I am planning to develop a prognostic model that would identify a particular group of head neck cancer patients who will do better if chemotherapy is added to standard radiation therapy. The data for the patients in question is derived from a randomized controlled trial. I have developed a prognostic model following the methods proposed by Dr. Harrell and Dr. Steyerberg and several posts in Cross Validated. The model was derived in patients who received radiotherapy alone and on application on patients receiving chemotherapy along with radiation it shows that the outcomes are different. I have used the median value of the prognostic index to divide my patient population into a group with good prognosis and a group with poor prognosis. I can see that the addition of chemotherapy makes a large difference in patients with poor prognosis but not in patients with good prognosis.
What I want to know however is how to determine the optimal cutoff for the prognostic index at which this difference becomes large enough that the physician will prefer to add chemotherapy (primarily as giving it in all patients is associated with the issues of excess toxicity with little additional benefit). One way I have thought of is deriving the Number needed to treat for each cutpoint - for each value of cutpoint calculate the benefit that addition of chemotherapy provides in the poor prognostic group and then calculate the NNT. Then use a predefined NNT threshold to determine the cutoff for the prognostic index. This, in turn, can be used to derive the optimal cutoff point on the nomogram that I will develop using the prognostic model.
My question is if this approach is a sound and if a better approach is available?