# How do I compute a cutoff based on sensitivity/specificity when the characteristics of my sample is different from the population?

I have a dataset containing the performance of a novel instrument to screen for disease A. The novel instrument uses a scoring system to score the subject to determine if they have disease A. I then proceed to use logistic regression to use the novel instrument and demographic variables such as age and gender to classify them into either having or not having disease A and comparing it with the gold standard result.

Since the prevalence of disease A is different in my sampling and in the actual population, how should I handle this problem? I see a couple of possible solutions:

1. Conduct the logistic regression and use the predicted values for each subject to compute for the sensitivity and specificity of the regression.
2. Conduct the logistic regression in the sample. Sample from my existing sample to create a new sample that corresponds to the population proportion of people with and without disease A. Perform the prediction to compute for the sensitivity and specificity of the regression.

My questions are:

• Should I adopt either strategy (1) or (2)?
• During my search for answers, I came across: Case weighted logistic regression. Following the thread on the mailing list, I see a way to make replicate weights for each subject (via survey package). Why should the proportion (in my case) of people with and without disease A affect the results of the logistic regression? Is the replicate weights method a valid way of overcoming this issue or would strategy (2) work as well?