Randomized clinical trials testing putative disease-modifying agents in Parkinson's disease (PD) have yielded controversial results that have not influenced evidence-based recommendations for the treatment of PD. We argue that the failure of these clinical trials may be linked to end point-based statistical analyses that must make prior assumptions about the magnitude and the time course of wash-in and wash-out of drug effects. Many of these shortcomings may be avoided with quantitative modeling of the entire time course of the clinical trial and examining evidence for three concomitant processes, disease progression, symptomatic drug effects and disease modifying effects. The power of this approach is illustrated by modeling of DATATOP and ELLDOPA trial data.
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