Many epidemiologists are familiar with Rothman's sufficient component cause model. In this paper, I propose a new index for this model, the completion potential index I show that, with proper assumptions (monotonicity, independent competing causes, proportional hazards), completion potentials for various classes of sufficient causes are estimable from routine epidemiologic data (cohort, case-control or time-to-event data). I discuss the advantage of the completion potential index over indices of rate ratio, rate difference, causal-pie weight, population attributable fraction, and attributable fraction within the exposed population. Hypothetical and real data examples are used. The completion potential index proposed here allows better characterization of complex interactive effects of multiple monotonic risk factors.