Analysis of dichotomous outcome data for community intervention studies

Community intervention trials are becoming increasingly popular as a tool for evaluating the effectiveness of health education and intervention strategies. Typically, units such as households, schools, towns, counties, are randomized to receive either intervention or control, then outcomes are measured on individuals within each of the units of randomization. It is well recognized that the design and analysis of such studies must account for the clustering of subjects within the units of randomization. Furthermore, there are usually both subject level and cluster level covariates that must be considered in the modelling process. While suitable methods are available for continuous outcomes, data analysis is more complicated when dichotomous outcomes are measured on each subject. This paper will compare and contrast several of the available methods that can be applied in such settings, including random effects models, generalized estimating equations and methods based on the calculation of 'design effects', as implemented in the computer package SUDAAN. For completeness, the paper will also compare these methods of analysis with more simplistic approaches based on the summary statistics. All the methods will be applied to a case study based on an adolescent anti-smoking intervention in Australia. The paper concludes with some general discussion and recommendations for routine design and analysis.     

Inference for pth‐order random coefficient integer‐valued autoregressive processes

Abstract. A pth‐order random coefficient integer‐valued autoregressive [RCINAR(p)] model is proposed for count data. Stationarity and ergodicity properties are established. Maximum likelihood, conditional least squares, modified quasi‐likelihood and generalized method of moments are used to estimate the model parameters. Asymptotic properties of the estimators are derived. Simulation results on the comparison of the estimators are reported. The models are applied to two real data sets.     

Meta-analyses involving cluster randomization trials: a review of published literature in health care

Throughout the 1980s and 1990s cluster randomization trials have been increasingly used to evaluate effectiveness of health care intervention. Such trials have raised several methodologic challenges in analysis. Meta-analyses involving cluster randomization trials are becoming common in the area of health care intervention. However, as yet there has been no empirical evidence of current practice in the meta-analyses. Thus a review was performed to identify and examine synthesis approaches of meta-analyses involving cluster randomization trials in the published literature. Electronic databases were searched for meta-analyses involving cluster randomization trials from the earliest date available to 2000. Once a meta-analysis was identified, papers on the relevant cluster randomization trials included were also requested. Each of the original papers of cluster randomization trials included was examined for its randomized design and unit, and adjustment for clustering effect in analysis. Each of the selected meta-analyses was then evaluated as to its synthesis concerning clustering effect. In total, 25 eligible meta-analyses were reviewed. Of these, 15 meta-analyses reported simple conventional methods of the fixed-effect model as method of analysis, while six did not incorporate the cluster randomization trial results in the synthesis methods but described the trial results individually. Three meta-analyses attempted to account for the clustering effect in the synthesis methods but approaches were in arbitrary. Fifteen meta-analyses included more than one cluster randomization trial, each of which included cluster randomization trials with a mixture of randomized designs and units, and units of analysis. These mixture situations might increase heterogeneity, but have not been considered in any meta-analysis. Some methods dealing with a binary outcome for some specific situations have been discussed. In conclusion, some difficulties in the quantitative synthesis procedures were found in the meta-analyses involving cluster randomization trials. Recommendations in the applications of approaches to some specific situations in a binary outcome variable have also been provided. There are still, however, several methodologic issues of the meta-analyses involving cluster randomization trials that need to be investigated further.     

Interventions in INGARCH processes

We study the problem of intervention effects generating various types of outliers in a linear count time‐series model. This model belongs to the class of observation‐driven models and extends the class of Gaussian linear time‐series models within the exponential family framework. Studies about effects of covariates and interventions for count time‐series models have largely fallen behind, because the underlying process, whose behaviour determines the dynamics of the observed process, is not observed. We suggest a computationally feasible approach to these problems, focusing especially on the detection and estimation of sudden shifts and outliers. We consider three different scenarios, namely the detection of an intervention effect of a known type at a known time, the detection of an intervention effect when the type and the time are both unknown and the detection of multiple intervention effects. We develop score tests for the first scenario and a parametric bootstrap procedure based on the maximum of the different score test statistics for the second scenario. The third scenario is treated by a stepwise procedure, where we detect and correct intervention effects iteratively. The usefulness of the proposed methods is illustrated using simulated and real data examples.     

