Arijit Ganguli

Comparison of FACT- and EQ-5D–Based Utility Scores in Cancer

OBJECTIVE Although utility-based algorithms have been developed for the Functional Assessment of Cancer Therapy (FACT), their properties are not well known compared with those of generic utility measures such as the EQ-5D. Our objective was to compare EQ-5D and FACT preference-based scores in cancer patients. METHODS A retrospective analysis was conducted on cross-sectional data collected from 472 cancer patients who completed both FACT-General and the EQ-5D. Preference-based scores were calculated by using published scoring functions for the EQ-5D (Dolan P. Modeling valuations for EuroQol health states. Med Care 1997;35:1095-108; Shaw JW, Johnson JA, Coons SJ. US valuation of the EQ-5D health states: development and testing of the D1 valuation model. Med Care 2005;43:203-20) and FACT (Dobrez D, Cella D, Pickard AS, et al. Estimation of patient preference-based utility weights from the Functional Assessment of Cancer Therapy-General. Value Health 2007;10:266-72; Kind P, Macran S. Eliciting social preference weights for Functional Assessment of Cancer Therapy-Lung health states. Pharmacoeconomics 2005;23:1143-53; Cheung YB, Thumboo J, Gao F, et al. Mapping the English and Chinese versions of the Functional Assessment of Cancer Therapy-General to the EQ-5D utility index. Value Health 2009;12:371-6). Scores were compared on the basis of clinical severity by using Eastern Cooperative Oncology Group performance status ratings by physicians and patients. Relative efficiency of each scoring function was examined by using ratios of F statistics. RESULTS Mean scores for the overall cohort were lowest when using Kind and Macrans FACT UK societal algorithm (0.55, SD 0.09) and highest when using Dobrez et al.s FACT US patient algorithm (0.83, SD 0.08). Mean difference scores associated with clinical severity, when extrapolated to quality-adjusted life-years (QALYs), had a range of 0.18 QALYs gained using FACT (Kind and Macran) to 0.45 QALYs gained using the EQ-5D (Dolan). However, relative efficiencies suggested that FACT (Kind and Macran) scores may provide greater statistical power to detect significant differences based on clinical severity. CONCLUSIONS We found important differences in utilities scores estimated by each algorithm, with FACT-based algorithms tending to underestimate the QALY benefit compared with algorithms based on the EQ-5D. These differences highlight some of the challenges in using disease-specific preference-based measures for decision making despite potentially more relevant disease-specific content.

A Checklist for Ascertaining Study Cohorts in Oncology Health Services Research Using Secondary Data: Report of the ISPOR Oncology Good Outcomes Research Practices Working Group

OBJECTIVES The ISPOR Oncology Special Interest Group formed a working group at the end of 2010 to develop standards for conducting oncology health services research using secondary data. The first mission of the group was to develop a checklist focused on issues specific to selection of a sample of oncology patients using a secondary data source. METHODS A systematic review of the published literature from 2006 to 2010 was conducted to characterize the use of secondary data sources in oncology and inform the leadership of the working group prior to the construction of the checklist. A draft checklist was subsequently presented to the ISPOR membership in 2011 with subsequent feedback from the larger Oncology Special Interest Group also incorporated into the final checklist. RESULTS The checklist includes six elements: identification of the cancer to be studied, selection of an appropriate data source, evaluation of the applicability of published algorithms, development of custom algorithms (if needed), validation of the custom algorithm, and reporting and discussions of the ascertainment criteria. The checklist was intended to be applicable to various types of secondary data sources, including cancer registries, claims databases, electronic medical records, and others. CONCLUSIONS This checklist makes two important contributions to oncology health services research. First, it can assist decision makers and reviewers in evaluating the quality of studies using secondary data. Second, it highlights methodological issues to be considered when researchers are constructing a study cohort from a secondary data source.

Treatment pattern by hormone receptors and HER2 status in patients with metastatic breast cancer in the UK, Germany, France, Spain and Italy (EU-5): results from a physician survey

BACKGROUND The differences in country-specific treatment patterns across Europe for metastatic breast cancer (mBC) patients have not been extensively studied. This study compared the treatment choices in aggregate, as well as by biomarker status, between various lines of therapy in clinical practice in the EU-5 countries among newly diagnosed mBC patients. MATERIALS & METHODS The IMS LifeLink™ Oncology Analyzer database, based on surveys of practicing oncologists, was used to identify mBC patients aged ≥21 years. In this database, sample-level data are projected to national-level estimates for each country using a sample projection technique. RESULTS The prevalence of hormone receptors (71-74%) is quite similar across different countries, while HER2 overexpression varies from 22 (France) to 34% (Italy); chemotherapy combined with HER2-targeted medicine was the mainstay of treatment for HER2(+) patients. The use of HER2-targeted medicine and bevacizumab greatly varied: while they were most frequently used in France, they were least frequently used in the UK. Fewer treatment options existed for triple-negative patients and patients with HER2(+) disease following trastuzumab treatment. Chemotherapy was the treatment choice for triple-negative patients, as these patients do not respond to hormonal therapy and HER2-targeted medicine. CONCLUSION This study found that, while a trastuzumab-based regimen is the preferred option for treating HER2(+) mBC patients in the EU-5, variations in this personalized medicine approach exist between different EU-5 countries. However, fewer treatment options exist for triple-negative and HER2(+) patients after trastuzumab treatment, highlighting the unmet need for these patient subgroups.