Analysis of longitudinal semicontinuous data using marginalized two-part model

Gebregziabher

2021

Stat Methods Med Res

Self Cite

Analysis of longitudinal semicontinuous data characterized by subjects’ attrition triggered by nonrandom dropout is complex and requires accounting for the within-subject correlation, and modeling of the dropout process. While methods that address the within-subject correlation and missing data are available, approaches that incorporate the nonrandom dropout, also referred to informative right censoring, in the modeling step are scarce due to the computational intensity and possible intractable integration needed for its implementation. Appreciating the complexity of this problem and the need for a new methodology that is feasible for implementation, we propose to extend a framework of likelihood-based marginalized two-part models to account for informative right censoring. The censoring process is modeled using two approaches: (1) Poisson censoring for the count of visits before dropout and (2) survival time to dropout. Novel consideration was given to the proposed joint modeling approaches for the semicontinuous and censoring components of the likelihood function which included (1) shared parameter, and (2) Clayton copula. The cross-part and within-part correlations were accounted for through a complex random effect structure that models correlated random intercepts and slopes. Feasibility of implementation, and accuracy of these approaches were investigated using extensive simulation studies and clinical application.

Section: Data Examplementioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Shared parameter and copula models for analysis of semicontinuous longitudinal data with nonrandom dropout and informative censoring

Gebregziabher

2021

Stat Methods Med Res

Self Cite

“…The limitations with data transformation in Part II include reduced information, difficulty in interpreting the results and possible heteroscedasticity [ 13 , 14 ]. An alternative approach is to use generalized linear mixed models (GLMMs) with distributions in the exponential family that can model skewed data, such as Log-Normal, Log-Skew-Normal [ 15 ], Gamma [ 13 ], Inverse Gamma, Inverse Gaussian [ 16 ], Beta [ 17 ], Bridge [ 18 ], Generalized Gamma family, and Weibull distributions [ 19 ]. It is noted that GLMMs often involve complicated iterative procedures in estimation which may lead to intensive computation burden and non-convergence issues.…”

Section: Introductionmentioning

confidence: 99%

Zero-augmented beta-prime model for multilevel semi-continuous data: a Bayesian inference

Mezerji

Soltanian

Mahjub

et al. 2022

BMC Med Res Methodol

Semi-continuous data characterized by an excessive proportion of zeros and right-skewed continuous positive values appear frequently in medical research. One example would be the pharmaceutical expenditure (PE) data for which a substantial proportion of subjects investigated may report zero. Two-part mixed-effects models have been developed to analyse clustered measures of semi-continuous data from multilevel studies. In this study, we propose a new flexible two-part mixed-effects model with skew distributions for nested semi-continuous cost data under the framework of a Bayesian approach. The proposed model specification consists of two mixed-effects models linked by the correlated random effects: Part I) a model on the occurrence of positive values using a generalized logistic mixed model; and Part II) a model on the magnitude of positive values using a linear mixed model where the model errors follow skew distributions including beta-prime (BP). The proposed method is illustrated with pharmaceutical expenditure data from a multilevel observational study and the analytic results are reported by comparing potential models under different skew distributions. Simulation studies are conducted to assess the performance of the proposed model. The DIC3, LPML, WAIC, and LOO as the Bayesian model selection criteria and measures of divergence used to compare the models.

“…Failure to do so may result in biased and misleading findings. Methods have been proposed in the literature for modeling and analysis of semicontinuous data, including, perhaps, the most popular two‐part model approach . In essence, the two‐part model incorporates an underlying two‐part structure in which the zero and nonzero observations are modeled separately through distinct (although sometimes overlapping) sets of parameters.…”

Section: Introductionmentioning

confidence: 99%

“…Methods have been proposed in the literature for modeling and analysis of semicontinuous data, including, perhaps, the most popular two-part model approach. [4][5][6][7][8][9][10][11][12][13][14] In essence, the two-part model incorporates an underlying two-part structure in which the zero and nonzero observations are modeled separately through distinct (although sometimes overlapping) sets of parameters. An excellent overview on the modeling and analysis of semicontinuous data (also of zero-inflated count data, a similar phenomenon) can be found in the works of Neelon et al 15,16 The present research is motivated by the CHEF (Cultivating Healthy Environments in Families with Type 1 Diabetes) study.…”

Section: Introductionmentioning

confidence: 99%

A model‐based approach for clustering of multivariate semicontinuous data with application to dietary pattern analysis and intervention

Jiang

Duan

et al. 2019

Statistics in Medicine

Semicontinuous data, characterized by a sizable number of zeros and observations from a continuous distribution, are frequently encountered in health research concerning food consumptions, physical activities, medical and pharmacy claims expenditures, and many others. In analyzing such semicontinuous data, it is imperative that the excessive zeros be adequately accounted for to obtain unbiased and efficient inference. Although many methods have been proposed in the literature for the modeling and analysis of semicontinuous data, little attention has been given to clustering of semicontinuous data to identify important patterns that could be indicative of certain health outcomes or intervention effects. We propose a Bernoulli‐normal mixture model for clustering of multivariate semicontinuous data and demonstrate its accuracy as compared to the well‐known clustering method with the conventional normal mixture model. The proposed method is illustrated with data from a dietary intervention trial to promote healthy eating behavior among children with type 1 diabetes. In the trial, certain diabetes friendly foods (eg, total fruit, whole fruit, dark green and orange vegetables and legumes, whole grain) were only consumed by a proportion of study participants, yielding excessive zero values due to nonconsumption of the foods. Baseline foods consumptions data in the trial are used to explore preintervention dietary patterns among study participants. While the conventional normal mixture model approach fails to do so, the proposed Bernoulli‐normal mixture model approach has shown to be able to identify a dietary profile that significantly differentiates the intervention effects from others, as measured by the popular healthy eating index at the end of the trial.