Catherine Beauchemin1, Marie-Ève Lapierre2, Nathalie Letarte2,3, Louise Yelle4, Jean Lachaine2. 1. Faculty of Pharmacy, University of Montreal, P.O. Box 6128, Station Centre-ville, Montreal, QC, H3C 3J7, Canada. catherine.beauchemin@umontreal.ca. 2. Faculty of Pharmacy, University of Montreal, P.O. Box 6128, Station Centre-ville, Montreal, QC, H3C 3J7, Canada. 3. Department of Pharmacy, Notre-Dame Hospital, Centre Hospitalier de l'Université de Montréal (Montreal University Health Centre), University of Montreal, Montreal, QC, Canada. 4. Department of Medicine, Notre-Dame Hospital, Centre Hospitalier de l'Université de Montréal (Montreal University Health Centre), University of Montreal, Montreal, QC, Canada.
Abstract
OBJECTIVES: This study assessed the use of intermediate endpoints in the economic evaluation of new treatments for advanced cancer and the methodological approaches adopted when overall survival (OS) data are unavailable or of limited use. METHODS: A systematic literature review was conducted to identify economic evaluations of treatments for advanced cancer published between 2003 and 2013. Cost-effectiveness and cost-utility analyses expressed in cost per life-year gained and cost per quality-adjusted life-year using an intermediate endpoint as an outcome measure were eligible. Characteristics of selected studies were extracted and comprised population, treatment of interest, comparator, line of treatment, study perspective, and time horizon. Use of intermediate endpoints and methods adopted when OS data were lacking were analyzed. RESULTS: In total, 7219 studies were identified and 100 fulfilled the eligibility criteria. Intermediate endpoints mostly used were progression-free survival and time to progression, accounting for 92 % of included studies. OS data were unavailable for analysis in nearly 25 % of economic evaluations. In the absence of OS data, studies most commonly assumed an equal risk of death for all treatment groups. Other methods included use of indirect comparison based on numerous assumptions, use of a proxy for OS, consultation with clinical experts, and use of published external information from different treatment settings. CONCLUSION: Intermediate endpoints are widely used in the economic evaluation of new treatments for advanced cancer in order to estimate OS. Currently, different methods are used in the absence of suitable OS data and the choice of an appropriate method depends on many factors including the data availability.
OBJECTIVES: This study assessed the use of intermediate endpoints in the economic evaluation of new treatments for advanced cancer and the methodological approaches adopted when overall survival (OS) data are unavailable or of limited use. METHODS: A systematic literature review was conducted to identify economic evaluations of treatments for advanced cancer published between 2003 and 2013. Cost-effectiveness and cost-utility analyses expressed in cost per life-year gained and cost per quality-adjusted life-year using an intermediate endpoint as an outcome measure were eligible. Characteristics of selected studies were extracted and comprised population, treatment of interest, comparator, line of treatment, study perspective, and time horizon. Use of intermediate endpoints and methods adopted when OS data were lacking were analyzed. RESULTS: In total, 7219 studies were identified and 100 fulfilled the eligibility criteria. Intermediate endpoints mostly used were progression-free survival and time to progression, accounting for 92 % of included studies. OS data were unavailable for analysis in nearly 25 % of economic evaluations. In the absence of OS data, studies most commonly assumed an equal risk of death for all treatment groups. Other methods included use of indirect comparison based on numerous assumptions, use of a proxy for OS, consultation with clinical experts, and use of published external information from different treatment settings. CONCLUSION: Intermediate endpoints are widely used in the economic evaluation of new treatments for advanced cancer in order to estimate OS. Currently, different methods are used in the absence of suitable OS data and the choice of an appropriate method depends on many factors including the data availability.
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