Semiparametric models for multilevel overdispersed count data with extra zeros

Mahmoodi, Marzieh; Moghimbeigi, Abbas; Mohammad, Kazem; Faradmal, Javad

doi:10.1177/0962280216657376

Cited by 7 publications

(3 citation statements)

References 48 publications

(91 reference statements)

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“…This is an improvement of the joint modeling of longitudinal zero-inflated count and survival model, as Feng and Zhu 27 and Mahmoodi et al. 26 used only the truncated power basis for modeling the longitudinal count response with extra zeros.…”

Section: Discussionmentioning

confidence: 99%

“…In the context of longitudinal zero-inflated count data modeling, Mahmoodi et al. 26 and Feng and Zhu 27 use truncated power series for taking into account the non-linear effect on the response variable and the Monte Carlo EM for parameter estimation. We use a hurdle model under Poisson and negative binomial distributional assumption for zero-inflated count data.…”

Section: Introductionmentioning

confidence: 99%

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Approximate Bayesian inference for joint linear and partially linear modeling of longitudinal zero-inflated count and time to event data

Baghfalaki

Ganjali

2021

Stat Methods Med Res

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Joint modeling of zero-inflated count and time-to-event data is usually performed by applying the shared random effect model. This kind of joint modeling can be considered as a latent Gaussian model. In this paper, the approach of integrated nested Laplace approximation (INLA) is used to perform approximate Bayesian approach for the joint modeling. We propose a zero-inflated hurdle model under Poisson or negative binomial distributional assumption as sub-model for count data. Also, a Weibull model is used as survival time sub-model. In addition to the usual joint linear model, a joint partially linear model is also considered to take into account the non-linear effect of time on the longitudinal count response. The performance of the method is investigated using some simulation studies and its achievement is compared with the usual approach via the Bayesian paradigm of Monte Carlo Markov Chain (MCMC). Also, we apply the proposed method to analyze two real data sets. The first one is the data about a longitudinal study of pregnancy and the second one is a data set obtained of a HIV study.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Approximate Bayesian inference for joint linear and partially linear modeling of longitudinal zero-inflated count and time to event data

Baghfalaki

Ganjali

2021

Stat Methods Med Res

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“…Therefore, ignoring or misdiagnosing the effect of these variables will not only bias the estimate of the effect, it will also make the effect appear meaningless. In this regard, generalized additive models (GAMs) have been extended for handling nonlinear associations for various outcomes including survival outcome (time-to-event) 15 – 25 . Leblanc et al, showed that the average error in the Cox model with an inappropriate functional form is three times higher than in a model with a non-linear functional effect form 17 .…”

Section: Introductionmentioning

confidence: 99%

Spatial inequalities and non-linear association of continuous variables with mortality risk of liver transplantation in Iran: a retrospective cohort study

Kazemimajd,

Roshanaei,

Tapak

2024

Sci Rep

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Liver transplantation is the second most common solid organ transplant and the best option for liver failure. Of course, patient survival after transplantation depends on many risk factors. The aim of this study was to investigate the spatial and non-linear effects of continuous risk factors on patient survival after liver transplantation. This retrospective cohort study (n = 3148) used data on liver transplantation in Iran (2004–2019). A generalized additive model with spatial effects and non-linear effects of age and Model for End-Stage Liver Disease (MELD) score variables by penalized spline was used. The majority of patients were male (63.3%), with a mean (SD) age of 42.65 (13.31) and a mean (SD) MELD score of 24.43 (6.72). The 1, 5, and 10-year survival rates were 88.2%, 84.6%, and 82.5% respectively. The non-linear effect showed a steeper slope of the age effect on the hazard of death after the age of 50 (p < 0.05), and the MELD score had a direct but non-linear relationship with the hazard of death (p < 0.05). In the spatial pattern, the provinces with a greater distance from the transplant center had significantly fewer old patients than other provinces. Also, more distant provinces with an older transplant age had higher post-transplant mortality rates. Our study showed that it is better to take age and MELD score into account in postoperative care. The spatial pattern of mortality risk reflects inequalities in access to transplantation and public health services after transplantation.

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Three-level zero-inflated Conway–Maxwell–Poisson regression model for analyzing dispersed clustered count data with extra zeros

2020

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Semiparametric models for multilevel overdispersed count data with extra zeros

Cited by 7 publications

References 48 publications

Approximate Bayesian inference for joint linear and partially linear modeling of longitudinal zero-inflated count and time to event data

Approximate Bayesian inference for joint linear and partially linear modeling of longitudinal zero-inflated count and time to event data

Spatial inequalities and non-linear association of continuous variables with mortality risk of liver transplantation in Iran: a retrospective cohort study

Three-level zero-inflated Conway–Maxwell–Poisson regression model for analyzing dispersed clustered count data with extra zeros

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