Comparing a multivariate response Bayesian random effects logistic regression model with a latent variable item response theory model for provider profiling on multiple binary indicators simultaneously

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.

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

Statistical Methods in Medical Research ◽

10.1177/0962280214530030 ◽

2016 ◽

Vol 25 (6) ◽

pp. 2650-2669 ◽

Cited By ~ 6

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.

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Interpreting Parameters in the Logistic Regression Model with Random Effects

Biometrics ◽

10.1111/j.0006-341x.2000.00909.x ◽

2000 ◽

Vol 56 (3) ◽

pp. 909-914 ◽

Cited By ~ 239

Author(s):

Klaus Larsen ◽

Jørgen Holm Petersen ◽

Esben Budtz-Jørgensen ◽

Lars Endahl

Keyword(s):

Logistic Regression ◽

Regression Model ◽

Random Effects ◽

Logistic Regression Model

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

European Journal of Operational Research ◽

10.1016/j.ejor.2006.10.006 ◽

2007 ◽

Vol 183 (1) ◽

pp. 472-478 ◽

Cited By ~ 26

Author(s):

So Young Sohn ◽

Hong Sik Kim

Keyword(s):

Logistic Regression ◽

Regression Model ◽

Random Effects ◽

Logistic Regression Model ◽

Credit Guarantee ◽

Default Prediction

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A latent variable approach to measuring and explaining peace agreement strength

Political Science Research and Methods ◽

10.1017/psrm.2019.23 ◽

2019 ◽

pp. 1-17

Author(s):

Rob Williams ◽

Daniel J. Gustafson ◽

Stephen E. Gent ◽

Mark J. C. Crescenzi

Keyword(s):

Item Response Theory ◽

Item Response ◽

Latent Variable ◽

Theory Model ◽

Peace Agreement ◽

Civil Conflicts ◽

Post Conflict ◽

Peace Agreements ◽

Variable Approach ◽

Latent Variable Approach

AbstractMuch of the peace agreement durability literature assumes that stronger peace agreements are more likely to survive the trials of the post-conflict environment. This work does an excellent job identifying which provisions indicate that agreements are more likely to endure. However, there is no widely accepted way to directly measure the strength of agreements, and existing measures suffer from a lack of nuance or reliance on subjective weighting. We use a Bayesian item response theory model to develop a principled measure of the latent strength of peace agreements in civil conflicts from 1975 to 2005. We illustrate the measure's utility by exploring how various international factors such as sanctions and mediation contribute to the strength or weakness of agreements.

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