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January 1, 1967Biometrika157 citations

Discrimination between alternative binary response models

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ECElizabeth A. ChambersDCD. R. Cox

Key Points

  • To evaluate the statistical power required to discriminate between logistic and integrated normal response models in three-dose-level experiments.
  • Calculated the power of significance tests testing the null hypothesis of a logistic response curve against a normal alternative, and vice versa.
  • Evaluated optimal dose-level spacing configurations across three experimental dose levels to maximize model discrimination.
  • Logistic and integrated normal curves show close agreement across central ranges and differ primarily in the distribution tails.
  • Optimal dose spacing for model discrimination was determined for three-level experimental designs.
  • Approximately 1000 observations are required to achieve even modest sensitivity in distinguishing between the two models.

Abstract

The logistic and integrated normal binary response curves are known to agree closely except in the tails. For experiments based on three dose levels the power of a signifacance test is found for the null hypothesis that the response curve is ligistic against the alternative that it is normal, and vice versa. From this an appropriate spacing of dose levels for discrimination is found. Approximately 1000 observations are necessary for even modest sensitivity.

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Cite This Study

Chambers et al. (1967) studied this question.

synapsesocial.com/papers/6a0c691e5712c53037e89e93https://doi.org/10.1093/biomet/54.3-4.573
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