scholarly journals A GEE-type approach to untangle structural and random zeros in predictors

2018 ◽  
Vol 28 (12) ◽  
pp. 3683-3696
Author(s):  
Peng Ye ◽  
Wan Tang ◽  
Jiang He ◽  
Hua He
Keyword(s):  
Count Data ◽  
Negative Binomial ◽  
Model Misspecification ◽  
Estimating Equation ◽  
Generalized Estimating Equation ◽  
Simulation Studies ◽  
The Difference ◽  
Random Zeros ◽  
Biased Estimates

Count outcomes with excessive zeros are common in behavioral and social studies, and zero-inflated count models such as zero-inflated Poisson (ZIP) and zero-inflated Negative Binomial (ZINB) can be applied when such zero-inflated count data are used as response variable. However, when the zero-inflated count data are used as predictors, ignoring the difference of structural and random zeros can result in biased estimates. In this paper, a generalized estimating equation (GEE)-type mixture model is proposed to jointly model the response of interest and the zero-inflated count predictors. Simulation studies show that the proposed method performs well for practical settings and is more robust for model misspecification than the likelihood-based approach. A case study is also provided for illustration.

scholarly journals A comparison of zero-inflated and hurdle models for modeling zero-inflated count data

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Cindy Xin Feng
Keyword(s):  
Health Services ◽  
Count Data ◽  
Goodness Of Fit ◽  
Negative Binomial ◽  
Simulation Studies ◽  
Final Choice ◽  
Hurdle Models ◽  
Count Distribution ◽  
Careful Assessment

AbstractCounts data with excessive zeros are frequently encountered in practice. For example, the number of health services visits often includes many zeros representing the patients with no utilization during a follow-up time. A common feature of this type of data is that the count measure tends to have excessive zero beyond a common count distribution can accommodate, such as Poisson or negative binomial. Zero-inflated or hurdle models are often used to fit such data. Despite the increasing popularity of ZI and hurdle models, there is still a lack of investigation of the fundamental differences between these two types of models. In this article, we reviewed the zero-inflated and hurdle models and highlighted their differences in terms of their data generating processes. We also conducted simulation studies to evaluate the performances of both types of models. The final choice of regression model should be made after a careful assessment of goodness of fit and should be tailored to a particular data in question.

Remittances and the Financing of Terrorism In Sub-Saharan Africa: 1974 - 2006

2012 ◽  
Vol 18 (1) ◽  
Author(s):  
Juliet U. Elu ◽  
Gregory N. Price
Keyword(s):  
Economic Growth ◽  
Count Data ◽  
Important Determinant ◽  
Estimating Equation ◽  
Sub Saharan Africa ◽  
Generalized Estimating Equation ◽  
Parameter Estimates ◽  
Consumption Expenditures ◽  
Sub Saharan ◽  
Financing Of Terrorism

AbstractRemittances have been recognized as an important determinant of economic growth for Sub-Saharan African economies as they can finance other determinants that constitute drivers of growth. To the extent that remittances finance terrorism, they can also inhibit economic growth as terrorism can constrain important drivers of growth such as investment and consumption expenditures. In this paper, we appeal to a theory of rational terrorism and consider whether remittances to Sub-Saharan Africa finance terrorism. We estimate the parameters of a static and dynamic terrorism incident supply function with maximum likelihood and Generalized Estimating Equation count data estimators for Sub-Saharan Africa between 1974 and 2006. Our parameter estimates suggest that for Sub-Saharan Africa, remittances are a source of finance for terrorism. We find that approximately one terrorism incident is financed in Sub-Saharan Africa for remittance inflows that range between approximately one quarter of a million dollars and one million dollars.

scholarly journals Factors associated with mortality from tuberculosis in Iran: an application of a generalized estimating equation-based zero-inflated negative binomial model to national registry data

