I describe a planning methodology for domains with uncertainty in the form of external events that are not completely predictable. Under certain conditions, these events can be modelled as continuous-time Markov chains whose states are characterised by the planner's domain predicates. Planning is goal-directed, but the subgoals are suggested by analysing the utility of the partial plan rather than being simply the open conditions of the operators in the plan, a technique I call ``decision-theoretic subgoaling''. Other planners for uncertain domains can be viewed as performing decision-theoretic subgoaling, which I argue is a useful way to combine AI-based planning and decision theory.