A Limited Repertoire of Emotion Regulation Strategies is Associated with Internalizing Problems in Adolescence

The present study was designed to test whether the beneficial effects of emotion regulation (ER) have less to do with the use of singular, ‘adaptive’ strategies and more to do with using a range of strategies. Using a community sample of adolescents (N = 177, M = 13.6 years), groups based on five measures of ER (reappraisal, suppression, concealing, emotional engagement, and adjusting) were created through latent profile analysis. A six‐group model was the best fit. As hypothesized, ER profiles with high scores on one or two indicators were associated with higher levels of internalizing problems, whereas profiles with average to high scores on several indicators and high scores on adjusting were associated with lower levels of internalizing problems (depression, general anxiety, and social anxiety). Results highlight the importance of having different ER strategies to use and that a limited ER repertoire in adolescence may be associated with internalizing problems.     

Monitoring Multivariate Simple Linear Profiles in the Presence of between Profile Autocorrelation

In recent years, statistical profile monitoring has emerged as a relatively new and potentially useful subarea of statistical process control and has attracted attention of many researchers and practitioners. A profile, waveform, or signature is a function that relates a dependent or a response variable to one or more independent variables. Different statistical methods have been proposed by researchers to monitor profiles where each method requires its own assumptions. One of the common and implicit assumptions in most of the proposed procedures is the assumption of independent residuals. Violation of this assumption can affect the performance of control procedures and ultimately leading to misleading results. In this article, we study phase II analysis of monitoring multivariate simple linear profiles when the independency assumption is violated. Three time series based methods are proposed to eliminate the effect of correlation that exists between multivariate profiles. Performances of the proposed methods are evaluated using average run length (ARL) criterion. Numerical results indicate satisfactory performance for the proposed methods. A simulated example is also used to show the application of the proposed methods.     

Sensitivity of Physiologically Based Pharmacokinetic Models to Variation in Model Parameters: Methylene Chloride

The parameters in a physiologically based pharmacokinetic (PBPK) model of methylene chloride were varied systematically, and the resulting variation in a number of model outputs was determined as a function of time for mice and humans at several exposure concentrations. The importance of the various parameters in the model was highly dependent on the conditions (concentration, species) for which the simulation was performed and the model output (dose surrogate) being considered. Model structure also had a significant impact on the results. For sensitivity analysis, particular attention must be paid to conservation equations to ensure that the variational calculations do not alter mass balance, introducing extraneous effects into the model. All of the normalized sensitivity coefficients calculated in this study ranged between −1.12 and 1, and most were much less than 1 in absolute value, indicating that individual input errors are not greatly amplified in the outputs. In addition to ranking parameters in terms of their impact on model predictions, time-dependent sensitivity analysis can also be used as an aid in the design of experiments to estimate parameters by predicting the experimental conditions and sampling points which will maximize parameter identifiability.     

A Pharmacokinetic Study of Occupational and Environmental Benzene Exposure with Regard to Gender

Using physiologically-based pharmacokinetic (PBPK) modeling, occupational, personal, and environmental benzene exposure scenarios are simulated for adult men and women. This research identifies differences in internal exposure due to physiological and biochemical gender differences. Physiological and chemical-specific model parameters were obtained from other studies reported in the literature and medical texts for the subjects of interest. Women were found to have a higher blood/air partition coefficient and maximum velocity of metabolism for benzene than men (the two most sensitive parameters affecting gender-specific differences). Additionally, women generally have a higher body fat percentage than men. These factors influence the internal exposure incurred by the subjects and should be considered when conducting a risk assessment. Results demonstrated that physicochemical gender differences result in women metabolizing 23–26% more benzene than men when subject to the same exposure scenario even though benzene blood concentration levels are generally higher in men. These results suggest that women may be at significantly higher risk for certain effects of benzene exposure. Thus, exposure standards based on data from male subjects may not be protective for the female population.     

