BACKGROUND: Interference in immunoassays may cause both false-negative and false-positive results. It may be detected using a number of affirmative tests such as reanalysis of certain samples using different assay platforms with known bias, after the addition of blocker antibodies, or assessment of linearity and parallelism following serial doubling dilutions. One should look for interference where it is likely and has high medical impact. Probabilistic Bayesian reasoning is a statistical tool to identify samples where interference is most likely. But when looking for interference where it is likely, do we find it where it has the largest population health consequences? METHODS: We used information theory to quantify the effect of assay interference by calculating the Shannon information content (using logarithms with base 2). We then obtained lower bounds of the population health consequences of a particular test and combined these expressions to get lower bounds of the population health consequences of interference. RESULTS AND CONCLUSION: We suggest that assays having a low frequency of true positives should be the primary target of retesting because: (i) assays with a low frequency of true positives exhibit a high likelihood of interference and (ii) the population health consequences of false-positive results are generally higher for assays with a low frequency of true positives. Finally, we give a worked example having a realistic frequency of interference and test costs. In some immunoassays (e.g., tumour markers), adding a blocker to all tests can be a more cost-efficient mean than retesting positive samples.
BACKGROUND: Interference in immunoassays may cause both false-negative and false-positive results. It may be detected using a number of affirmative tests such as reanalysis of certain samples using different assay platforms with known bias, after the addition of blocker antibodies, or assessment of linearity and parallelism following serial doubling dilutions. One should look for interference where it is likely and has high medical impact. Probabilistic Bayesian reasoning is a statistical tool to identify samples where interference is most likely. But when looking for interference where it is likely, do we find it where it has the largest population health consequences? METHODS: We used information theory to quantify the effect of assay interference by calculating the Shannon information content (using logarithms with base 2). We then obtained lower bounds of the population health consequences of a particular test and combined these expressions to get lower bounds of the population health consequences of interference. RESULTS AND CONCLUSION: We suggest that assays having a low frequency of true positives should be the primary target of retesting because: (i) assays with a low frequency of true positives exhibit a high likelihood of interference and (ii) the population health consequences of false-positive results are generally higher for assays with a low frequency of true positives. Finally, we give a worked example having a realistic frequency of interference and test costs. In some immunoassays (e.g., tumour markers), adding a blocker to all tests can be a more cost-efficient mean than retesting positive samples.