A nonmechanistic parametric modeling approach for benchmark dose estimation of event-time data
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A nonmechanistic parametric modeling approach for benchmark dose estimation of event-time data. / Jensen, Signe Marie; Cedergreen, Nina; Kluxen, Felix M; Ritz, Christian.
I: Risk Analysis, Bind 41, Nr. 11, 2021, s. 2081-2093.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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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