scholarly journals Additive Dose Response Models: Explicit Formulation and the Loewe Additivity Consistency Condition

2018 ◽  
Vol 9 ◽  
Author(s):  
Simone Lederer ◽  
Tjeerd M. H. Dijkstra ◽  
Tom Heskes
Keyword(s):  
Dose Response ◽  
Consistency Condition ◽  
Response Models ◽  
Explicit Formulation ◽  
Loewe Additivity

scholarly journals Additive Dose Response Models: Defining Synergy

2018 ◽  
Author(s):  
Simone Lederer ◽  
Tjeerd M.H. Dijkstra ◽  
Tom Heskes
Keyword(s):  
High Throughput ◽  
Dose Response ◽  
Antagonistic Effect ◽  
Parametric Approach ◽  
Huge Amount ◽  
Response Models ◽  
Explicit Formulation ◽  
Measured Response ◽  
Loewe Additivity ◽  
Bliss Independence

AbstractIn synergy studies, one focuses on compound combinations that promise a synergistic or antagonistic effect. With the help of high-throughput techniques, a huge amount of compound combinations can be screened and filtered for suitable candidates for a more detailed analysis. Those promising candidates are chosen based on the deviance between a measured response and an expected non-interactive response. A non-interactive response is based on a principle of no interaction, such as Loewe Additivity [Loewe, 1928] or Bliss Independence [Bliss, 1939]. In Lederer et al. [2018a], an explicit formulation of the hitherto implicitly defined Loewe Additivity has been introduced, the so-called Explicit Mean Equation. In the current study we show that this Explicit Mean Equation outperforms the original implicit formulation of Loewe Additivity and Bliss Independence when measuring synergy in terms of the deviance between measured and expected response. Further, we show that a deviance based computation of synergy outper-forms a parametric approach. We show this on two datasets of compound combinations that are categorized into synergistic, non-interactive and antagonistic [Yadav et al., 2015, Cokol et al., 2011].

scholarly journals Additive Dose Response Models: Explicit Formulations and the Loewe Additivity Consistency Condition

2017 ◽  
Author(s):  
Simone Lederer ◽  
Tjeerd M. H. Dijkstra ◽  
Tom Heskes
Keyword(s):  
Mean Squared Error ◽  
Reference Model ◽  
Consistency Condition ◽  
Data Sets ◽  
The Novel ◽  
Response Models ◽  
Reference Models ◽  
Squared Error ◽  
Common Principle ◽  
Loewe Additivity

AbstractHigh-throughput techniques allow for massive screening of drug combinations. To find combinations that exhibit an interaction effect, one filters for promising compound combinations by comparing to a response without interaction. A common principle for no interaction is Loewe Additivity which is based on the assumption that no compound interacts with itself and that doses of both compounds for a given effect are equivalent. For the model to be consistent, the doses of both compounds have to be proportional. We call this restriction the Loewe Additivity Consistency Condition (LACC). We derive explicit and implicit null reference models from the Loewe Additivity principle that are equivalent when the LACC holds. Of these two formulations, the implicit formulation is the known General Isobole Equation [1], whereas the explicit one is the novel contribution. The LACC is violated in a significant number of cases. In this scenario the models make different predictions. We analyze two data sets of drug screening that are non-interactive [2, 3] and show that the LACC is mostly violated and Loewe Additivity not defined. Further, we compare the measurements of the non-interactive cases of both data sets to the theoretical null reference models in terms of bias and mean squared error. We demonstrate that the explicit formulation of the null reference model leads to smaller mean squared errors than the implicit one and is much faster to compute.

scholarly journals Weighted mixed-effects dose–response models for tables of correlated contrasts

2021 ◽  
Vol 21 (2) ◽  
pp. 320-347
Author(s):  
Nicola Orsini
Keyword(s):  
Dose Response ◽  
Response Pattern ◽  
Mixed Effects ◽  
Data Generation ◽  
Measures Of Association ◽  
Response Models ◽  
Response Data ◽  
Hazard Ratios ◽  
Health Related ◽  
Generation Mechanisms

Recognizing a dose–response pattern based on heterogeneous tables of contrasts is hard. Specification of a statistical model that can consider the possible dose–response data-generating mechanism, including its variation across studies, is crucial for statistical inference. The aim of this article is to increase the understanding of mixed-effects dose–response models suitable for tables of correlated estimates. One can use the command drmeta with additive (mean difference) and multiplicative (odds ratios, hazard ratios) measures of association. The postestimation command drmeta_graph greatly facilitates the visualization of predicted average and study-specific dose–response relationships. I illustrate applications of the drmeta command with regression splines in experimental and observational data based on nonlinear and random-effects data-generation mechanisms that can be encountered in health-related sciences.

Development of Dose-Response Models to Predict the Relationship for HumanToxoplasma gondiiInfection Associated with Meat Consumption

Risk Analysis ◽  
2015 ◽  
Vol 36 (5) ◽  
pp. 926-938 ◽  
Author(s):  
Miao Guo ◽  
Abhinav Mishra ◽  
Robert L. Buchanan ◽  
Jitender P. Dubey ◽  
Dolores E. Hill ◽  
...  
Keyword(s):  
Dose Response ◽  
Meat Consumption ◽  
Response Models ◽  
The Relationship

Improving the Development and Use of Biologically Based Dose Response Models (BBDR) in Risk Assessment

2001 ◽  
Vol 7 (5) ◽  
pp. 1091-1120 ◽  
Author(s):  
Robert S. DeWoskin ◽  
Stan Barone ◽  
Harvey J. Clewell ◽  
R. Woodrow Setzer
Keyword(s):  
Risk Assessment ◽  
Dose Response ◽  
Response Models
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