A nonmechanistic parametric modeling approach for benchmark dose estimation of event-time data

Research output: Contribution to journal › Journal article › Research › peer-review

Standard

A nonmechanistic parametric modeling approach for benchmark dose estimation of event-time data. / Jensen, Signe Marie; Cedergreen, Nina; Kluxen, Felix M; Ritz, Christian.

In: Risk Analysis, Vol. 41, No. 11, 2021, p. 2081-2093.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Jensen, SM, Cedergreen, N, Kluxen, FM & Ritz, C 2021, 'A nonmechanistic parametric modeling approach for benchmark dose estimation of event-time data', Risk Analysis, vol. 41, no. 11, pp. 2081-2093. https://doi.org/10.1111/risa.13708

APA

Jensen, S. M., Cedergreen, N., Kluxen, F. M., & Ritz, C. (2021). A nonmechanistic parametric modeling approach for benchmark dose estimation of event-time data. Risk Analysis, 41(11), 2081-2093. https://doi.org/10.1111/risa.13708

Vancouver

Jensen SM, Cedergreen N, Kluxen FM, Ritz C. A nonmechanistic parametric modeling approach for benchmark dose estimation of event-time data. Risk Analysis. 2021;41(11):2081-2093. https://doi.org/10.1111/risa.13708

Author

Jensen, Signe Marie ; Cedergreen, Nina ; Kluxen, Felix M ; Ritz, Christian. / A nonmechanistic parametric modeling approach for benchmark dose estimation of event-time data. In: Risk Analysis. 2021 ; Vol. 41, No. 11. pp. 2081-2093.

Bibtex

@article{499cbcaa245644f9abc53b5c2d2011ed,
title = "A nonmechanistic parametric modeling approach for benchmark dose estimation of event-time data",
abstract = "We propose benchmark dose estimation for event-time data, using a two-step approach. This approach avoids estimation of complex models and has been previously shown to give robust results for summarizing relevant parameters for risk assessment. In the first step, the probability of the event of interest to occur (in a certain time interval) is described as a function of time, resulting in an event-time model; such a model is fitted allowing an individual curve for each dose, and relevant estimates are extracted. In the second step, a dose-response model is fitted to the estimates of t50 obtained from the event-time model in the first step. Given a predefined benchmark response, the benchmark dose is then estimated from the resulting model. This novel approach is demonstrated in two examples. Our application of the time-to-event model showed a gain in power compared to the traditional analysis of end-of-study summary data.",
keywords = "Faculty of Science, Hazard characterization, Risk assessment, Survival analysis, Temperature stress, α-cypermethrin",
author = "Jensen, {Signe Marie} and Nina Cedergreen and Kluxen, {Felix M} and Christian Ritz",
note = "CURIS 2021 NEXS 051",
year = "2021",
doi = "10.1111/risa.13708",
language = "English",
volume = "41",
pages = "2081--2093",
journal = "Risk Analysis",
issn = "0272-4332",
publisher = "Wiley-Blackwell",
number = "11",

}

RIS

TY - JOUR

T1 - A nonmechanistic parametric modeling approach for benchmark dose estimation of event-time data

AU - Jensen, Signe Marie

AU - Cedergreen, Nina

AU - Kluxen, Felix M

AU - Ritz, Christian

N1 - CURIS 2021 NEXS 051

PY - 2021

Y1 - 2021

N2 - We propose benchmark dose estimation for event-time data, using a two-step approach. This approach avoids estimation of complex models and has been previously shown to give robust results for summarizing relevant parameters for risk assessment. In the first step, the probability of the event of interest to occur (in a certain time interval) is described as a function of time, resulting in an event-time model; such a model is fitted allowing an individual curve for each dose, and relevant estimates are extracted. In the second step, a dose-response model is fitted to the estimates of t50 obtained from the event-time model in the first step. Given a predefined benchmark response, the benchmark dose is then estimated from the resulting model. This novel approach is demonstrated in two examples. Our application of the time-to-event model showed a gain in power compared to the traditional analysis of end-of-study summary data.

AB - We propose benchmark dose estimation for event-time data, using a two-step approach. This approach avoids estimation of complex models and has been previously shown to give robust results for summarizing relevant parameters for risk assessment. In the first step, the probability of the event of interest to occur (in a certain time interval) is described as a function of time, resulting in an event-time model; such a model is fitted allowing an individual curve for each dose, and relevant estimates are extracted. In the second step, a dose-response model is fitted to the estimates of t50 obtained from the event-time model in the first step. Given a predefined benchmark response, the benchmark dose is then estimated from the resulting model. This novel approach is demonstrated in two examples. Our application of the time-to-event model showed a gain in power compared to the traditional analysis of end-of-study summary data.

KW - Faculty of Science

KW - Hazard characterization

KW - Risk assessment

KW - Survival analysis

KW - Temperature stress

KW - α-cypermethrin

U2 - 10.1111/risa.13708

DO - 10.1111/risa.13708

M3 - Journal article

C2 - 33533082

VL - 41

SP - 2081

EP - 2093

JO - Risk Analysis

JF - Risk Analysis

SN - 0272-4332

IS - 11

ER -

ID: 256270471