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In the patient sample, the impact of the following characteristics on preferences was explored: presenting condition (gastroenterology or rheumatology); level of education (degree level and equivalent or above; or no degree); had a pharmacogenetic test (self-reported); reacted badly to a medicine; member of the family has reacted badly to a medicine; taken azathioprine; reacted badly to azathioprine. In the health-care professional sample, the impact of the following characteristics on preferences was explored: discipline (hospital doctor, GP, pharmacist, nurse); clinical specialty of respondent (gastroenterology, rheumatology, renal medicine, dermatology); used a pharmacogenetic test; reacted badly to a medicine; member of the family has reacted badly to a medicine. These analyses were run separately for the patient and health-care professional sample data and included covariates in Carfilzomib purchase the model as interaction terms of the attribute and characteristics. The goodness of fit for each model with and without these interaction terms was tested using the likelihood ratio test with a P value threshold of statistical significance set at P VX-809 cost between DCEs that have been generated from two Cefaloridine data sources, for example, a sample of patients and health-care professionals, need to take account of differences in unobserved variability between the data sources and take account of the possible effect of this scale parameter [37]. To identify the impact of the scale parameter, step one was to plot the estimated coefficients from each sample against each other on a scatter plot, to visualize whether the differences are purely due to a scaling effect. A strong linear relationship will indicate that any difference in the magnitude of the coefficients is explained by the scale parameter and differences in scale between the data from patients and health-care professionals. The Swait and Louviere [38] test was then used to formally test whether the true parameter coefficients are significantly different. Calculating the marginal rate of substitution (MRS), using a value attribute, was used as an alternative means of overcoming the issue of the scale parameter that does not allow direct comparison of estimated parameters from two data sources. The scale parameter does not affect the ratio of any two coefficients. The MRS was calculated by dividing the estimated parameter coefficient for the attribute by the estimated parameter coefficient for the selected value attribute.