Improving decision-making: Deriving patient-valued utilities from a disease-specific quality of life questionnaire for evaluating clinical trials

The aim of the work reported in this thesis was to develop a scoring algorithm that converts ratings from a validated disease-specific quality of life questionnaire called the Utility-Based Questionnaire-Cancer (UBQ-C) into a utility index that is designed for evaluating clinical trials to inform clinical decisions about cancer treatments. The UBQ-C includes a scale for global health status (1 item); and subscales for physical function (3 items), social/usual activities (4 items), self-care (1 item), and distresses due to physical and psychological symptoms (21 items). Data from three studies was used. A valuation survey consisted of patients with advanced cancer (n=204) who completed the UBQ-C and assigned time-trade-off utilities about their own health state. Clinical trials were of chemotherapy for advanced (n=325) and early (n=126) breast cancer. A scoring algorithm was derived to convert the subscales into a subset index, and combine it with the global scale into an overall quality of life index, which was converted to a utility index with a power transformation. Optimal weights were assigned to the subscales that reflected their correlations with a global scale in each study. The derived utilities were validated by comparison with other patient characteristics. Each trial was evaluated in terms of differences in utility between treatment groups. In the valuation survey, the weights (range 0 to 1) for the subset index were: physical function 0.28, social/usual activities 0.06, self-care 0.01, and distresses 0.64. Weights for the overall quality of life index were health status 0.65 and subset index 0.35. The mean of the utility index scores was similar to the mean of the time trade-off utilities (0.92 vs. 0.91, p=0.6). The weights were adjusted in each clinical trial. The utility index was substantially correlated with other measures of quality of life, discriminated between breast cancer that was advanced rather than early (means 0.88 vs 0.94, p<0.0001), and was responsive to toxic effects of chemotherapy in early breast cancer (mean change 0.07, p<0.0001). There were trends to better mean scores on the utility index for patients allocated to standard-dose versus high-dose chemotherapy in the early cancer trial (p=0.1), and oral versus intravenous chemotherapy in the advanced cancer trial (p=0.2). In conclusion, data from a simple, self-rated, disease-specific questionnaire can be converted into a utility index based on cancer patients’ preferences. The index can be optimised in different clinical contexts to reflect the relative importance of different aspects of quality of life to the patients in a trial. The index can be used to generate utility scores and quality-adjusted life-years in clinical trials. It enables the evaluation of the net effect of treatments on health-related quality of life (accounting for trade-offs between disparate aspects); the evaluation of the net benefit of treatments (accounting for trade-offs between quality of life and survival); and an alternate perspective for comparing the incremental cost-effectiveness of treatments (accounting for trade-offs between net benefit and costs). The practical significance of this work is to facilitate the integration of data about health-related quality of life with traditional trial endpoints such as survival and tumour response. This will better inform clinical decision-making, and provide an alternate viewpoint for economic decision-making. Broadly, it will help patients, clinicians and health funders make better decisions about cancer treatments, by considering potential trade-offs between effects on survival and health-related quality of life

Improving decision-making: Deriving patient-valued utilities from a disease-specific quality of life questionnaire for evaluating clinical trials

The aim of the work reported in this thesis was to develop a scoring algorithm that converts ratings from a validated disease-specific quality of life questionnaire called the Utility-Based Questionnaire-Cancer (UBQ-C) into a utility index that is designed for evaluating clinical trials to inform clinical decisions about cancer treatments. The UBQ-C includes a scale for global health status (1 item); and subscales for physical function (3 items), social/usual activities (4 items), self-care (1 item), and distresses due to physical and psychological symptoms (21 items). Data from three studies was used. A valuation survey consisted of patients with advanced cancer (n=204) who completed the UBQ-C and assigned time-trade-off utilities about their own health state. Clinical trials were of chemotherapy for advanced (n=325) and early (n=126) breast cancer. A scoring algorithm was derived to convert the subscales into a subset index, and combine it with the global scale into an overall quality of life index, which was converted to a utility index with a power transformation. Optimal weights were assigned to the subscales that reflected their correlations with a global scale in each study. The derived utilities were validated by comparison with other patient characteristics. Each trial was evaluated in terms of differences in utility between treatment groups. In the valuation survey, the weights (range 0 to 1) for the subset index were: physical function 0.28, social/usual activities 0.06, self-care 0.01, and distresses 0.64. Weights for the overall quality of life index were health status 0.65 and subset index 0.35. The mean of the utility index scores was similar to the mean of the time trade-off utilities (0.92 vs. 0.91, p=0.6). The weights were adjusted in each clinical trial. The utility index was substantially correlated with other measures of quality of life, discriminated between breast cancer that was advanced rather than early (means 0.88 vs 0.94, p<0.0001), and was responsive to toxic effects of chemotherapy in early breast cancer (mean change 0.07, p<0.0001). There were trends to better mean scores on the utility index for patients allocated to standard-dose versus high-dose chemotherapy in the early cancer trial (p=0.1), and oral versus intravenous chemotherapy in the advanced cancer trial (p=0.2). In conclusion, data from a simple, self-rated, disease-specific questionnaire can be converted into a utility index based on cancer patients’ preferences. The index can be optimised in different clinical contexts to reflect the relative importance of different aspects of quality of life to the patients in a trial. The index can be used to generate utility scores and quality-adjusted life-years in clinical trials. It enables the evaluation of the net effect of treatments on health-related quality of life (accounting for trade-offs between disparate aspects); the evaluation of the net benefit of treatments (accounting for trade-offs between quality of life and survival); and an alternate perspective for comparing the incremental cost-effectiveness of treatments (accounting for trade-offs between net benefit and costs). The practical significance of this work is to facilitate the integration of data about health-related quality of life with traditional trial endpoints such as survival and tumour response. This will better inform clinical decision-making, and provide an alternate viewpoint for economic decision-making. Broadly, it will help patients, clinicians and health funders make better decisions about cancer treatments, by considering potential trade-offs between effects on survival and health-related quality of life