Factorial optimization of the effects of melt‐spinning conditions on biodegradable as‐spun aliphatic–aromatic copolyester fibers. III. Diameter,tensile properties,and thermal shrinkage

To model the melt‐spinning process of biodegradable as‐spun linear aliphatic–aromatic copolyester fibers, a fraction factorial experimental design and appropriate statistical analysis for the 32 screening trials involving five control parameters were used. Because of their central role in the production processes and end use textiles, it is important to simulate the mechanical and thermal shrinkage properties of AAC fibers. Concise statistical models of fiber behavior are based on factorial experimental design data. Process's data are collected, analyzed, and mathematical models created to predict the diameter, tenacity, elongation at break, modulus, and thermal shrinkage of the spun fiber in terms of random variables and their associated probability distributions. The theoretical regression models obtained form the main source code in the enhanced forecasting program, which presents the melt‐spinning process of aromatic–aliphatic copolyester fibers. Factorial statistical approaches, based on over indicated region levels of melt‐spinning process parameters, are given in terms of assumptions and theory to produce biodegradable, environmentally friendly fibers for different applications. © 2011 Wiley Periodicals, Inc. J Appl Polym Sci, 2011     

EPDM/PA硫化共混物力学行为的模拟

采用Rivlin模型对EPDM／聚酰胺（PA）硫化共混物的单轴拉伸力学行为进行模拟。结果表明，2个参数的Ri。lin模型可以较好地描述PA用量不超过30份时EPDM／PA静态硫化共混物的应力-应变行为；3和5个参数的Rivlin模型在PA试验用量范围内对静态硫化共混物的拟合效果很好。3个参数的Rivlin模型可以很好地描述EP-DM／PA动态硫化共混物的应力-应变行为，但PA用量过大时，EPDM／PA动态硫化共混物显示出明显的塑料特征，拟合曲线与试验数据有所偏离。     

Models for the compressibility of layered polymeric sponges

The characteristic sigmoid compressive stress-strain relationships of sponges and their layered arrays are described by two kinds of mathematical models having three experimentally determined parameters. Since sponge compression is not accompanied by a significant cross-sectional area expansion, it was assumed that the stress in a multilayered array is the same in each layer. This enables prediction of the array's stress-strain relationship from the parameters of the individual layers and their known thickness. The applicability of the method is demonstrated with experimental data of two kinds of double-layered arrays of polyurethane sponges. It is shown that the arrays' compressive behavior can be satisfactorily predicted irrespective of the mathematical form of the model and whether the strain is expressed as engineering strain or Hencky's.     

Convolution‐closed models for count time series with applications

There has recently been an upsurge of interest in time series models for count data. Many papers focus on the model with first‐order (Markov) dependence and Poisson innovations. Our paper considers practical models that can capture higher‐order dependence based on the work of Joe (1996). In this framework we are able to model both equidispersed and overdispersed marginal distributions of data. The latter is approached using generalized Poisson innovations. Central to the models is the use of the property of closure under convolution of certain families of random variables. The models can be thought of as stationary Markov chains of finite order. Parameter estimation is undertaken by maximum likelihood, inference procedures are considered and means of assessing model adequacy employed. Applications to two new data sets are provided.     

Interaction of a normally incident shock wave with a porous material layer on a solid wall

Interaction of an incident shock wave with a layer of a porous material located near a solid wall is studied with the use of two mathematical models. The layer is modeled by a mixture of solid and gaseous phases. The mathematical models used are the equations of mechanics of equilibrium heterogeneous media with allowance for the gas pressure only and the model of two compressible media with identical partial pressures of the components. Verification of the approaches used is performed by comparisons of numerical results with experimental data.     

Modeling of creep and shrinkage behavior of polymeric films used in magnetic tapes

For ever increasing high recording densities of magnetic tape drives, improved dimensional stability of the polymeric films used as magnetic tape substrates is required. During storage and use, creep and shrinkage occur simultaneously and it needs to be minimized. To obtain constitutive relationships for creep and shrinkage, these contributions need to be separated and modeled. A mathematical model based on Kelvin–Voigt models has been developed to characterize simultaneous creep and shrinkage behavior to obtain the constitutive relationships for creep and shrinkage. Experiments have been performed to separate out creep and shrinkage effects and this model has been used to compensate the effect of shrinkage on creep data and to get true creep data. The experimental creep and shrinkage data of various films have been modeled to obtain viscoelastic parameters. © 2003 Wiley Periodicals, Inc. J Appl Polym Sci 91: 78–88, 2004     

