Mixed models for binary data

2011 ◽  
pp. 75-83
Keyword(s):  
Mixed Models ◽  
Binary Data

scholarly journals LASSO type Penalized Spline Regression for Binary Data

2020 ◽  
Author(s):  
Muhammad Mullah ◽  
James Hanley ◽  
Andrea Benedetti
Keyword(s):  
Mixed Models ◽  
Binary Data ◽  
Model Fitting ◽  
Correlated Data ◽  
Chronic Obstructive ◽  
Obstructive Pulmonary Disease ◽  
Penalized Spline ◽  
Constant Variance ◽  
Lasso Penalty ◽  
Fitting In

Abstract Background: Generalized linear mixed models (GLMMs), typically used for analyzing correlated data, can also be used for smoothing by considering the knot coefficients from a regression spline as random effects. The resulting models are called semiparametric mixed models (SPMMs). Allowing the random knot coefficients to follow a normal distribution with mean zero and a constant variance is equivalent to using a penalized spline with a ridge regression type penalty. We introduce the least absolute shrinkage and selection operator (LASSO) type penalty in the SPMM setting by considering the coefficients at the knots to follow a Laplace double exponential distribution with mean zero. Methods: We adopt a Bayesian approach and use the Markov Chain Monte Carlo (MCMC) algorithm for model fitting. Through simulations, we compare the performance of curve fitting in a SPMM using a LASSO type penalty to that of using ridge penalty for binary data. We apply the proposed method to obtain smooth curves from data on the relationship between the amount of pack years of smoking and the risk of developing chronic obstructive pulmonary disease (COPD). Results: The LASSO penalty performs as well as ridge penalty for simple shapes of association and outperforms the ridge penalty when the shape of association is complex or linear. Conclusion: We demonstrated that LASSO penalty captured complex dose-response association better than the Ridge penalty in a SPMM.

Generalized linear mixed models for correlated binary data with t-link

2013 ◽  
Vol 24 (6) ◽  
pp. 1111-1123 ◽  
Author(s):  
Marcos O. Prates ◽  
Denise R. Costa ◽  
Victor H. Lachos
Keyword(s):  
Mixed Models ◽  
Binary Data ◽  
Generalized Linear Mixed Models ◽  
Linear Mixed Models ◽  
Correlated Binary Data

scholarly journals Marginal Correlation in Longitudinal Binary Data Based on Generalized Linear Mixed Models

2010 ◽  
Vol 39 (19) ◽  
pp. 3540-3557 ◽  
Author(s):  
Tony Vangeneugden ◽  
Geert Molenberghs ◽  
Annouschka Laenen ◽  
Helena Geys ◽  
Caroline Beunckens ◽  
...  
Keyword(s):  
Mixed Models ◽  
Binary Data ◽  
Generalized Linear Mixed Models ◽  
Linear Mixed Models ◽  
Longitudinal Binary Data

scholarly journals Modeling perinatal mortality in twins via generalized additive mixed models: a comparison of estimation approaches

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Muhammad Abu Shadeque Mullah ◽  
James A. Hanley ◽  
Andrea Benedetti
Keyword(s):  
Birth Order ◽  
Perinatal Mortality ◽  
Mixed Models ◽  
Binary Data ◽  
Bayesian Method ◽  
Specific Weight ◽  
Multiple Birth ◽  
Unique Challenge ◽  
Generalized Additive Mixed Models ◽  
Additive Mixed Models

Abstract Background The analysis of twin data presents a unique challenge. Second-born twins on average weigh less than first-born twins and have an elevated risk of perinatal mortality. It is not clear whether the risk difference depends on birth order or their relative birth weight. This study evaluates the association between birth order and perinatal mortality by birth order-specific weight difference in twin pregnancies. Methods We adopt generalized additive mixed models (GAMMs) which are a flexible version of generalized linear mixed models (GLMMs), to model the association. Estimation of such models for correlated binary data is challenging. We compare both Bayesian and likelihood-based approaches for estimating GAMMs via simulation. We apply the methods to the US matched multiple birth data to evaluate the association between twins’ birth order and perinatal mortality. Results Perinatal mortality depends on both birth order and relative birthweight. Simulation results suggest that the Bayesian method with half-Cauchy priors for variance components performs well in estimating all components of the GAMM. The Bayesian results were sensitive to prior specifications. Conclusion We adopted a flexible statistical model, GAMM, to precisely estimate the perinatal mortality risk differences between first- and second-born twins whereby birthweight and gestational age are nonparametrically modelled to explicitly adjust for their effects. The risk of perinatal mortality in twins was found to depend on both birth order and relative birthweight. We demonstrated that the Bayesian method estimated the GAMM model components more reliably than the frequentist approaches.

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