Abstract
It is well-known that optimal designs for logistics regression models depend on the unknown parameter values. In practice, guess values are used as proxies. Thus the actual design implemented is a pseudo optimal design. In this article, we assess whether this problem in optimality from ill-guessed parameter values can be improved by a 2-stage optimal design. We examine the optimal allocation of resources to each stage and how the 2-stage optimal design compares to the single-stage pseudo optimal design for various departures of the guess values from the true parameter values. We restrict our study to A- and D-optimality.
Original language | English |
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Pages (from-to) | 1118-1141 |
Number of pages | 24 |
Journal | Communications in Statistics: Simulation and Computation |
Volume | 49 |
Issue number | 5 |
DOIs | |
State | Published - 3 May 2020 |
Keywords
- 2-Stage design
- A-optimality
- D-optimality
- Dose-response model
- Information matrix
- Logistic regression model