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. 2018;28(6):1182-1192.
doi: 10.1080/10543406.2018.1452026. Epub 2018 Mar 15.

Multiplicity-Adjusted Confidence Limits in Risk Assessment With Quantal Response Data

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Multiplicity-Adjusted Confidence Limits in Risk Assessment With Quantal Response Data

Lucy Kerns. J Biopharm Stat. .

Abstract

In risk assessment, it is often desired to make inferences on the risk at certain low doses or on the dose(s) at which a specific benchmark risk (BMR) is attained. At times, [Formula: see text] dose levels or BMRs are of interest, and some form of multiplicity adjustment is necessary to ensure a valid [Formula: see text] simultaneous inference. Bonferroni correction is often employed in practice for such purposes. Though relative simple to implement, the Bonferroni strategy can suffer from extreme conservatism (Nitcheva et al., 2005; Al-Saidy et al., 2003). Recently, Kerns (2017) proposed the use of simultaneous hyperbolic and three-segment bands to perform multiple inferences in risk assessment under Abbott-adjusted log-logistic model with the dose level constrained to a given interval. In this paper, we present and compare methods for deriving multiplicity-adjusted upper limits on extra risk and lower bounds on the benchmark dose under Abbott-adjusted log-logistic model. Monte Carlo simulations evaluate the characteristics of the simultaneous limits. An example is given to illustrate the use of the methods.

Keywords: Abbott-adjusted log-logistic model; benchmark dose; risk assessment; simultaneous confidence bands; simultaneous inferences.

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