Probabilistic Models for Risk Assessment

Recent concern with the potential for stray carbon fibers to damage electronic equipment and cause economic losses has led to the development of advanced risk-assessment methods. Risk assessment often requires the synthesis of risk profiles which represent the probability distribution of total annual losses due to a certain set of events or activities. A number of alternative probabilistic models are presented which the authors have used to develop such profiles. Examples are given of applications of these methods to assessment of risk due to conductive fibers released from aircraft or automobile fires. These assessments usually involve a two-stage approach: estimation of losses for several subclassifications of the overall process, and synthesis of the results into an aggregate risk profile. The methodology presented is capable of treating a wide variety of situations involving sequences of random physical events.     

Tenure Profiles and Efficient Separation in a Stochastic Productivity Model

We develop a theoretical model based on efficient bargaining, where both log outside productivity and log productivity in the current job follow a random walk. This setting allows the application of real option theory. We derive the efficient worker-firm separation rule. We show that wage data from completed job spells are uninformative about the true tenure profile. The model is estimated on the Panel Study of Income Dynamics. It fits the observed distribution of job tenures well. Selection of favorable random walks can account for the concavity in tenure profiles. About 80% of the estimated wage returns to tenure is due to selectivity in the realized outside productivities.     

Steady-State Solutions to PBPK Models and Their Applications to Risk Assessment I: Route-to-Route Extrapolation of Volatile Chemicals

Although analysis of in vivo pharmacokinetic data necessitates use of time-dependent physiologically-based pharmacokinetic (PBPK) models, risk assessment applications are often driven primarily by steady-state and/or integrated (e.g., AUC) dosimetry. To that end, we present an analysis of steady-state solutions to a PBPK model for a generic volatile chemical metabolized in the liver. We derive an equivalent model that is much simpler and contains many fewer parameters than the full PBPK model. The state of the system can be specified by two state variables-the rate of metabolism and the rate of clearance by exhalation. For a given oral dose rate or inhalation exposure concentration, the system state only depends on the blood-air partition coefficient, metabolic constants, and the rates of blood flow to the liver and of alveolar ventilation. At exposures where metabolism is close to linear, only the effective first-order metabolic rate is needed. Furthermore, in this case, the relationship between cumulative exposure and average internal dose (e.g., AUCs) remains the same for time-varying exposures. We apply our analysis to oral-inhalation route extrapolation, showing that for any dose metric, route equivalence only depends on the parameters that determine the system state. Even if the appropriate dose metric is unknown, bounds can be placed on the route-to-route equivalence with very limited data. We illustrate this analysis by showing that it reproduces exactly the PBPK-model-based route-to-route extrapolation in EPA's 2000 risk assessment for vinyl chloride. Overall, we find that in many cases, steady-state solutions exactly reproduce or closely approximate the solutions using the full PBPK model, while being substantially more transparent. Subsequent work will examine the utility of steady-state solutions for analyzing cross-species extrapolation and intraspecies variability.     

Dynamics of Market Power and Concentration Profiles

This paper examines some of the economic and econometric issues that arise in attempting to measure the degree of concentration in an industry and its dynamic evolution. A general axiomatic basis is developed. We offer new measures of concentration over aggregated periods of time and provide a sound statistical basis for inferences. Concentration is one aspect of the problem of measuring “market power” within an industry. Modern economic analysis of antitrust issues does not focus only on the level of concentration, but still must examine the issue carefully. We contrast concentration at a point in time with a dynamic profile of change in the distribution of shares in a given market. Our methods are demonstrated with an application to the US steel industry.     

A general method to determine sampling windows for nonlinear mixed effects models with an application to population pharmacokinetic studies

Optimal design methods have been proposed to determine the best sampling times when sparse blood sampling is required in clinical pharmacokinetic studies. However, the optimal blood sampling time points may not be feasible in clinical practice. Sampling windows, a time interval for blood sample collection, have been proposed to provide flexibility in blood sampling times while preserving efficient parameter estimation. Because of the complexity of the population pharmacokinetic models, which are generally nonlinear mixed effects models, there is no analytical solution available to determine sampling windows. We propose a method for determination of sampling windows based on MCMC sampling techniques. The proposed method attains a stationary distribution rapidly and provides time-sensitive windows around the optimal design points. The proposed method is applicable to determine sampling windows for any nonlinear mixed effects model although our work focuses on an application to population pharmacokinetic models.