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Optimal and/or Efficient Two treatment Crossover Designs for Five Carryover Models

Author

Listed:
  • Gondaliya Jigneshkumar

    (Statistics, Gujarat Commerce college, Ellisbridge, Ahmedabad, Gujarat, India)

  • Divecha Jyoti

    (Statistics, Sardar Patel University, Vallabh Vidyanagar, Gujarat, India)

Abstract

Crossover designs robust to changes in carryover models are useful in clinical trials where the nature of carryover effects is not known in advance. The designs have been characterized for being optimal and efficient under no carryover-, traditional-, and, self and mixed carryover- models, however, ignoring the number of subjects, which has significant impact on both optimality and administrative convenience. In this article, adding two more practical models, the traditional, and, self and mixed carryover models having carryover effect only for the new or test treatment, a 5M algorithm is presented. The 5M algorithm based computer code searches all possible two treatment crossover designs under the five carryover models and list those which are optimal and /or efficient to all the five carryover models. The resultant exhaustive list consists of optimal and/or efficient crossover designs in two, three, and four periods, having 4 to 20 subjects of which 24 designs are new optimal for one of the established carryover models, and 34 designs are optimal for newly added models.

Suggested Citation

  • Gondaliya Jigneshkumar & Divecha Jyoti, 2018. "Optimal and/or Efficient Two treatment Crossover Designs for Five Carryover Models," The International Journal of Biostatistics, De Gruyter, vol. 14(2), pages 1-11, November.
    • Handle: RePEc:bpj:ijbist:v:14:y:2018:i:2:p:11:n:4
    DOI: 10.1515/ijb-2018-0001
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