Previous reviews of DCEs in oncology have focused mainly on the methodology, such as experiment design, estimation procedures and validity of responses, on the treatment application, or on the preferences with regard to cancer screening. Overall, cancer is a progressive disease that can affect every part of the body by 2012, the burden of cancer had risen to approximately 14 million new cases per year and 8.2 million deaths per year. The total economic cost of cancer is estimated to be €126 billion in the European Union. Within this research, attention is focused on DCE studies investigating patients’ preferences for cancer treatment, as early death and disability caused by cancer have the highest total economic burden worldwide. The importance of processes and costs in healthcare, however, are investigated less often. It is known that health-related outcomes are important decision criteria for patients, clinicians, policy makers and payers in medical decision-making processes.
Attributes chosen to describe alternatives within DCEs can be categorised, overall, into three main categories: (1) outcome attributes such as effectiveness or adverse effects (2) process attributes, such as the mode of administration or involvement in clinical decision-making and (3) cost attributes. The identification and selection of attributes and levels are fundamentally important to obtaining valid results, and a proper selection and descriptions are required. extent of drug effectiveness, types of adverse effects or frequency of dosage). In DCEs, respondents are asked to make choices among hypothetical alternatives that are described by systematically varying attribute levels (e.g. The feasibility of the DCE method has already been investigated by several HTA agencies, including the German Institute for Quality and Efficiency in Health Care (IQWiG ), and an approval decision supported by data from a DCE was taken by the US Food and Drug Administration (FDA). The importance of attributes always depends on the other attributes included in a DCE and on the range of levels included for an attribute.
A DCE is suitable for assessing the relative importance of attributes and levels, and for calculating trade-offs between them. Among stated preferences methods, discrete choice experiment (DCE), a specific form of conjoint analysis, has been used extensively to elicit preferences in healthcare. In contrast, revealed patient preferences, which rely on observed data, are difficult to investigate and thus are rarely used in healthcare. By using surveys to elicit patient preferences for characteristics of hypothetical treatments in an experimental framework, stated preference methods enable the assessment the importance of attributes.
Overall, preference elicitation methods can be divided into ‘revealed’ and ‘stated’ preference methods. In order to uncover patients’ preferences, several choices have to be made with regard to the methods of preference elicitation. Furthermore, those preferences can be useful in designing and evaluating healthcare programmes. Matching healthcare policy with patient preferences might lead to the improved effectiveness of healthcare interventions by, for example, improving the adoption of and adherence to clinical treatments and public health programmes. At the same time, taking patient preferences into account is seen as increasingly important, as patients are the payers and consumers of health technologies and services. In Germany, for instance, a reform of the pharmaceutical market (AMNOG ) has been introduced in order to manage the costs of pharmaceuticals. Rising expenses in healthcare give greater importance to the evaluation of interventions, financing and service delivery, which together entail the valuation of healthcare and health outcomes. As the population ages, expenditures are increasing, particularly in oncology care, and the efficient allocation of scarce resources is a key challenge for both policy makers and healthcare professionals.
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