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Cost-effectiveness analysis microsimulation for low- and middle-income countries can guide decision makers in allocating limited health budgets. But in order to compare alternative options, estimates of cost and effect must account for a high level of uncertainty in input parameters (parameter uncertainty) and also include an appropriate amount of stochastic uncertainty. It can be a challenge to incorporate the stochastic uncertainty appropriately, particularly when there is a lot of parameter uncertainty, due to large variance in the quantities of interest (such as incremental cost and health of an intervention scenario as compared to a baseline scenario). We investigated the utility of the common-random-numbers approach to variance reduction in simulation and found it very useful. We found that without variance reduction our intervention erroneously appeared unacceptable at any threshold, but with variance reduced …