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I have my dataset with different mutations as unit of analysis. These mutations belong to 5 different classes. Also, I have collected, 9 features about these mutations. In other words I have 12 columns:

  • First column: mutation ID
  • Second column: Mutation class
  • Third column to eleven: Features about these mutations (at individual level)
  • Twelve column: Drug resistant/susceptible or binary column.

In addition, I have done a survey of experts, asking them the probability of resistance given each mutation class.

Now, I want to model drug resistant as a function of these 9 features using Bayesian Hierarchical model. I want to take into account those mutation classes and the probabilities from experts as prior information. I don't know if Bayesian Hierarchical model is the right approach. If this is correct, then, how can I parametricize my model. I want to write in my methods section.

Thank you!

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  • $\begingroup$ Please edit the question to limit it to a specific problem with enough detail to identify an adequate answer. $\endgroup$
    – Community Bot
    Mar 19 at 18:15

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