Poisson QMLE of Count Time Series Models

Regularity conditions are given for the consistency of the Poisson quasi‐maximum likelihood estimator of the conditional mean parameter of a count time series model. The asymptotic distribution of the estimator is studied when the parameter belongs to the interior of the parameter space and when it lies at the boundary. Tests for the significance of the parameters and for constant conditional mean are deduced. Applications to specific integer‐valued autoregressive (INAR) and integer‐valued generalized autoregressive conditional heteroscedasticity (INGARCH) models are considered. Numerical illustrations, Monte Carlo simulations and real data series are provided.     

Fibre reinforced nanocomposites: Mechanical properties of PA6/clay and glass fibre/PA6/clay nanocomposites

In this paper, the effect of two different reinforcements: clay at the nanoscale and glass fibres at the micro-scale, on the mechanical properties of PA/clay and GF/PA/clay are studied. The Halpin-Tsai model is used to predict the modulus of PA/Clay and GF/PA/Clay, both of which are influenced by two factors: reinforcement shape and volume fraction. The relationships between the modulus and reinforcement shape and volume fraction are discussed. Tensile modulus, measured in tensile tests is used to fit the Halpin-Tsai models. The results demonstrate a synergy between the reinforcements at the two different scales.     

Remarks upon thermokinetic parameters of the one-and two-dimensional mathematical models of heat transfer in a tubular flow apparatus with a packed bed

Considerable discrepancies have been observed between the published correlations concerning the overall heat transfer coefficient α, as well as the effective thermal conductivity λ_e,r and the wall heat transfer coefficient α_w, which are the respective thermokinetic parameters of the one- and two-dimensional mathematical models of heat transfer in a tubular flow apparatus with a packed bed.A precise method has been elaborated for evaluation of these parameters using the data collected in the same experiment. This method has allowed us to point out the reasons for the scatter of the empirical correlations published by several authors, and to verify the coincidence of the experimentally determined and theoretically predicted values of the model parameters for various beds; the best fitting of the data was observed for beds packed with spherical pellets. The theoretically developed relation between the values of the parameters of both the mathematical models has been verified experimentally and it was proved that, if the experiment is performed correctly, no correlation factor is needed in that relation, as has been proposed by several investigators.     

Prediction of the spray scrubbers’ performance in the gaseous and particulate scrubbing processes

In this study, the spray scrubber's performance in its two classical applications, i.e. gaseous pollutants scrubbing and aerosol removing processes has been simulated by developing two proper mathematical models. The droplets dynamic behavior has been modeled in the Lagrangian framework in which the PSI-Cell model has been applied to obtain the droplets concentration in each tower increment. In order to apply the Lagrangian approach, a mathematical model has been presented for the classical pressure nozzles. One of the unique advantages of the models is their capability to predict the liquid film formation. Moreover, the droplet size distribution and especially the nozzles’ real locations have been incorporated into the models and their probable effects have been investigated. The effects of liquid film formation on both the gaseous and particulate scrubbing efficiency have been probed as well. After validation of the model by some experimental data from the literature, the effect of different parameters such as nozzle locations, nozzle jet velocity and other parameters have been explained.     

Determination of ball mill residence time distributions form tracer data taken in closed-circuit operation

Based on a method which searches on the parameter values for models, the possible one- and two-detection system tracer methods for the determination of ball mill residence time distributions from data taken on closed-circuit recycle systems are treated. Mathematical relationships for the most promising approaches are developed for both methods. This includes deriving, demonstrating, and verifying (with existing simple analytical models of closed-circuit mills) analytical models of tracer input and output based on the use of tanks-in-series and finite stage models with the convolution integral principle. Simulated results for typical processes are given and non-linear search methods are described and demonstrated for determining mill residence time distribution model parameters.     

