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      Sample Size Requirements for Discrete-Choice Experiments in Healthcare: a Practical Guide

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          Abstract

          Discrete-choice experiments (DCEs) have become a commonly used instrument in health economics and patient-preference analysis, addressing a wide range of policy questions. An important question when setting up a DCE is the size of the sample needed to answer the research question of interest. Although theory exists as to the calculation of sample size requirements for stated choice data, it does not address the issue of minimum sample size requirements in terms of the statistical power of hypothesis tests on the estimated coefficients. The purpose of this paper is threefold: (1) to provide insight into whether and how researchers have dealt with sample size calculations for healthcare-related DCE studies; (2) to introduce and explain the required sample size for parameter estimates in DCEs; and (3) to provide a step-by-step guide for the calculation of the minimum sample size requirements for DCEs in health care.

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          The online version of this article (doi:10.1007/s40271-015-0118-z) contains supplementary material, which is available to authorized users.

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          Constructing Efficient Stated Choice Experimental Designs

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            Is Open Access

            Policy interventions that attract nurses to rural areas: a multicountry discrete choice experiment

            OBJECTIVE: To evaluate the relative effectiveness of different policies in attracting nurses to rural areas in Kenya, South Africa and Thailand using data from a discrete choice experiment (DCE). METHODS: A labelled DCE was designed to model the relative effectiveness of both financial and non-financial strategies designed to attract nurses to rural areas. Data were collected from over 300 graduating nursing students in each country. Mixed logit models were used for analysis and to predict the uptake of rural posts under different incentive combinations. FINDINGS: Nurses' preferences for different human resource policy interventions varied significantly between the three countries. In Kenya and South Africa, better educational opportunities or rural allowances would be most effective in increasing the uptake of rural posts, while in Thailand better health insurance coverage would have the greatest impact. CONCLUSION: DCEs can be designed to help policy-makers choose more effective interventions to address staff shortages in rural areas. Intervention packages tailored to local conditions are more likely to be effective than standardized global approaches.
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              Sample size requirements for stated choice experiments

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                Author and article information

                Contributors
                +31-10-7043954 , e.debekker@erasmusmc.nl
                Journal
                Patient
                Patient
                The Patient
                Springer International Publishing (Cham )
                1178-1653
                1178-1661
                1 March 2015
                1 March 2015
                2015
                : 8
                : 5
                : 373-384
                Affiliations
                [ ]Department of Public Health, Erasmus MC, University Medical Centre Rotterdam, PO Box 2040, 3000 CA Rotterdam, The Netherlands
                [ ]Department of Business Economics, Erasmus University, Rotterdam, The Netherlands
                [ ]Department of Health Economics, Policy and Law, Erasmus University, Rotterdam, The Netherlands
                Article
                118
                10.1007/s40271-015-0118-z
                4575371
                25726010
                b1cf86dd-7430-4298-8f77-37c11b5b0c0f
                © The Author(s) 2015

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.

                History
                Categories
                Practical Application
                Custom metadata
                © Springer International Publishing Switzerland 2015

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