I've been using linear models (
lm function in R) to determine if the the expression of certain proteins is associated with disease severity. Sometimes the expression of proteins can also correlate with age, so I've been including that in the models as so:
lm(ProteinA ~ DiseaseSeverity + Age, data = mydata)
But where I'm getting confused is in interpreting the output when the entire model is significant but my coefficient of interest is not significant. For instance, consider these results:
(Intercept): p = 0.01438 DiseaseSeverityMild: p = 0.88170 Age: p = 0.00278 F-statistic: 6.418 on 2 and 34 DF, p-value: 0.004319
In this case, the overall model is significant, and the intercept is significant, and age is significant, but
DiseaseSeverity is not significant.
Am I correct in my understanding that I therefore cannot say that ProteinA is a significant correlate of DiseaseSeverity? Or, how would you interpret these results?
Does the fact that the intercept is significant mean anything, either in general or in the context of answering my question of "Is ProteinA expression related to DiseaseSeverity?"?