1992Statistics in MedicineRequires access

Is the ‘simple carry‐over’ model useful?

Stephen Senn

Open publisher page 77 citations

Abstract

It is argued that the models usually employed for carry-over effects in cross-over trials are not reasonable. The example of a non-linear dose response for a dose-finding trial arranged in a Williams square is developed to show that if carry-over is present to any appreciable degree the usual statistical models provide no guaranteed protection against its effects. It is concluded that the most reasonably defended assumption about carry-over effects is that no important carry-over has taken place and that, where this assumption cannot be defended, statistical models provide no satisfactory substitute for it.

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It is argued that the models usually employed for carry-over effects in cross-over trials are not reasonable. The example of a non-linear dose response for a dose-finding trial arranged in a Williams square is developed to show that if carry-over is present to any appreciable degree the usual statistical models provide no guaranteed protection against its effects. It is concluded that the most reasonably defended assumption about carry-over effects is that no important carry-over has taken place and that, where this assumption cannot be defended, statistical models provide no satisfactory substitute for it.

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Available abstract

It is argued that the models usually employed for carry-over effects in cross-over trials are not reasonable. The example of a non-linear dose response for a dose-finding trial arranged in a Williams square is developed to show that if carry-over is present to any appreciable degree the usual statistical models provide no guaranteed protection against its effects. It is concluded that the most reasonably defended assumption about carry-over effects is that no important carry-over has taken place and that, where this assumption cannot be defended, statistical models provide no satisfactory substitute for it.

Key concepts: Carry (investment), Simple (philosophy), Statistical model, Computer science, Econometrics, Mathematics, Statistics, Economics

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