Deriving a preference-based measure for cancer using the EORTC QLQ-C30 : a confirmatory versus exploratory approach

Background: To derive preference-based measures from various condition-specific descriptive health-related quality of life (HRQOL) measures. A general 2-stage method is evolved: 1) an item from each domain of the HRQOL measure is selected to form a health state classification system (HSCS); 2) a sample of health states is valued and an algorithm derived for estimating the utility of all possible health states. The aim of this analysis was to determine whether confirmatory or exploratory factor analysis (CFA, EFA) should be used to derive a cancer-specific utility measure from the EORTC QLQ-C30. Methods: Data were collected with the QLQ-C30v3 from 356 patients receiving palliative radiotherapy for recurrent or metastatic cancer (various primary sites). The dimensional structure of the QLQ-C30 was tested with EFA and CFA, the latter based on a conceptual model (the established domain structure of the QLQ-C30: physical, role, emotional, social and cognitive functioning, plus several symptoms) and clinical considerations (views of both patients and clinicians about issues relevant to HRQOL in cancer). The dimensions determined by each method were then subjected to item response theory, including Rasch analysis. Results: CFA results generally supported the proposed conceptual model, with residual correlations requiring only minor adjustments (namely, introduction of two cross-loadings) to improve model fit (increment χ2(2) = 77.78, p 75% observation at lowest score), 6 exhibited misfit to the Rasch model (fit residual > 2.5), none exhibited disordered item response thresholds, 4 exhibited DIF by gender or cancer site. Upon inspection of the remaining items, three were considered relatively less clinically important than the remaining nine. Conclusions: CFA appears more appropriate than EFA, given the well-established structure of the QLQ-C30 and its clinical relevance. Further, the confirmatory approach produced more interpretable results than the exploratory approach. Other aspects of the general method remain largely the same. The revised method will be applied to a large number of data sets as part of the international and interdisciplinary project to develop a multi-attribute utility instrument for cancer (MAUCa)

Developing a clinical pathway for the identification and management of anxiety and depression in adult cancer patients: an online Delphi consensus process

Purpose: People with cancer and their families experience high levels of psychological morbidity. However many cancer services do not routinely screen patients for anxiety and depression and there are no standardized clinical referral pathways. This study aimed to establish consensus on elements of a draft clinical pathway tailored to the Australian context. Methods: A two-round Delphi study was conducted to gain consensus among Australian oncology and psycho-oncology clinicians about the validity of 39 items that form the basis of a clinical pathway that includes screening, assessment, referral and stepped-care management of anxiety and depression in the context of cancer. The expert panel comprised 87 multidisciplinary clinician members of the Australian Psycho-oncology Cooperative Research Group (PoCoG). Respondents rated their level of agreement with each statement on a 5-point likert scale. Consensus was defined as >80% of respondents scoring within 2 points on the likert scale. Results: Consensus was reached for 21 of 39 items, and a further 15 items approached consensus except for specific contextual factors, after 2 Delphi rounds. Formal screening for anxiety and depression, a stepped care model of management and recommendations for inclusion of length of treatment and time to review were endorsed. Consensus was not reached on items related to roles and responsibilities, particularly those not applicable across cancer settings. Conclusions: This study identified a core set of evidence- and consensus-based principles considered essential to a stepped care model of care incorporating identification, referral and management of anxiety and depression in adult cancer patients.This study was funded by Sydney Catalyst Translational Cancer Research Centr

Clinical pathway for the screening, assessment and management of anxiety and depression in adult cancer patients: Australian guidelines.