Embedded cluster Δ-XANES modeling of adsorption processes on Pt

Full multiple scattering models of X-ray absorption near edge spectra of Pt Janin clusters, with atop and bridge bound oxygen, were systematically improved by increasing the number of cluster atoms contributing X-ray photoelectrons from unity (limited-absorber model), to all six Janin cluster Pt atoms, and finally embedding the Janin cluster into a larger Pt reservoir with photoelectron contributions from all atoms. The simulated XANES of the adsorbate modified clusters were subtractively normalized to their respective clean clusters to yield Δ-XANES signatures. The sequential analysis of previously published limited-absorber model, and the isolated and embedded all atoms models provide insights concerning the relative contribution of surface vs. subsurface atoms to adsorbate induced ligand effects and charge compensation provided by the bulk lattice. Limitations to the application of Δ-XANES signatures to interpretation of Δ-XANES data obtained from an air-breathing fuel cell are discussed.     

Flexible and Robust Mixed Poisson INGARCH Models

In this article, we propose a general class of INteger‐valued Generalized AutoRegressive Conditional Heteroskedastic (INGARCH) models based on a flexible family of mixed Poisson (MP) distributions. Our proposed class of count time series models contains the negative binomial (NB) INGARCH process as particular case and open the possibility to introduce new models such as the Poisson‐inverse Gaussian (PIG) and Poisson generalized hyperbolic secant processes. In particular, the PIG INGARCH model is an interesting and robust alternative to the NB model. We explore first‐order and second‐order stationary properties of our MPINGARCH models and provide expressions for the autocorrelation function and mean and variance marginals. Conditions to ensure strict stationarity and ergodicity properties for our class of INGARCH models are established. We propose an Expectation‐Maximization algorithm to estimate the parameters and obtain the associated information matrix. Further, we discuss two additional estimation methods. Monte Carlo simulation studies are considered to evaluate the finite‐sample performance of the proposed estimators. We illustrate the flexibility and robustness of the MPINGARCH models through two real‐data applications about number of cases of Escherichia coli and Campylobacter infections. This article contains a Supporting Information.     

Competitive adsorption of protocatechuic acid and caffeic acid on C18 particles

Comparisons of protocatechuic acid (PA) and caffeic acid (CA) adsorption isotherm on C₁₈ column and competitive adsorption of the two compounds were investigated. By linear and nonlinear regression analysis, the experimental parameters in the equilibrium isotherms were estimated. Adsorption equilibrium data of the two compounds were investigated using six different models including linear, Langmuir, Freundlich, Langmuir-Freundlich, competitive Langmuir and Quadratic. In the moderate range of concentrations, the competitive Langmuir isotherm proved to be the best model for these experimental data. The regression coefficients of the competitive Langmuir adsorption isotherms were 0.9860 for PA and 0.9898 for CA, respectively. The coefficients obtained for the six isotherm models confirmed the superiority of the competitive Langmuir isotherm for analyzing the competitive adsorption data of solutes.     

Kinetic Modeling of Large‐Scale Reaction Systems

This review gives a brief account of basic ideas underlying approaches to analysis and modeling of large‐scale reaction systems. The emphasis is on model simplification and mechanism/dimension reduction via heuristic concepts and formal mathematical techniques. Among the key topics discussed are: top‐down and bottom‐up modeling approaches, graph/matrix representation of chemical reactions, mechanistic vs. pathways models, quantitative structure‐reactivity relationships, mathematical reduction of dimensionality, high‐fidelity surrogate models, continuum approximation, lumping of nonlinear kinetics, overall behavior/kinetics of many reactions, effect of pore diffusion, steady state multiplicity and stability. Some common features of dimension reduction methodologies are noted. Areas where further work will be valuable are identified.     

An alternative approach to estimating parameters in creep models of high‐density polyethylene

Polyethylene (PE) is increasingly used in structural applications due to its light weight and rust‐resistant nature. With growing demand for the use of PE as a structural material, there is a need for mathematical models that describe the mechanical behavior of this material. Curve fitting using a linear time‐dependent model is a common approach for modeling creep of PE at the macrostructural level. However, besides the point estimates of the model parameters and the (visual) fit of the model to experimental data, little else is learnt from the curve‐fitting approach. This work presents a rigorous statistical approach for modeling creep compliance of PE. Four high‐density PE resins used over a wide range of applications are studied. Linear viscoelastic modeling using the multi‐Kelvin element theory is examined in two forms: model linear in parameters and model nonlinear in parameters. With the application of valid statistical techniques, complex relationships between model parameters, largely unstudied before, are observed, such as evidence of a high degree of correlation among material parameters of the creep model. POLYM. ENG. SCI., 2011. © 2010 Society of Plastics Engineers.