Model robust designs for survival trials
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Date
2016
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Abstract
The exponential-based proportional hazards model is often assumed in time-
to-event experiments but may only approximately hold. We consider deviations
in different neighbourhoods of this model that include other widely used paramet-
ric proportional hazards models and we further assume that the data are subject
to censoring. Minimax designs are then found explicitly based on criteria corre-
sponding to classical c- and D-optimality. We provide analytical characterisations
of optimal designs which, unlike optimal designs for related problems in the litera-
ture, have finite support and thus avoid the issues of implementing a density-based
design in practice. Finally, our designs are compared with the balanced design that
is traditionally used in practice, and recommendations for practitioners are given.
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Keywords
proportional hazards models, Type-I censoring, c-optimality, D-optimality, minimax optimal designs