Episode 9, Breast Cancer: Mammogram Screening Show Notes

Number needed to invite or NNI is a term that we discussed for mammogram screening, referencing the number of women needed to be invited to mammograms to save one life.  The invitation refers to letters or other forms of invitations for women to get a mammogram.  To calculate the number needed to invite, you would compare the difference in mortality between women invited and those not invited to the Absolute Risk Reduction (ARR) (E.g. if 1% of women who don't receive invitations die from breast cancer, and .5% of women who receive invitations die from breast cancer; then the ARR is .5% or .005.  To calculate NNI, you do 1/ARR or 1/.005= 200 women need to be invited to save one life.  (FYI all numbers here are made up examples, the actually NNI is 1900 women)


Number needed to treat or NNT is a term similar to NNI, only instead of invitations it is treatment.  In this case the treatment is obtaining a mammogram.  The calculations are the same (1/ARR)


True Positive - the test shows positive and the patient actually has the condition


False Positive - the test shows positive but the patient does not have the condition


True Negative - the test shows negative and the patient does not have the condition


False Negative - the test shows negative but the patient does have the condition (what you really want to avoid in screening tests)


Sensitivity is the number of true positives divided by the true positives plus false negatives TP/(TP+FN).  You can think of this as out of all the people who actually have breast cancer, what proportion of them did you test correctly identify as negative.  A good screening test is very sensitive because the goal is to pick up all the early signs.


Specificity is the number of true negatives divided by the true negatives plus false positives.  You can think of this as out of all the people that were negative, what proportion did your test correctly identify as negative.


Positive Predictive Value is the number of true positives divided by true positives plus false positives TP/(TP+FP).  You can think of it as the proportion of positives that were actually true.


Negative Predictive Value is the number of true negatives divided by true negatives plus false negatives TN/(TN+FN).  You can think of it as the proportion of negatives that were actually true.  This was used in reference to MRI scans.  They have a high NPV which means that there is a high chance that any negative screen means the patient does not have breast cancer.