionnaire. METHODS: We derived a descriptive system based on a subset of the 27-item FACT-G. Item selection was informed by psychometric analyses of existing FACT-G data (n = 6912) and by patient input (n = 82). We then conducted an online valuation survey, with participants recruited via an Australian general population online panel. A discrete choice experiment (DCE) was used, with attributes being the HRQL dimensions of the descriptive system and survival duration, and 16 choice-pairs per participant. Utility decrements were estimated with conditional logit and mixed logit modeling. RESULTS: Eight HRQL dimensions were included in the descriptive system: pain, fatigue, nausea, sleep, work, social support, sadness, and future health worry; each with 5 levels. Of 1737 panel members who accessed the valuation survey, 1644 (95%) completed 1 or more DCE choice-pairs and were included in analyses. Utility decrements were generally monotonic; within each dimension, poorer HRQL levels generally had larger utility decrements. The largest utility decrements were for the highest levels of pain (-0.40) and nausea (-0.28). The worst health state had a utility of -0.54, considerably worse than dead. CONCLUSIONS: A descriptive system and preference-based scoring approach were developed for the FACT-8D, a new cancer-specific multi-attribute utility instrument derived from the FACT-G. The Australian value set is the first of a series of country-specific value sets planned that can facilitate cost-utility analyses based on items from the FACT-G and related FACIT questionnaires containing FACT-G items.

U.K. utility weights for the EORTC QLU-C10D.

The EORTC QLU-C10D is a new multi-attribute utility instrument derived from the widely used cancer-specific quality of life questionnaire, EORTC QLQ-C30. It contains 10 dimensions (physical functioning, role functioning, social functioning, emotional functioning, pain, fatigue, sleep, appetite, nausea, bowel problems), each with four levels. The aim of this study was to provide U.K. general population utility weights for the QLU-C10D. A U.K. online panel was quota-sampled to align the sample to the general population proportions of sex and age (≥18 years). The online valuation survey included a discrete choice experiment (DCE). Each participant was asked to complete 16 choice-pairs, each comprising two QLU-C10D health states plus duration. DCE data were analysed using conditional logistic regression to generate utility weights. Data from 2,187 respondents who completed at least one choice set were included in the DCE analysis. The final U.K. QLU-C10D utility weights comprised decrements for each level of each health dimension. For nine of the 10 dimensions (all except appetite), the expected monotonic pattern was observed across levels: Utility decreased as severity increased. For the final model, consistent monotonicity was achieved by merging inconsistent adjacent levels for appetite. The largest utility decrements were associated with physical functioning and pain. The worst possible health state (the worst level of each dimension) is -0.083, which is considered slightly worse than being dead. The U.K.-specific utility weights will enable cost-utility analysis (CUA) for the economic evaluation of new oncology therapies and technologies in the United Kingdom, where CUA is commonly used to inform resource allocation.

Australian Utility Weights for the EORTC QLU-C10D, a Multi-Attribute Utility Instrument Derived from the Cancer-Specific Quality of Life Questionnaire, EORTC QLQ-C30.

BACKGROUND: The EORTC QLU-C10D is a new multi-attribute utility instrument derived from the widely used cancer-specific quality-of-life (QOL) questionnaire, EORTC QLQ-C30. The QLU-C10D contains ten dimensions (Physical, Role, Social and Emotional Functioning; Pain, Fatigue, Sleep, Appetite, Nausea, Bowel Problems), each with four levels. To be used in cost-utility analysis, country-specific valuation sets are required. OBJECTIVE: The aim of this study was to provide Australian utility weights for the QLU-C10D. METHODS: An Australian online panel was quota-sampled to ensure population representativeness by sex and age (≥ 18 years). Participants completed a discrete choice experiment (DCE) consisting of 16 choice-pairs. Each pair comprised two QLU-C10D health states plus life expectancy. Data were analysed using conditional logistic regression, parameterised to fit the quality-adjusted life-year framework. Utility weights were calculated as the ratio of each QOL dimension-level coefficient to the coefficient on life expectancy. RESULTS: A total of 1979 panel members opted in, 1904 (96%) completed at least one choice-pair, and 1846 (93%) completed all 16 choice-pairs. Dimension weights were generally monotonic: poorer levels within each dimension were generally associated with greater utility decrements. The dimensions that impacted most on choice were, in order, Physical Functioning, Pain, Role Functioning and Emotional Functioning. Oncology-relevant dimensions with moderate impact were Nausea and Bowel Problems. Fatigue, Trouble Sleeping and Appetite had relatively small impact. The value of the worst health state was -0.096, somewhat worse than death. CONCLUSIONS: This study provides the first country-specific value set for the QLU-C10D, which can facilitate cost-utility analyses when applied to data collected with the EORTC QLQ-C30, prospectively and retrospectively.

Mode of administration does not cause bias in patient-reported outcome results: a meta-analysis.

