I am trying to create a statistical model for my research. I am dealing with a surgery that has the chance of causing damage to the patient. I have found 5 signs, which I believe are indicators that the surgery will fail. Both the signs and the result of the surgery are binary. I am trying to create a model that will tell me the probability of surgery failing given that 1-5 of the signs are present. I was thinking of using multinomial logistic regression to create my model. Is there better distribution to use to create my model? Any other thoughts? Thank you for your help!

  • $\begingroup$ Please consider changing the title of this question to something more descriptive. $\endgroup$ – deemel Jul 2 '19 at 11:15

So it appears that the result of surgery has been over-simplified down to a 0/1 outcome, meaning that a much larger sample size will be required in order to build a reliable model, e.g., at least a few hundred surgical failures. The choice of a model for binary Y is the binary logistic regression model. Multinomial logistic regression would be used if Y were categorical with more than 2 unordered possible values. You can use the 5 predictors in the binary logistic model without assuming they are equally important, i.e., without just counting the number present.

To do a credible job will require a large amount of background reading. I suggest you start by reading Ewout Steyerberg's Clinical Prediction Models book as well as introductory papers or books on logistic regression.

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