Zero Truncated Bivariate Poisson Model: Marginal-Conditional Modeling Approach with an Application to Traffic Accident Data

A new covariate dependent zero-truncated bivariate Poisson model is proposed in this paper employing generalized linear model. A marginal-conditional approach is used to show the bivariate model. The proposed model with estimation procedure and tests for goodness-of-fit and under (or over) dispersion are shown and applied to road safety data. Two correlated outcome variables considered in this study are number of cars involved in an accident and number of casualties for given number of cars.

Causal inference with marginal structural modeling for longitudinal data in laparoscopic surgery: A technical note

Causal inference prevails in the field of laparoscopic surgery.Once the causality between an intervention and outcome is established,the intervention can be applied to a target population to improve clinical outcomes.In many clinical scenarios,interventions are applied longitudinally in response to patients’conditions.Such longitudinal data comprise static variables,such as age,gender,and comorbidities;and dynamic variables,such as the treatment regime,laboratory variables,and vital signs.Some dynamic variables can act as both the confounder and mediator for the effect of an intervention on the outcome;in such cases,simple adjustment with a conventional regression model will bias the effect sizes.To address this,numerous statistical methods are being developed for causal inference;these include,but are not limited to,the structural marginal Cox regression model,dynamic treatment regime,and Cox regression model with time-varying covariates.This technical note provides a gentle introduction to such models and illustrates their use with an example in the field of laparoscopic surgery.

边缘无浆体病动物模型的建立及其生理指标变化规律的研究

建立边缘无浆体病的动物疾病模型。采用地塞米松注射液长期抑制实验动物的免疫力,通过犊牛颈静脉接种含有无浆体病原的牛血液,监测奶牛无浆体感染过程中其生理指标的变化趋势,以及在实验动物体内的消长规律,建立起无浆体人工感染动物模型。无浆体感染实验动物有5～7d的潜伏期,感染早期体温先升高后回复正常,呼吸先加快后回复正常水平,心率无明显变化。边缘无浆体感染过程中,红细胞出现大量变形和破碎,数量减少,白细胞呈现减少升高再减少的过程后,逐步恢复到正常水平,而血小板数量增加。因此,可用1月龄犊牛在免疫抑制情况下建立无浆体人工感染动物模型,揭示犊牛在感染边缘无浆体过程中其生理与血液指标的变化规律。

More Efficient Estimators for Marginal Additive Hazards Model in Case-cohort Studies with Multiple Outcomes

Case-cohort study designs are widely used to reduce the cost of large cohort studies. When several diseases are of interest, we can use the same subcohort. In this paper, we will study the casecohort design of marginal additive hazards model for multiple outcomes by a more efficient version. Instead of analyzing each disease separately, ignoring the additional exposure measurements collected on subjects with other diseases, we propose a new weighted estimating equation approach to improve the efficiency by utilizing as much information collected as possible. The consistency and asymptotic normality of the resulting estimator are established. Simulation studies are conducted to examine the finite sample performance of the proposed estimator, which confirm the efficiency gains.

Hydrogen-induced marginal growth model for the synthesis of graphene by arc discharge

High-quality graphene is prepared by arc discharge with low cost under hydrogen atmosphere. However, the growth mechanism of graphene synthesis by arc discharge remains unclear. In this paper, the hydrogen-induced marginal growth(HIMG) model is deduced to study the growth mechanism of graphene by combining experiment with numerical simulation results. First, the characteristics of thick edges and thin middle and containing hydrogen are verified by transmission electron microscopy and Raman spectroscopy, respectively. In addition, numerical simulation provides the chemical species and temperature range of graphene growth. Second, the marginal growth pattern of hydrogen transfer and carbon addition is introduced because the C–H and C–C reduce configuration energy and island energy, respectively. Meanwhile, the stacking growth at the margin of the graphene island leads to the longitudinal growth of graphene because of the Van der Waals force and the effect of self-assembly,increasing the number of graphene layers. Finally, graphene sheets with a small amount of hydrogen are deposited on the inner wall after annealing. The investigation of the growth mechanism of graphene under hydrogen atmosphere lays a foundation for the large-scale preparation of graphene by arc discharge.

