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Combinations of drugs are now ubiquitous in treating complex diseases such as cancer and HIV due to their potential for enhanced efficacy and reduced side effects. The traditional combination experiments of drugs focus primarily on the dose effects of the constituent drugs. However, with the doses of drugs remaining unchanged, different sequences of drug administration may also affect the efficacy endpoint. Such drug effects shall be called as order effects. The common order‐effect linear models are usually inadequate for analyzing combination experiments due to the nonlinear relationships and complex interactions among drugs. In this article, we propose a random field model for order‐effect modeling. This model is flexible, allowing nonlinearities, and interaction effects to be incorporated with a small number of model parameters. Moreover, we propose a subtle experimental design that will collect good quality data for modeling the order effects of drugs with a reasonable run size. A real‐data analysis and simulation studies are given to demonstrate that the proposed design and model are effective in predicting the optimal drug sequences in administration. [ABSTRACT FROM AUTHOR]

A growing trend in engineering and science is to use multiple computer codes with different levels of accuracy to study the same complex system. Strategies are needed to combine the simulation results obtained at different levels of accuracy to produce an efficient surrogate model for prediction. In this paper, we propose an integral model to borrow as much information as possible from the low‐accuracy experiment. We ignore the Markov property assumed before and model the high‐accuracy experiment based on an integral form of the low‐accuracy experiment. The proposed model is more general thus better predictions are expected. Two explicit forms of some matrices and vectors used in our predictions are given. The effectiveness of the proposed model is illustrated with several examples. [ABSTRACT FROM AUTHOR]

Sliced Latin hypercube designs (SLHDs) achieve maximum stratification in each dimension, but neither the full designs nor their slices can guarantee a good uniformity over the experimental region. Although the uniformity of the full SLHD and that of its slices are related, there is no one-to-one correspondence between them. In this paper, we propose a new uniformity measure for SLHDs by combining the two kinds of uniformity. Based on such a combined uniformity measure, the obtained uniform SLHDs have the design points evenly spread over the experimental region not only for the whole designs but also for their slices. Numerical simulation shows the effectiveness of the proposed uniform SLHDs for computer experiments with both quantitative and qualitative factors. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]