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A binomial logistic regression function (Eq. 2) was then used to determine the detection probability (DP) of Ga68-PSMA-11-PET/CT Ga68-PSMA-11-PET/CT Ga68-PSMA-11-PET/CT Ga68-PSMA-11-PET/CTposit.
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The present study utilizes a binomial logistic regression with a fixed lag function, which is very close to a Poisson regression for these large populations, but with the extension of allowing for linear and nonlinear spline functions.
Furthermore, a scoring-function-based binomial logistic regression model for prediction of B. pseudomallei MIP inhibitors with submicromolar affinity is presented.
Data were analyzed using binomial logistic regression modeling designed to detect differential item functioning (DIF).
We used Bayesian binomial logistic regression (BBLR) to update the map showing unripened subsoils for a reclamation area in the west of The Netherlands.
Bayesian binomial logistic regression proved to be a flexible mapping tool but the accuracy gain compared to conventional logistic regression was marginal and may not outweigh the extra modelling and computing effort.
Use of binomial logistic regression analysis.
A binomial logistic regression predicts forest distribution under natural conditions based on terrain variables.
In this study, binomial logistic regression methodology has been applied.
The reliability of the survey was tested using binomial logistic regression analysis.
In order to test the proposed model, a binomial logistic regression was carried out.
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