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What does it mean by #sensitivity vs #sensitivity?
what are false positive rate and false negatives?
How are they calculated?
Here's a simple explanation
[Thread] #medtwitter #tweetorial
what are false positive rate and false negatives?
How are they calculated?
Here's a simple explanation
[Thread] #medtwitter #tweetorial
SENSITIVITY aka true positive rate (TPR) = number of people who have the disease that tests positive
low sensitivity tests will have high false negative rate (FNR). aka type II errors = number of people who have the disease but tests negative
low sensitivity tests will have high false negative rate (FNR). aka type II errors = number of people who have the disease but tests negative
SPECIFICITY aka true negative rate (TNR) = Number of people who do NOT have the disease that tests negative.
low specificity tests will have high false positive rate (FPR). aka type I errors = number of people without disease who tests positive
low specificity tests will have high false positive rate (FPR). aka type I errors = number of people without disease who tests positive
A good test rarely misses the thing you are looking for (
sensitivity) and rarely mistakes it for something else (specificity)
How are they calculated?
sensitivity) and rarely mistakes it for something else (specificity)How are they calculated?
Sensitivity : probability (%) that a sample tests positive given the patient has the disease.
Sensitivity = Number of true positive ÷ (true positives + false negative)
or = Number of true positives ÷ Total number of individuals with the illness
Sensitivity = Number of true positive ÷ (true positives + false negative)
or = Number of true positives ÷ Total number of individuals with the illness
Specificity : probability (%) that a sample tests negative given the patient does NOT have the disease.
Specificity = Number of true negatives ÷ (true negatives + false positives)
or = Number of true negatives ÷ Total number of individuals without the illness
Specificity = Number of true negatives ÷ (true negatives + false positives)
or = Number of true negatives ÷ Total number of individuals without the illness
eg: you have 150 people with disease and 400 without.
You run the samples, you have;
Those with disease testing positive (TP) = 144
Those without disease testing negative (FP) = 12
Those without disease testing negative (TN) = 388
Those with disease testing negative (FN)= 6
You run the samples, you have;
Those with disease testing positive (TP) = 144
Those without disease testing negative (FP) = 12
Those without disease testing negative (TN) = 388
Those with disease testing negative (FN)= 6
Sensitivity = 144 / (144 + 6) = 0.96
Specificity = 388 / (388 + 12) = 0.9
Therefore the test is 96% sensitive and 97% specific
Specificity = 388 / (388 + 12) = 0.9
Therefore the test is 96% sensitive and 97% specific
Thats not all..
There's also PPV and NPV that you need to understand, they are NOT the same as sensitivity & specificity although are related.
There's also PPV and NPV that you need to understand, they are NOT the same as sensitivity & specificity although are related.
positive predictive value (PPV) = probability that you have the disease given your test is positive
negative predictive value (NPV) = probability that you DONT have the disease given your test is negative.
This information is more useful for discussing results with a patient.
negative predictive value (NPV) = probability that you DONT have the disease given your test is negative.
This information is more useful for discussing results with a patient.
eg if the PPV of a test is 92%, then if you tested positive, theres a 92% chance that you've got the disease
So basically, the sensitivity and specificity evaluate the test, whereas the PPV and NPV evaluate the results at hand.
So basically, the sensitivity and specificity evaluate the test, whereas the PPV and NPV evaluate the results at hand.
how is it calculated?
PPV = true positive ÷ (true positives + false positives)
or = true positives ÷ samples that tested positive
NPV = true negatives ÷ (true negatives + false negatives)
or = true negatives ÷ samples that tested negative
PPV = true positive ÷ (true positives + false positives)
or = true positives ÷ samples that tested positive
NPV = true negatives ÷ (true negatives + false negatives)
or = true negatives ÷ samples that tested negative
So, how accurate is a test?
Accuracy: ability to differentiate those with and without the disease correctly.
Accuracy= (TP+TN) ÷ (TP+TN+FP+FN)
or = number correctly predicted ÷ total tested
Accuracy: ability to differentiate those with and without the disease correctly.
Accuracy= (TP+TN) ÷ (TP+TN+FP+FN)
or = number correctly predicted ÷ total tested
