OBJECTIVES:

Patient-level simulation (PLS) approaches such as discrete event simulation (DES) typically capture important nuances of an individual patient’s profile and history. The benefits in accuracy often must be balanced against a computational trade-off in model run-time, which can be compounded in a probabilistic sensitivity analysis (PSA) of parameter uncertainty. This project sought to investigate whether retail cloud computing solutions could be used to improve the run-time of a DES model.

METHODS:

This project used a cost-effectiveness DES model built in R. Cloud computing was used to explore various running times via parallelisation, which is running an analysis on multiple central processing units (CPUs) simultaneously. Analyses on a laptop utilising one and six CPUs were explored, as well as cloud computing analyses utilising eight, 32 and 128 CPUs. To assess the improvement in run-time the DES model was executed using 1,000 patients for 1,000 PSA runs and the time taken was recorded. The total cost per analysis associated with each cloud computing approach was also recorded.

RESULTS:

When using a laptop with no parallelisation the model took 4.3 hours to complete. This was reduced to 1.6 hours (a 63% reduction on run-time) by using a typical parallelisation approach with six CPUs. Using the cloud computing approach model runs with eight, 32 and 128 CPUs led to run-times of 52.4, 15.7 and 5.5 minutes (reductions of 80%, 94% and 98% respectively). The total cost associated with the cloud computing approaches ranged from $0.35-$0.59 (United States Dollars) per analysis.

CONCLUSIONS:

Since R is amenable to parallelisation it may be a useful software platform for computationally intensive PLS analyses. The significant reduction in model run-time illustrates how cloud-based solutions can provide a considerable and inexpensive improvement of the run-time of PLS, thereby improving their practicality and usefulness.