Random effects logistic regression model for default prediction of technology credit guarantee fund

2007 ◽  
Vol 183 (1) ◽  
pp. 472-478 ◽  
Author(s):  
So Young Sohn ◽  
Hong Sik Kim
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Random Effects ◽  
Logistic Regression Model ◽  
Credit Guarantee ◽  
Default Prediction

Factors related to the prevalence of pathogenicYersinia enterocoliticaon pig farms

2011 ◽  
Vol 139 (12) ◽  
pp. 1919-1927 ◽  
Author(s):  
S. E. VIRTANEN ◽  
L. K. SALONEN ◽  
R. LAUKKANEN ◽  
M. HAKKINEN ◽  
H. KORKEALA
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Statistical Analysis ◽  
Regression Model ◽  
Random Effects ◽  
Yersinia Enterocolitica ◽  
Logistic Regression Model ◽  
Protective Factor ◽  
Organic Production ◽  
Pig Farms

SUMMARYA survey of 788 pigs from 120 farms was conducted to determine the within-farm prevalence of pathogenicYersinia enterocoliticaand a questionnaire of management conditions was mailed to the farms afterwards. A univariate statistical analysis with carriage and shedding as outcomes was conducted with random-effects logistic regression with farm as a clustering factor. Variables with aPvalue <0·15 were included into the respective multivariate random-effects logistic regression model. The use of municipal water was discovered to be a protective factor against carriage and faecal shedding of the pathogen. Organic production and buying feed from a certain feed manufacturer were also protective against total carriage. Tonsillar carriage, a different feed manufacturer, fasting pigs before transport to the slaughterhouse, higher-level farm health classification, and snout contacts between pigs were risk factors for faecal shedding. We concluded that differences in management can explain different prevalences ofY. enterocoliticabetween farms.

scholarly journals A comparison of imputation strategies in cluster randomized trials with missing binary outcomes

2016 ◽  
Vol 25 (6) ◽  
pp. 2650-2669 ◽  
Author(s):  
Agnès Caille ◽  
Clémence Leyrat ◽  
Bruno Giraudeau
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Multiple Imputation ◽  
Random Effects ◽  
Logistic Regression Model ◽  
Randomized Trials ◽  
Cluster Randomized Trials ◽  
Complete Case ◽  
Intervention Effect ◽  
Cluster Randomized

In cluster randomized trials, clusters of subjects are randomized rather than subjects themselves, and missing outcomes are a concern as in individual randomized trials. We assessed strategies for handling missing data when analysing cluster randomized trials with a binary outcome; strategies included complete case, adjusted complete case, and simple and multiple imputation approaches. We performed a simulation study to assess bias and coverage rate of the population-averaged intervention-effect estimate. Both multiple imputation with a random-effects logistic regression model or classical logistic regression provided unbiased estimates of the intervention effect. Both strategies also showed good coverage properties, even slightly better for multiple imputation with a random-effects logistic regression approach. Finally, this latter approach led to a slightly negatively biased intracluster correlation coefficient estimate but less than that with a classical logistic regression model strategy. We applied these strategies to a real trial randomizing households and comparing ivermectin and malathion to treat head lice.

Interpreting Parameters in the Logistic Regression Model with Random Effects

Biometrics ◽  
2000 ◽  
Vol 56 (3) ◽  
pp. 909-914 ◽  
Author(s):  
Klaus Larsen ◽  
Jørgen Holm Petersen ◽  
Esben Budtz-Jørgensen ◽  
Lars Endahl
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Random Effects ◽  
Logistic Regression Model

Random effects logistic regression model for anomaly detection

2010 ◽  
Vol 37 (10) ◽  
pp. 7162-7166 ◽  
Author(s):  
Min Seok Mok ◽  
So Young Sohn ◽  
Yong Han Ju
Keyword(s):  
Logistic Regression ◽  
Anomaly Detection ◽  
Regression Model ◽  
Random Effects ◽  
Logistic Regression Model
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