PURPOSE: Technological advances in recent decades have led to the availability of new modes to administer patient-reported outcomes (PROs). To aid selecting optimal modes of administration (MOA), we undertook a systematic review to determine whether differences in bias (both size and direction) exist among modes. METHODS: We searched five electronic databases from 2004 (date of last comprehensive review on this topic) to April 2014, cross-referenced and searched reference lists. Studies that compared two or more MOA for a health-related PRO measure in adult samples were included. Two reviewers independently applied inclusion and quality criteria and extracted findings. Meta-analyses and meta-regressions were conducted using random-effects models. RESULTS: Of 5100 papers screened, 222 were considered potentially relevant and 56 met eligibility criteria. No evidence of bias was found for: (1) paper versus electronic self-complete; and (2) self-complete versus assisted MOA. Heterogeneity for paper versus electronic comparison was explained by type of construct (i.e. physical vs. psychological). Heterogeneity for self-completion versus assisted modes was in part explained by setting (clinic vs. home); the largest bias was introduced when assisted completion occurred in the clinic and follow-up was by self-completion (either electronic or paper) in the home. CONCLUSIONS: Self-complete paper and electronic MOA can be used interchangeably for research in clinic and home settings. Self-completion and assisted completion produce equivalent scores overall, although heterogeneity may be induced by setting. These results support the use of mixed MOAs within a research study, which may be a useful strategy for reducing missing PRO data.

Systematic evaluation of the patient-reported outcome (PRO) content of clinical trial protocols.

BACKGROUND: Qualitative evidence suggests patient-reported outcome (PRO) information is frequently absent from clinical trial protocols, potentially leading to inconsistent PRO data collection and risking bias. Direct evidence regarding PRO trial protocol content is lacking. The aim of this study was to systematically evaluate the PRO-specific content of UK National Institute for Health Research (NIHR) Health Technology Assessment (HTA) programme trial protocols. METHODS AND FINDINGS: We conducted an electronic search of the NIHR HTA programme database (inception to August 2013) for protocols describing a randomised controlled trial including a primary/secondary PRO. Two investigators independently reviewed the content of each protocol, using a specially constructed PRO-specific protocol checklist, alongside the 'Standard Protocol Items: Recommendations for Interventional Trials' (SPIRIT) checklist. Disagreements were resolved through discussion with a third investigator. 75 trial protocols were included in the analysis. Protocols included a mean of 32/51 (63%) SPIRIT recommendations (range 16-41, SD 5.62) and 11/33 (33%) PRO-specific items (range 4-18, SD 3.56). Over half (61%) of the PRO items were incomplete. Protocols containing a primary PRO included slightly more PRO checklist items (mean 14/33 (43%)). PRO protocol content was not associated with general protocol completeness; thus, protocols judged as relatively 'complete' using SPIRIT were still likely to have omitted a large proportion of PRO checklist items. CONCLUSIONS: The PRO components of HTA clinical trial protocols require improvement. Information on the PRO rationale/hypothesis, data collection methods, training and management was often absent. This low compliance is unsurprising; evidence shows existing PRO guidance for protocol developers remains difficult to access and lacks consistency. Study findings suggest there are a number of PRO protocol checklist items that are not fully addressed by the current SPIRIT statement. We therefore advocate the development of consensus-based supplementary guidelines, aimed at improving the completeness and quality of PRO content in clinical trial protocols.

Patient-reported outcome (PRO) assessment in clinical trials: a systematic review of guidance for trial protocol writers.

BACKGROUND: Evidence suggests there are inconsistencies in patient-reported outcome (PRO) assessment and reporting in clinical trials, which may limit the use of these data to inform patient care. For trials with a PRO endpoint, routine inclusion of key PRO information in the protocol may help improve trial conduct and the reporting and appraisal of PRO results; however, it is currently unclear exactly what PRO-specific information should be included. The aim of this review was to summarize the current PRO-specific guidance for clinical trial protocol developers. METHODS AND FINDINGS: We searched the MEDLINE, EMBASE, CINHAL and Cochrane Library databases (inception to February 2013) for PRO-specific guidance regarding trial protocol development. Further guidance documents were identified via Google, Google scholar, requests to members of the UK Clinical Research Collaboration registered clinical trials units and international experts. Two independent investigators undertook title/abstract screening, full text review and data extraction, with a third involved in the event of disagreement. 21,175 citations were screened and 54 met the inclusion criteria. Guidance documents were difficult to access: electronic database searches identified just 8 documents, with the remaining 46 sourced elsewhere (5 from citation tracking, 27 from hand searching, 7 from the grey literature review and 7 from experts). 162 unique PRO-specific protocol recommendations were extracted from included documents. A further 10 PRO recommendations were identified relating to supporting trial documentation. Only 5/162 (3%) recommendations appeared in ≥50% of guidance documents reviewed, indicating a lack of consistency. CONCLUSIONS: PRO-specific protocol guidelines were difficult to access, lacked consistency and may be challenging to implement in practice. There is a need to develop easily accessible consensus-driven PRO protocol guidance. Guidance should be aimed at ensuring key PRO information is routinely included in appropriate trial protocols, in order to facilitate rigorous collection/reporting of PRO data, to effectively inform patient care.