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Designing choice experiments with many attributes. An application to setting priorities for orthopaedic waiting lists
Author(s) -
Witt Julia,
Scott Anthony,
Osborne Richard H.
Publication year - 2009
Publication title -
health economics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.55
H-Index - 109
eISSN - 1099-1050
pISSN - 1057-9230
DOI - 10.1002/hec.1396
Subject(s) - pooling , flexibility (engineering) , context (archaeology) , computer science , quality (philosophy) , task (project management) , explanatory power , operations research , econometrics , risk analysis (engineering) , actuarial science , statistics , medicine , mathematics , artificial intelligence , economics , paleontology , philosophy , management , epistemology , biology
The aim of this paper is to undertake a discrete choice experiment using a ‘blocked attribute’ design. To date in the health economics literature, most discrete choice experiments have used only a relatively small number of attributes due to concerns about task complexity, non‐compensatory decision rules, simplicity of experimental designs, and the costs of surveys. This may lead to omitted variable bias and reduced explanatory power when attributes have been pre‐selected from a longer list. There may be situations where it is desirable to include a longer list of attributes, such as attaching weights to quality‐of‐life instruments to obtain single index scores. We examine this issue in the context of attaching weights to a disease‐specific quality‐of‐life instrument used to prioritise patients on orthopaedic waiting lists in Victorian hospitals. Eleven attributes are allocated across three separate experimental designs and the data pooled for analysis. Pooling is justified given the specific context of the study, including attempts to minimise the effect of unobserved heterogeneity across the three models when designing the study and collecting data. Blocked attribute designs may offer flexibility to researchers when it is not possible or desirable to reduce the number of attributes. Copyright © 2008 John Wiley & Sons, Ltd.