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

Kerns, L

HERO ID

12033148

Reference Type

Journal Article

Year

2018

HERO ID 12033148
In Press No
Year 2018
Title Multiplicity-Adjusted Confidence Limits in Risk Assessment with Quantal Response Data
Authors Kerns, L
Journal Journal of Biopharmaceutical Statistics
Volume 28
Issue 6
Page Numbers 1182-1192
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.
Doi 10.1080/10543406.2018.1452026
Url https://www.ncbi.nlm.nih.gov/pubmed/29543575
Is Certified Translation No
Dupe Override No
Is Public Yes
Keyword Animals; Biostatistics/methods; Carbon Disulfide/toxicity; Coleoptera/drug effects; Computer Simulation; Confidence Intervals; Data Interpretation, Statistical; Dose-Response Relationship, Drug; Insecticides/toxicity; Models, Statistical; Monte Carlo Method; Risk Assessment/statistics & numerical data; Toxicity Tests/statistics & numerical data; Abbott-adjusted log-logistic model; benchmark dose; risk assessment; simultaneous confidence bands; simultaneous inferences