Covid-19 Antibody Test Optimiser
Specificity, positive predictive value and validation statistics in the context of CoViD-19
Sebastian M Richter, PhD CANDOR Bioscience GmbH, Wangen, Germany.
During the CoViD-19 pandemic one can read and hear a lot about sensitivity and specificity, less about positive and negative predictive values and - unfortunately too little - also about exact validation parameters and their statistical evaluation.
However, more and more voices criticise the currently available antibody detection tests due to insufficient performance.
Here we would like to explain what the key parameters of a test procedure are and why specificity is of crucial importance in the context of a pandemic:
Specificity: probability that a test procedure correctly identifies a sample lacking the target analyte as negative (true negative rate)
Sensitivity: probability that a test procedure correctly identifies a sample containing the target analyte as positive (true positive rate)
Positive predictive value: Probability that a sample tested positive actually contains the target analyte, or that a person with a positive test result is actually sick.
Negative predictive value: The probability that a sample tested negative actually does not contain the target analyte, or that a person with a negative test result is actually healthy.
Specificity, positive predictive value and validation statistics in the context of CoViD-19
The Covid-19 antibody test challenge
Dr. Tobias Polifke & Dr. Peter Rauch, CANDOR Bioscience GmbH, Wangen, Germany
In the current situation of the Covid-19 pandemic, reliable IgG antibody tests are urgently needed all around the world. Virologists caution against cross-reactivities that could potentially lead to false positives.
Using ELISAs as an example, we discuss the technical requirements for the development of truly dependable immunological testing methods, since the cross-reactivities debated by many virologists constitute only a small fraction of the actual challenges.