For diseases assessed by disease symptoms it is difficult to distinguish whether interventions slow, stabilize, or reverse the disease, or only reduce symptoms. For example, when testing glucose-lowering drugs for delaying or preventing type 2 diabetes, reduced rates of diabetes diagnoses based on glycemic values do not directly answer this question, because glucose lowering reduces this surrogate without necessarily benefiting prediabetic individuals. A washout could evaluate whether effects persist after eliminating any direct glucose lowering. Several trials analyzed cumulative diabetes diagnoses, from a treatment period including a washout in not-yet-diagnosed subjects. This approach is severely biased, as demonstrated by simulations, because different misclassification errors occur unequally on drug and placebo as a result of the glucose-lowering effect during active treatment and the variability of glycemic values. An alternative is to analyze continuous end-of-washout values for all patients. This requires an imputation of glycemic values after diabetes diagnosis, which can no longer be observed without distortion by physician intervention. Valid imputation is possible, because the known diagnostic criteria lead to a missing at random situation. Trials with a washout in all subjects or delayed start designs are more efficient than current trial designs, and also minimize reliance on data imputation.