2019 ◽  
Vol 41 ◽  
pp. e2019032
Author(s):  
Fatemeh Sarvi ◽  
Abbas Moghimbeigi ◽  
Hossein Mahjub ◽  
Mahshid Nasehi ◽  
Mahmoud Khodadost
Keyword(s):  
Risk Factors ◽  
Negative Binomial ◽  
Public Health Problem ◽  
Estimating Equation ◽  
Negative Binomial Model ◽  
Generalized Estimating Equation ◽  
Binomial Model ◽  
Global Public Health ◽  
Related Factors ◽  
Generalized Estimating

OBJECTIVES: Tuberculosis (TB) is a global public health problem that causes morbidity and mortality in millions of people per year. The purpose of this study was to examine the relationship of potential risk factors with TB mortality in Iran.METHODS: This cross-sectional study was performed on 9,151 patients with TB from March 2017 to March 2018 in Iran. Data were gathered from all 429 counties of Iran by the Ministry of Health and Medical Education and Statistical Center of Iran. In this study, a generalized estimating equation-based zero-inflated negative binomial model was used to determine the effect of related factors on TB mortality at the community level. For data analysis, R version 3.4.2 was used with the relevant packages.RESULTS: The risk of mortality from TB was found to increase with the unemployment rate (βˆ=0.02), illiteracy (βˆ=0.04), household density per residential unit (βˆ=1.29), distance between the center of the county and the provincial capital (βˆ=0.03), and urbanization (βˆ=0.81). The following other risk factors for TB mortality were identified: diabetes (βˆ=0.02), human immunodeficiency virus infection (βˆ=0.04), infection with TB in the most recent 2 years (βˆ=0.07), injection drug use (βˆ=0.07), long-term corticosteroid use (βˆ=0.09), malignant diseases (βˆ=0.09), chronic kidney disease (βˆ=0.32), gastrectomy (βˆ=0.50), chronic malnutrition (βˆ=0.38), and a body mass index more than 10% under the ideal weight (βˆ=0.01). However, silicosis had no effect.CONCLUSIONS: The results of this study provide useful information on risk factors for mortality from TB.

Stochastic variable selection strategies for zero-inflated models

2017 ◽  
Vol 18 (1) ◽  
pp. 3-23 ◽  
Author(s):  
Eva Cantoni ◽  
Marie Auda
Keyword(s):  
Variable Selection ◽  
Count Data ◽  
Negative Binomial ◽  
Stochastic Search ◽  
Simulation Studies ◽  
Stochastic Variable ◽  
Selection Strategies ◽  
Selection For ◽  
A Chain ◽  
Zero Counts

When count data exhibit excess zero, that is more zero counts than a simpler parametric distribution can model, the zero-inflated Poisson (ZIP) or zero-inflated negative binomial (ZINB) models are often used. Variable selection for these models is even more challenging than for other regression situations because the availability of p covariates implies 4 p possible models. We adapt to zero-inflated models an approach for variable selection that avoids the screening of all possible models. This approach is based on a stochastic search through the space of all possible models, which generates a chain of interesting models. As an additional novelty, we propose three ways of extracting information from this rich chain and we compare them in two simulation studies, where we also contrast our approach with regularization (penalized) techniques available in the literature. The analysis of a typical dataset that has motivated our research is also presented, before concluding with some recommendations.

State Failure and Transnational Terrorism

2016 ◽  
Vol 62 (3) ◽  
pp. 471-495 ◽  
Author(s):  
Justin George
Keyword(s):  
Negative Binomial ◽  
Estimating Equation ◽  
State Failure ◽  
Generalized Estimating Equation ◽  
Failed States ◽  
Transnational Terrorism ◽  
States Experience ◽  
The Relationship ◽  
Concentration Curves ◽  
Binomial Models

Based on a country panel from 1995 to 2013, this study examines the relationship between state failure and transnational terrorism with respect to perpetrator’s proximity to the target and logistical complexity of attacks. Using concentration curves and generalized estimating equation negative binomial models, the study shows that failed states experience significantly more transnational terrorism when the perpetrators are from the home country. But these states do not produce terrorists who cross borders and carry out attacks in other countries, neither do they attract foreign perpetrators. The latter suggests that conditions in failed states present major operational challenges to foreign terrorists. State failure also causes more logistically complex attacks due to lack of effective counterterrorism measures by failed states. The main results hold true for both relative and dichotomous measures of state failure.

