Orthogonal array composite designs for drug combination experiments with applications for tuberculosis

Jose Luna, Jessica Jaynes, Hongquan Xu, Weng Kee Wong

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


The aim of this article is to provide an overview of the orthogonal array composite design (OACD) methodology, illustrate the various advantages, and provide a real-world application. An OACD combines a two-level factorial design with a three-level orthogonal array and it can be used as an alternative to existing composite designs for building response surface models. We compare the (Formula presented.) -efficiencies of OACDs relative to the commonly used central composite design (CCD) when there are a few missing observations and demonstrate that OACDs are more robust to missing observations for two scenarios. The first scenario assumes one missing observation either from one factorial point or one additional point. The second scenario assumes two missing observations either from two factorial points or from two additional points, or from one factorial point and one additional point. Furthermore, we compare OACDs and CCDs in terms of (Formula presented.) -optimality for precise predictions. Lastly, a real-world application of an OACD for a tuberculosis drug combination study is provided.

Original languageEnglish
Pages (from-to)3380-3397
Number of pages18
JournalStatistics in Medicine
Issue number17
Publication statusPublished - 2022 Jul 30

All Science Journal Classification (ASJC) codes

  • Epidemiology
  • Statistics and Probability


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