Pharmacological Treatment of Adult Attention-Deficit/Hyperactivity Disorder(ADHD)in a Longitudinal Observational Study:Estimated Treatment Effect Strengthened by Improved Covariate Balance

An improved method for estimation of causal effects from observational data is demonstrated. Applications in medicine have been few, and the purpose of the present study is to contribute new clinical insight by means of this new and more sophisticated analysis. Long term effect of medication for adult ADHD patients is not resolved. A model with causal parameters to represent effect of medication was formulated, which accounts for time-varying confounding and selection-bias from loss to follow-up. The popular marginal structural model (MSM) for causal inference, of Robins et al., adjusts for time-varying confounding, but suffers from lack of robustness for misspecification in the weights. Recent work by Imai and Ratkovic?[1][2] achieves robustness in the MSM, through improved covariate balance (CBMSM). The CBMSM (freely available software) was compared with a standard fit of a MSM and a naive regression model, to give a robust estimate of the true treatment effect in 250 previously non-medicated adults, treated for one year, in a specialized ADHD outpatient clinic in Norway. Covariate balance was greatly improved, resulting in a stronger treatment effect than without this improvement. In terms of treatment effect per week, early stages seemed to have the strongest influence. An estimated average reduction of 4 units on the symptom scale assessed at 12 weeks, for hypothetical medication in the 9 - 12 weeks period compared to no medication in this period, was found. The treatment effect persisted throughout the whole year, with an estimated average reduction of 0.7 units per week on symptoms assessed at one year, for hypothetical medication in the last 13 weeks of the year, compared to no medication in this period. The present findings support a strong and causal direct and indirect effect of pharmacological treatment of adults with ADHD on improvement in symptoms, and with a stronger treatment effect than has been reported.

A Study of the Setting of Margin Levels in Futures Market

Margin rules are very important rules in futures market. This paper provides a new Value-at-Risk （VaR） approach which uses GARCH model to set margin levels. The new approach overcomes the limitation of the hypothesis of normal distribution in traditional methods and improves the estimation precision. We use the data of metal futures in China＇s Shanghai Futures Exchange （SHFE） to have an empirical study.

A general additive-multiplicative rates model for recurrent event data

In this article, we propose a general additive-multiplicative rates model for recurrent event data. The proposed model includes the additive rates and multiplicative rates models as special cases. For the inference on the model parameters, estimating equation approaches are developed, and asymptotic properties of the proposed estimators are established through modern empirical process theory. In addition, an illustration with multiple-infection data from a clinic study on chronic granulomatous disease is provided.

A Semiparametric Additive Rates Model for Clustered Recurrent Event Data

Recurrent event data often arises in biomedical studies, and individuals within a cluster might not be independent. We propose a semiparametric additive rates model for clustered recurrent event data, wherein the covariates are assumed to add to the unspecified baseline rate. For the inference on the model parameters, estimating equation approaches are developed, and both large and finite sample properties of the proposed estimators are established.

A General Additive-multiplicative Rates Model for Recurrent and Terminal Events

Recurrent events data with a terminal event （e.g. death） often arise in clinical and observational studies. Most of existing models assume multiplicative covariate effects and model the conditional recurrent event rate given survival. In this article, we propose a general mSditive-multiplicative rates model for recurrent event data in the presence of a terminal event, where the terminal event stop the further occurrence of recurrent events. Based on the estimating equation approach and the inverse probability weighting technique, we propose two procedures for estimating the regression parameters and the baseline mean function. The asymptotic properties of the resulting estimators are established. In addition, some graphical and numerical procedures are presented for model checking. The finite-sample behavior of the proposed methods is examined through simulation studies, and an application to a bladder cancer study is also illustrated.

A Class of Box-Cox Transformation Models for Recurrent Event Data with a Terminal Event

In this article, we study a class of Box-Cox transformation models for recurrent event data in the presence of terminal event, which includes the proportional means models as special cases. Estimating equation approaches and the inverse probability weighting technique are used for estimation of the regression parameters. The asymptotic properties of the resulting estimators are established. The finite sample behavior of the proposed methods is examined through simulation studies, and an application to a heart failure study is presented to illustrate the proposed method.

Additive-multiplicative Hazards Model for Case-Cohort Studies with Multiple Disease Outcomes

Case-cohort design is an efficient and economical design to study risk factors for diseases with expensive measurements, especially when the disease rate is low. When several diseases are of interest, multiple case-cohort design studies may be conducted using the same subcohort. To study the association between risk factors and each disease occurrence or death, we consider a general additive-multiplicative hazards model for case-cohort designs with multiple disease outcomes. We present an estimation procedure for the regression parameters of the additive-multiplicative hazards model, and show that the proposed estimator is consistent and asymptotically normal. Large sample approximation works well in finite sample studies in simulation. Finally, we apply the proposed method to a real data example for illustration.