Modeling Benzene Pharmacokinetics Across Three Sets of Animal Data: Parametric Sensitivity and Risk Implications

Typically, the uncertainty affecting the parameters of physiologically based pharmacokinetic (PBPK) models is ignored because it is not currently practical to adjust their values using classical parameter estimation techniques. This issue of parametric variability in a physiological model of benzene pharmacokinetics is addressed in this paper. Monte Carlo simulations were used to study the effects on the model output arising from variability in its parameters. The output was classified into two categories, depending on whether the output of the model on a particular run was judged to be generally consistent with published experimental data. Statistical techniques were used to examine sensitivity and interaction in the parameter space. The model was evaluated against the data from three different experiments in order to test for the structural adequacy of the model and the consistency of the experimental results. The regions of the parameter space associated with various inhalation and gavage experiments are distinct, and the model as presently structured cannot adequately represent the outcomes of all experiments. Our results suggest that further effort is required to discern between the structural adequacy of the model and the consistency of the experimental results. The impact of our results on the risk assessment process for benzene is also examined.     

Fitting Weibull duration models with random effects

Duration time models often should include correlated failure times, due to clustered data. These random effects hierarchical models sometimes are called frailty models when used for survival analyses. The data analyzed here involve such correlations because patient level outcomes (the times until graft failure following kidney transplantation) are observed, but patients are clustered in different transplant centers. We describe fitting such models by combining two kinds of software, one for parametric survival regression models, and the other for doing Poisson regression in a hierarchical setting. The latter is implemented by using PRIMM (Poisson Regression and Interactive Multilevel Modeling) methods and software (Christiansen & Morris, 1994a). An illustrative example for profiling data is included withk=11 kidney transplant centers andN=412 patients.     

Dynamic microeconomic models of fertility choice: a survey

We review existing approaches to the specification and estimation of dynamic microeconomic models of fertility. Dynamic fertility models explain the evolution of fertility variates over the life-cycle as the solution to a dynamic programming model involving economic choices. Dynamic models may be classified into structural and reduced-form models. Structural models generally require solution of the underlying dynamic programming problem. Reduced-form models, while based on a structural specification, do not. Recent innovations in estimation methodologies make both types practical and realistic alternatives to static models of lifetime fertility. JEL classification: J13, C41, C61 Received August 26, 1994/Accepted October 23, 1996     

Modeling Social Processes: Some Lessons from Sports

Sports events can provide helpful models for thinking about social processes. DiMaggio's hitting streak in baseball, for example, raises serious questions about how the causes of relatively uncommon social events are typically studied. The hitting streak also directs our attention to considering models that generate a distribution of outcomes in a population of social units. The series of games leading to a football championship is relevant for approaching the role of complex chains of events in leading to a given outcome (where each individual sequence itself has a low probability of occurring). A model of changing templates suggests a more realistic way of thinking about the linkages over time from an initial starting point to a given outcome. Another sports example, based on the application of a binomial model to the outcome of a series of games, suggests that we should think of chance as a dependent variable that is influenced by—among other factors—the nature of the social structure. Also examined are the special problems in explaining any specific outcome.     

碱水驱组分输运过程有效性研究

建立了碱水驱组分孔隙输运数学模型,将模型中所需的物化参数描述为驱油剂浓度相关的函数,用“有效驱替带”量化和分析了吸附、驱油剂段塞尺寸、驱替速度、扩散弥散等因素对碱组分输运过程有效性的影响机理。在此基础上,进一步分析了大庆油田ASP复合驱用碱剂NaOH在孔隙输运过程中的有效性和持久性,并对模拟软件进行了实验验证,它们具有很好的一致性。经过实验验证的模拟软件,不仅可代替部分实验室流动实验和辅助机理分析,而且可以为碱水驱和ASP复合驱工程工艺方案设计和动态监测提供指导。     

Estimating prediction error for complex samples

With a growing interest in using non-representative samples to train prediction models for numerous outcomes it is necessary to account for the sampling design that gives rise to the data in order to assess the generalized predictive utility of a proposed prediction rule. After learning a prediction rule based on a non-uniform sample, it is of interest to estimate the rule's error rate when applied to unobserved members of the population. Efron (1986) proposed a general class of covariance penalty inflated prediction error estimators that assume the available training data are representative of the target population for which the prediction rule is to be applied. We extend Efron's estimator to the complex sample context by incorporating Horvitz–Thompson sampling weights and show that it is consistent for the true generalization error rate when applied to the underlying superpopulation. The resulting Horvitz–Thompson–Efron estimator is equivalent to dAIC, a recent extension of Akaike's information criteria to survey sampling data, but is more widely applicable. The proposed methodology is assessed with simulations and is applied to models predicting renal function obtained from the large-scale National Health and Nutrition Examination Study survey. The Canadian Journal of Statistics 48: 204–221; 2020 © 2019 Statistical Society of Canada     

Local influence of nonlinear mixed effects model based on M-estimation

Abstract

In this work we mainly study the local influence in nonlinear mixed effects model with M-estimation. A robust method to obtain maximum likelihood estimates for parameters is presented, and the local influence of nonlinear mixed models based on robust estimation (M-estimation) by use of the curvature method is systematically discussed. The counting formulas of curvature for case weights perturbation, response variable perturbation and random error covariance perturbation are derived. Simulation studies are carried to access performance of the methods we proposed. We illustrate the diagnostics by an example presented in Davidian and Giltinan, which was analyzed under the non-robust situation.     

Sensitivity analysis of partially linear models with response missing at random

This article investigates case-deletion influence analysis via Cook’s distance and local influence analysis via conformal normal curvature for partially linear models with response missing at random. Local influence approach is developed to assess the sensitivity of parameter and nonparametric estimators to various perturbations such as case-weight, response variable, explanatory variable, and parameter perturbations on the basis of semiparametric estimating equations, which are constructed using the inverse probability weighted approach, rather than likelihood function. Residual and generalized leverage are also defined. Simulation studies and a dataset taken from the AIDS Clinical Trials are used to illustrate the proposed methods.     

Estimation of Seemingly Unrelated Tobit Regressions via the EM Algorithm

An expectation-maximum (EM) likelihood algorithm is used to estimate two seemingly unrelated Tobit regressions in which the dependent variables are truncated normal. An illustrative example on the determination of the life-health insurance and pension benefits is also given.     

Restricted Estimation in Partially Linear Errors-in-Variables Models

As a compromise between parametric regression and nonparametric regression, partially linear models are frequently used in statistical modelling. This article considers statistical inference for this semiparametric model when the linear covariate is measured with additive error and some additional linear restrictions on the parametric component are assumed to hold. We propose a restricted corrected profile least-squares estimator for the parametric component, and study the asymptotic normality of the estimator. To test hypothesis on the parametric component, we construct a Wald test statistic and obtain its limiting distribution. Some simulation studies are conducted to illustrate our approaches.