Purpose A clinical pathway for anxiety and depression in adult cancer patients was developed to guide best practice in Australia. Methods The pathway was based on a rapid review of existing guidelines, systematic reviews and meta-analyses, stakeholder interviews, a Delphi process with 87 multi-disciplinary stakeholders and input from a multidisciplinary advisory panel. Results The pathway recommends formalised routine screening for anxiety and depression in patients with cancer at key points in the patient’s journey. The Edmonton Symptom Assessment System (ESAS) or Distress Thermometer (DT) with problem checklist are recommended as brief screening tools, combined with a more detailed tool, such as the Hospital Anxiety and Depression Scale (HADS), to identify possible cases. A structured clinical interview will be required to confirm diagnosis. When anxiety or depression is identified it is recommended one person in a treating team takes responsibility for co-ordinating appropriate assessment, referral and follow-up (not necessarily carrying these out themselves). A stepped care model of intervention is proposed, beginning with the least intensive available that is still likely to provide significant health gain. The exact intervention, treatment length and follow up timelines as well as professionals involved, are provided as a guide only. Each service should identify their own referral network based on local resources and current service structure, as well as patient preference. Discussion This clinical pathway will assist cancer services to design their own systems to detect and manage anxiety and depression in their patients, to improve the quality of care

Everybody wants it done but nobody wants to do it. An exploration of the barrier and enablers of critical components towards creating a clinical pathway for anxiety and depression in cancer

Background: This study aimed to explore barriers to and enablers for future implementation of a draft clinical pathway for anxiety and depression in cancer patients in the Australian context. Methods: Health professionals reviewed a draft clinical pathway and participated in qualitative interviews about the delivery of psychosocial care in their setting, individual components of the draft pathway, and barriers and enablers for its future implementation. Results: Five interrelated themes were identified: ownership; resources and responsibility; education and training; patient reluctance; and integration with health services beyond oncology. Conclusions: The five themes were perceived as both barriers and enablers and provide a basis for an implementation plan that includes strategies to overcome barriers. The next steps are to design and deliver the clinical pathway with specific implementation strategies that address team ownership, endorsement by leaders, education and training modules designed for health professionals and patients and identify ways to integrate the pathway into existing cancer services

Deriving a preference-based utility measure for cancer patients from the European Organisation for the Research and Treatment of Cancer's Quality of Life Questionnaire C30: a confirmatory versus exploratory approach

Background: Multi attribute utility instruments (MAUIs) are preference-based measures that comprise a health state classification system (HSCS) and a scoring algorithm that assigns a utility value to each health state in the HSCS. When developing a MAUI from a health-related quality of life (HRQOL) questionnaire, first a HSCS must be derived. This typically involves selecting a subset of domains and items because HRQOL questionnaires typically have too many items to be amendable to the valuation task required to develop the scoring algorithm for a MAUI. Currently, exploratory factor analysis (EFA) followed by Rasch analysis is recommended for deriving a MAUI from a HRQOL measure. Aim: To determine whether confirmatory factor analysis (CFA) is more appropriate and efficient than EFA to derive a HSCS from the European Organisation for the Research and Treatment of Cancer’s core HRQOL questionnaire, Quality of Life Questionnaire (QLQ-C30), given its well-established domain structure. Methods: QLQ-C30 (Version 3) data were collected from 356 patients receiving palliative radiotherapy for recurrent/metastatic cancer (various primary sites). The dimensional structure of the QLQ-C30 was tested with EFA and CFA, the latter informed by the established QLQC30 structure and views of both patients and clinicians on which are the most relevant items. Dimensions determined by EFA or CFA were then subjected to Rasch analysis. Results: CFA results generally supported the proposed QLQ-C30 structure (comparative fit index =0.99, Tucker–Lewis index =0.99, root mean square error of approximation =0.04). EFA revealed fewer factors and some items cross-loaded on multiple factors. Further assessment of dimensionality with Rasch analysis allowed better alignment of the EFA dimensions with those detected by CFA. Conclusion: CFA was more appropriate and efficient than EFA in producing clinically interpretable results for the HSCS for a proposed new cancer-specific MAUI. Our findings suggest that CFA should be recommended generally when deriving a preference-based measure from a HRQOL measure that has an established domain structure