Appropriate statistical methods to derive a cut-off predictive values for a binary outcome

I am in need of statistical advice. My aim is “To derive waist circumference cut-off values predictive of metabolic syndrome among ART-experienced and naïve patients”.

Metabolic syndrome is defined as having 3 or more of the following characteristics: raised Blood pressure, waist circumference, fasting blood sugar, triglycerides and reduced high-density lipoprotein. Currently, there are no abnormal values of waist circumference specific for black Africa, the one used is based on Europid population which I feel might be different.

If I want to answer that objective, does it mean I need to omit waist circumference in the definition of metabolic syndrome and just leave the four characteristics? or I can still include and find the best predictive values of waist circumference for metabolic syndrome.

Maybe I need to first find the normal and abnormal ranges of waist circumference among our population (note that in Africa we still use the Europid values- no data so far specific for our setting) and see how best they can predict MS as compared to that Europid currently being used.

I will really appreciate your guidance, I am kind of confused about which route to take.

Such cutoffs do not exist and it is futile to seek them IMHO. We never see discontinuous relationships.

Thanks Frank. Then how do I find the normal range of waist circumference in my cohort? Currently we are using the Europid cutoff of >=80cm for female and >=94cm for male. I want to see if I generate one specific for my setting.

Such normal ranges exist only in the mind. If you want to define a reference range and just call it the 0.95 quantile interval, then just compute the 0.025 and 0.975 quantiles. If you need to covariate adjust, use quantile regression. Just don’t assign any other meaning to the interval. Its actual use would depend on the nature of any non-standard group you want to apply it to.

Thanks Frank this is of great help. Let me take that route.