Many statistical multiple integration problems involve integrands that have a dominant peak. In applying numerical methods to solve these problems, statisticians have paid relatively little attention to existing quadrature methods and available software developed in the numerical analysis literature. One reason these methods have been largely overlooked, even though they are known to be more efficient than Monte Carlo for well-behaved problems of modest dimensionality, may be that when applied naively they are poorly suited for peaked-integrand problems. In this paper we use transformations based on "split-t" distributions to allow the integrals to be efficiently computed using a subregion-adaptive numerical integration algorithm. Our split-t distributions are modifications of those suggested by Geweke (1989) and may also be used to define Monte Carlo importance functions. We then compare our approach to Monte Carlo. In the several examples we examine here, we find subregion-adaptive integration to be substantially more efficient than importance sampling.