Associations between healthcare consumption in country of origin and in country of residence by people with Turkish and Moroccan backgrounds living in the Netherlands: the HELIUS study

2019 ◽  
Vol 29 (4) ◽  
pp. 694-699
Author(s):  
Aydın Şekercan ◽  
Marieke B Snijder ◽  
Ron J Peters ◽  
Karien Stronks
Keyword(s):  
The Netherlands ◽  
Negative Binomial ◽  
Country Of Origin ◽  
Estimating Equation ◽  
Population Based ◽  
Generalized Estimating Equation ◽  
Healthcare Use ◽  
Allied Health Professionals ◽  
Frequent Attenders ◽  
Helius Study

Abstract Background In Europe, a substantial percentage of the 22 million inhabitants with histories of migration from non-European countries utilize healthcare in their countries of origin. That could reflect avoidance of healthcare in the country of residence, but this has not been studied previously. Methods We linked Dutch healthcare reimbursement data to the multi-ethnic population-based data from the HELIUS study conducted in Amsterdam. In multivariable logistic regression and negative binomial generalized estimating equation (GEE) analyses, we examined associations between healthcare use in country of origin and in country of residence by people with Turkish and with Moroccan backgrounds (N = 2920 and N = 3031, respectively) in the period 2010–15. Results Participants with Turkish and Moroccan backgrounds who utilized healthcare one or multiple times in the country of origin (n = 1335 and n = 558, respectively) were found to be more likely, in comparison with non-users (n = 1585, n = 2473), to be frequent attenders of services by general practitioners, medical specialists and/or allied health professionals in the Netherlands [odds ratios between 1.21 (95% CI 0.91–1.60) and 3.15 (95% CI 2.38–4.16)]. GEE analyses showed similar results. Conclusion People with Turkish or Moroccan backgrounds living in the Netherlands who use healthcare in their countries of origin are more likely than non-users to be higher users of healthcare in the Netherlands. We thus found no indications for avoidance of healthcare in the country of residence.

scholarly journals Doubly robust methods for handling confounding by cluster

Biostatistics ◽  
2016 ◽  
Vol 17 (2) ◽  
pp. 264-276 ◽  
Author(s):  
Johan Zetterqvist ◽  
Stijn Vansteelandt ◽  
Yudi Pawitan ◽  
Arvid Sjölander
Keyword(s):  
Cognitive Abilities ◽  
Model Misspecification ◽  
R Package ◽  
Estimating Equation ◽  
Generalized Estimating Equation ◽  
Robust Methods ◽  
Valid Inference ◽  
Smoking During Pregnancy ◽  
Working Model ◽  
Doubly Robust

Abstract In clustered designs such as family studies, the exposure-outcome association is usually confounded by both cluster-constant and cluster-varying confounders. The influence of cluster-constant confounders can be eliminated by studying the exposure-outcome association within (conditional on) clusters, but additional regression modeling is usually required to control for observed cluster-varying confounders. A problem is that the working regression model may be misspecified, in which case the estimated within-cluster association may be biased. To reduce sensitivity to model misspecification we propose to augment the standard working model for the outcome with an auxiliary working model for the exposure. We derive a doubly robust conditional generalized estimating equation (DRCGEE) estimator for the within-cluster association. This estimator combines the two models in such a way that it is consistent if either model is correct, not necessarily both. Thus, the DRCGEE estimator gives the researcher two chances instead of only one to make valid inference on the within-cluster association. We have implemented the estimator in an R package and we use it to examine the association between smoking during pregnancy and cognitive abilities in offspring, in a sample of siblings.

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