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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., (Copyright © 2021 ISPOR–The Professional Society for Health Economics and Outcomes Research. Published by Elsevier Inc. All rights reserved.)

Objective: Decision-making in ductal carcinoma in situ (DCIS) is complex due to the heterogeneity of the disease. This study aimed to understand women's experience of making treatment decisions for DCIS, their information and support needs, and factors that influenced decisions., Methods: We searched six electronic databases, conference proceedings, and key authors. Two reviewers independently applied inclusion and quality criteria, and extracted findings. Thematic analysis was used to combine and summarise findings., Results: We identified six themes and 28 subthemes from 18 studies. Women with DCIS have knowledge deficits about DCIS, experience anxiety related to information given at diagnosis and the complexity of decision-making, and have misconceptions regarding risks and outcomes of treatment. Women's decisions are influenced by their understanding of risk, the clinical features of their DCIS, and the benefits and harms of treatment options. Women are dissatisfied with the decisional support available., Conclusions: Informed and shared decision-making in this complex decision setting requires clear communication of information specific to DCIS and individual's, as well as decision support for patients and clinicians., Practice Implications: This approach would educate patients and clinicians, and assist clinicians in supporting patients to an evidence-based treatment plan that aligns with individual values and pReferences., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Despite increased recognition of the value of including patient-reported outcomes (PROs) as important end points in phase III clinical trials, there has been a lack of pre-specified PRO hypotheses and shortcomings with the analyses and interpretation of PROs in many ovarian cancer trials. This paper discusses and provides examples of the so-called lost opportunities in ovarian cancer trials. These include: (i) no clear pre-specified PRO hypotheses; (ii) PRO end points not included; (iii) insensitive PRO end point selection; (iv) collection of poor-quality PRO data not suitable for analysis; (v) differences in PROs between treatment arms ignored; and (vi) poor reporting quality. We can learn from the past and with relatively little additional effort, improve the collection and interpretation of PRO data in future ovarian cancer trials. The importance of doing so is underpinned by recent initiatives to improve the standard and usefulness of PRO data in clinical trials. These include the Food and Drug Administration (FDA) Guidance for PROs to support labelling claims, the European Society for Medical Oncology Magnitude of Clinical Benefit Scale (ESMO MCBS), the International Society for Quality-of-Life Research PRO reporting guidance and the Consolidated Standards of Reporting Clinical Trials (CONSORT)-PRO-extension statement which includes a checklist of recommended items to include in PRO sections of trial protocols. Promoting the importance of hypothesis-driven PROs in ovarian cancer clinical trials will lead to improvements in the design of these trials and the interpretation of their results., (© The Author 2016. Published by Oxford University Press on behalf of the European Society for Medical Oncology. All rights reserved. For permissions, please email: journals.permissions@oup.com.)