Objectives: It is widely thought that correlations between screening markers will tend to degrade screening performance. We performed a computer simulation study to investigate the quantitative effect of correlations between two markers on screening performance, using prenatal screening for Down's syndrome as an example, although the results apply generally.
Methods: Monte Carlo simulation was used to generate values of two hypothetical markers, A and B, in 1000 affected and 1000 unaffected pregnancies. The means, standard deviations and correlations of A and B were varied in five different examples.
Results: If markers A and B are, on average, higher in affected than unaffected pregnancies and each marker, individually, has the same detection rate for a given false-positive rate (i.e. the same screening performance), then the screening performance of A and B together tends to decrease as A and B become more positively correlated with each other (within affected or unaffected categories) and tends to increase as A and B become more negatively correlated. If A is, on average, higher in affected pregnancies and B is, on average, lower in affected pregnancies (but again each marker has the same screening performance), the opposite pattern is observed; screening performance increases as A and B become more positively correlated and screening performance decreases as they become more negatively correlated. If A and B have unequal screening performances, modest correlations between A and B have little effect on the screening performance of A and B together, but when the correlations are strong whether positive or negative (with r values greater than about 0.45 or less than −0.45) screening performance progressively increases.
Conclusion: Correlations between screening markers considered separately in affected and unaffected pregnancies can either decrease or increase screening performance. In practice, these effects are usually modest, because most screening markers are not highly correlated with each other and the effects become important only with strong correlations, whether positive or negative.
