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The efficacy of an intervention can be assessed by randomizing patients todifferent diagnostic tests instead of directly to an intervention and control.This principle is applied by allocating individuals to intervention if the testresult is positive (or on one side of a threshold) but allocating individualsto 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. Thefrequencies of the outcome in those with each of the four resultingobservations are then used to calculate the risk ratio (RR) for the marginalprobabilities by solving simultaneous equations. This assumes that the RR dueto intervention compared to control is the same in both test groups created byrandomization. The calculations are illustrated by using data from a randomizedcontrolled trial (RCT) that assessed the efficacy of an angiotensin receptorblocker (ARB) in lowering the risk of diabetic nephropathy in patientsconditional on urinary albumin excretion rates (AERs). The calculations arealso illustrated with simulated data for assessing the effectiveness of test,trace and isolation to reduce transmission of the SARS-Cov-2 virus byrandomizing to RT-PCR or LFD tests. This approach allows the probabilities ofoutcomes, their RRs and odds ratios (OR) conditional on the results ofcovariates to be determined, also suggesting a way forward for natural asopposed to active randomization.

The probabilities of an outcome on intervention and control can be  estimated by randomizing subjects to different testing strategies, required  for assessing diagnostic tests, test trace and isolation and for natural  randomisation