Cost-utility analysis of adjuvant goserelin (Zoladex) and adjuvant chemotherapy in premenopausal women with breast cancer

<p>Abstract</p> <p>Background</p> <p>Increased health care costs have made it incumbent on health-care facilities and physicians to demonstrate both clinical and cost efficacy when recommending treatments. Though studies have examined the cost-effectiveness of adjuvant goserelin with radiotherapy for locally advanced prostate cancer, few have compared the cost-effectiveness of adjuvant goserelin to adjuvant chemotherapy alone in premenopausal breast cancer.</p> <p>Methods</p> <p>In this retrospective study at one hospital, the records of 152 patients with stage Ia to IIIa ER + breast cancer who received goserelin or chemotherapy were reviewed. Survival analysis was assessed by the Kaplan-Meier method. Patients were interviewed to evaluate their quality of life using the European Organization for Research and Treatment Quality of Life questionnaire (EORTC-QLQ-C30, version 4.0), and to obtain the utility value by the standard gamble (SG) and visual scale (VS) methods. Total medical cost was assessed from the (National Health Insurance) NHI payer's perspective.</p> <p>Results</p> <p>Survival at 11 years was significantly better in the groserelin group (<it>P </it>< 0.0012). The lifetime lost was lower in the goserelin group (42 months vs. 66 months). The quality adjusted survival (QAS) of patients who received goserelin was longer (122.5 ± 6.3 vs. 112.2 ± 6.7 months). Total expenses of goserelin were more than cyclophosphamide, methotrexate, 5-fluorouracil (CMF) or 5-fluorouracil, epirubicin, cyclophosphamide (FEC) chemotherapy regimes, but less than docetaxel, epirubicin (TE) or docetaxel, epirubicin, cyclophosphamide (TEC) regimes. The quality-adjusted life-year was higher in the goserelin group.</p> <p>Conclusions</p> <p>Goserelin therapy results in better survival and higher utility-weighted life-years, and is more cost-effective than TC or TEC chemotherapy.</p

The Use of Health State Utility Values In Decision Models

Methodological issues of how to use health state utility values (HSUVs) in decision models arise frequently, including the most appropriate evidence to use as the baseline (e.g. the baseline HSUVs associated with avoiding a particular health condition or event), how to capture changes due to adverse events and how to appropriately capture uncertainty in progressive conditions where the expected change in quality of life is likely to be monotonically decreasing over time. As preference-based measures provide different values when collected from the same patient, it is important to ensure that all HSUVs used within a single model are obtained from the same instrument where ever possible. When people enter the model without the condition of interest (e.g. primary prevention of cardiovascular disease, screening or vaccination programmes), appropriate age- and gender-adjusted HSUVs from people without the particular condition should be used as the baseline. General population norms may be used as a proxy if the exact condition-specific evidence is not available. Individual discrete health states should be used for serious adverse reactions to treatment and the corresponding HSUVs sourced as normal. Care should be taken to avoid double counting when capturing the effects for both less severe adverse reactions (e.g. itchy skin rash or dry cough) and more severe adverse events (e.g. fatigue in oncology). Transparency in reporting standards for both the justification of the evidence used and any ‘adjustments’ is important to increase readers’ confidence that the evidence used is the most appropriate available

International Regulations and Recommendations for Utility Data for Health Technology Assessment

Recommendations and guidelines for the collection, generation, source and usage of utility data for health technology assessment (HTA) vary across different countries, with no international consensus. Many international agencies generate their own guidelines providing details on their preferred methods for HTA submissions, and there is variability in both what they recommend and the clarity and amount of detail provided in their guidelines. This article provides an overview of international regulations and recommendations for utility data in HTA for a selection of key HTA countries: Australia, Canada, France, Germany, the Netherlands, Spain (Catalonia), Sweden and the UK (England/Wales and Scotland). International guidelines are typically clear and detailed for the selection of countries assessed regarding the source description of health states (e.g. generic preference-based measure) and who should provide preference weights for these health states (e.g. general population for own country). Many guidelines specify the use of off-the-shelf generic preference-based measures, and some further specify a measure, such as EQ-5D. However, international guidelines are either unclear or lack detailed guidance regarding the collection (e.g. patients report own health), source (e.g. clinical trial) and usage (e.g. adjusting for comorbidities) of utility values. It is argued that there is a need for transparent and detailed international guidelines on utility data recommendations to provide decision makers with the best possible evidence. Where this is not possible it is recommended that best practice should be used to inform the collection, source and usage of utility values in HTA

Recommended Methods for the Collection of Health State Utility Value Evidence in Clinical Studies

A conceptual model framework and an initial literature review are invaluable when considering what health state utility values (HSUVs) are required to populate health states in decision models. They are the recommended starting point early within a research and development programme, and before development of phase III trial protocols. While clinical trials can provide an opportunity to collect the required evidence, their appropriateness should be reviewed against the requirements of the model structure taking into account population characteristics, time horizon and frequency of clinical events. Alternative sources such as observational studies or registries may be more appropriate when evidence describing changes in HSUVs over time or rare clinical events is required. Phase IV clinical studies may provide the opportunity to collect additional longitudinal real-world evidence. Aspects to consider when designing the collection of the evidence include patient and investigator burden, whom to ask, the representativeness of the population, the exact definitions of health states within the economic model, the timing of data collection, sample size, and mode of administration. Missing data can be an issue, particularly in longitudinal studies, and it is important to determine whether the missing data will bias inferences from analyses. For example, respondents may fail to complete follow-up questionnaires because of a relapse or the severity of their condition. The decision on the preferred study type and the particular quality of life measure should be informed by any evidence currently available in the literature, the design of data collection, and the exact requirements of the model that will be used to support resource allocation decisions (e.g. reimbursement)