  The efficacy of an intervention can be assessed by randomizing patients to
different diagnostic tests instead of directly to an intervention and control.
This principle is applied by allocating individuals to intervention if the test
result is positive (or on one side of a threshold) but allocating individuals
to a control if the result is negative (or on the other side of the threshold).
This can also be done with different dichotomizing thresholds for one test. The
frequencies of the outcome in those with each of the four resulting
observations are then used to calculate the risk ratio (RR) for the marginal
probabilities by solving simultaneous equations. This assumes that the RR due
to intervention compared to control is the same in both test groups created by
randomization. The calculations are illustrated by using data from a randomized
controlled trial (RCT) that assessed the efficacy of an angiotensin receptor
blocker (ARB) in lowering the risk of diabetic nephropathy in patients
conditional on urinary albumin excretion rates (AERs). The calculations are
also illustrated with simulated data for assessing the effectiveness of test,
trace and isolation to reduce transmission of the SARS-Cov-2 virus by
randomizing to RT-PCR or LFD tests. This approach allows the probabilities of
outcomes, their RRs and odds ratios (OR) conditional on the results of
covariates to be determined, also suggesting a way forward for natural as
opposed to active randomization.
