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Using Patient Experience Data and Discrete Choice Experiment to Assess Values of Drugs

Using Patient Experience Data and Discrete Choice Experiment to Assess Values of Drugs

Furthermore, the ML model can estimate individual-specific preferences from the mean of the parameters within the subpopulation of individual patients who select the same choices from the same choice sets. The model can also estimate the distribution of preferences for the attributes. Preference heterogeneity can be identified from the significant standard deviations of coefficients. From the ML model, the kernel density plots of the distribution of the individual-specific willingness-to-pay (WTP) estimates conditional on observed choices for each of the drug attributes can be developed.12 The kernel density plot shows the proportion of patients for each WTP estimate point. Finally, the individual-specific WTP estimates from each of the drug attributes are added to provide the distribution of the individual-specific WTP estimates or values of the drug.13 In other words, the distribution provides the proportion of patients who are willing to pay for the drug at specific amounts.

CONCLUSIONS

The US healthcare system can benefit from a variety of value assessment approaches. The patient experience value framework approach combines methodologically sound patient experience data and DCEs to assess the value of drugs in a patient-centered way. This method offers advantages over traditional QALY-based approaches and can complement existing value frameworks.

Dr Ngorsuraches is an associate professor in the Department of Health Outcomes Research and Policy, Harrison School of Pharmacy, Auburn University, in Auburn, Alabama.

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