Genetic and environmental analysis of behavioral risk factors for adolescent drug use in a community twin sample.

We investigated the etiology of six problem behaviors that might facilitate an understanding of behavioral pathways to substance use and abuse in adolescents. These behavioral measures, classified as Conduct Problems, Hyperactivity, School Problems, Low Self-esteem, Neuroticism, and Social Withdrawal were the result of a previously reported (Siewert et al., 2003) modification of the Drug Use Screening Inventory (DUSI; Tarter, 1990; Tarter & Hegedus, 1991). We developed these measures as interpretable components of risk for substance use and abuse in a community based sample of 633 twin pairs, who were under the legal drinking age of 21 (mean age = 15.0 years). Using multivariate analyses, model comparisons indicated that these six behavioral measures could be thought of as two heritable, and genetically distinct, dimensions of problem behavior. Two closely competing models resulted from our analyses. The best fitting model hypothesized a general genetic factor loading on all 6 behavioral measures with a second genetic factor loading on only the three internalizing behavioral measures with loadings of 0.25-0.59 and 0.26-0.44, respectively. A second model, which fit the data almost as well, hypothesized one genetic factor loading only on the externalizing behavioral measures, and a second genetic factor loading only on the internalizing behavioral measures, with a correlation between the two latent factors of 0.75. Because our analyses show that there are two genetically distinct factors influencing these six problem behaviors, we anticipate that there may be different patterns of relationship of these factors to risk for substance use, abuse, and dependence.     

Estimating compliance to study medication from serum drug levels: application to an AIDS clinical trial of zidovudine.

This paper examines the use of serum drug levels to assess compliance to study medication in a clinical trial. We discuss problems of false-positivity, false-negativity, and bias that arise because of experimental errors in the drug assays, pharmacokinetic variations of the drug, and differential dosing levels. Basic concepts in probability are applied to derive a simple model that quantifies these problems. This model is used to obtain an estimate of compliance rate that corrects for these problems. However, derivation of this estimate requires additional information about false-positive and false-negative rates of the assay as well as some knowledge of the pharmacokinetic properties of the drug. We illustrate the evaluation of such a compliance estimate in the setting of an AIDS clinical trial of zidovudine (ZDV), in which some accessory data are available on the properties of ZDV serum assays and on the pharmacokinetic behavior of ZDV. We also describe a method that uses the accessory data to provide the additional information needed for computing the compliance estimate.     

A Time-Series DDP for Functional Proteomics Profiles

Summary Using a new type of array technology, the reverse phase protein array (RPPA), we measure time-course protein expression for a set of selected markers that are known to coregulate biological functions in a pathway structure. To accommodate the complex dependent nature of the data, including temporal correlation and pathway dependence for the protein markers, we propose a mixed effects model with temporal and protein-specific components. We develop a sequence of random probability measures (RPM) to account for the dependence in time of the protein expression measurements. Marginally, for each RPM we assume a Dirichlet process model. The dependence is introduced by defining multivariate beta distributions for the unnormalized weights of the stick-breaking representation. We also acknowledge the pathway dependence among proteins via a conditionally autoregressive model. Applying our model to the RPPA data, we reveal a pathway-dependent functional profile for the set of proteins as well as marginal expression profiles over time for individual markers.     

Sample size recalculation in multicenter randomized controlled clinical trials based on noncomparative data

Many late-phase clinical trials recruit subjects at multiple study sites. This introduces a hierarchical structure into the data that can result in a power-loss compared to a more homogeneous single-center trial. Building on a recently proposed approach to sample size determination, we suggest a sample size recalculation procedure for multicenter trials with continuous endpoints. The procedure estimates nuisance parameters at interim from noncomparative data and recalculates the sample size required based on these estimates. In contrast to other sample size calculation methods for multicenter trials, our approach assumes a mixed effects model and does not rely on balanced data within centers. It is therefore advantageous, especially for sample size recalculation at interim. We illustrate the proposed methodology by a study evaluating a diabetes management system. Monte Carlo simulations are carried out to evaluate operation characteristics of the sample size recalculation procedure using comparative as well as noncomparative data, assessing their dependence on parameters such as between-center heterogeneity, residual variance of observations, treatment effect size and number of centers. We compare two different estimators for between-center heterogeneity, an unadjusted and a bias-adjusted estimator, both based on quadratic forms. The type 1 error probability as well as statistical power are close to their nominal levels for all parameter combinations considered in our simulation study for the proposed unadjusted estimator, whereas the adjusted estimator exhibits some type 1 error rate inflation. Overall, the sample size recalculation procedure can be recommended to mitigate risks arising from misspecified nuisance parameters at the planning stage.     

Expected Shannon Entropy and Shannon Differentiation between Subpopulations for Neutral Genes under the Finite Island Model

Shannon entropy H and related measures are increasingly used in molecular ecology and population genetics because (1) unlike measures based on heterozygosity or allele number, these measures weigh alleles in proportion to their population fraction, thus capturing a previously-ignored aspect of allele frequency distributions that may be important in many applications; (2) these measures connect directly to the rich predictive mathematics of information theory; (3) Shannon entropy is completely additive and has an explicitly hierarchical nature; and (4) Shannon entropy-based differentiation measures obey strong monotonicity properties that heterozygosity-based measures lack. We derive simple new expressions for the expected values of the Shannon entropy of the equilibrium allele distribution at a neutral locus in a single isolated population under two models of mutation: the infinite allele model and the stepwise mutation model. Surprisingly, this complex stochastic system for each model has an entropy expressable as a simple combination of well-known mathematical functions. Moreover, entropy- and heterozygosity-based measures for each model are linked by simple relationships that are shown by simulations to be approximately valid even far from equilibrium. We also identify a bridge between the two models of mutation. We apply our approach to subdivided populations which follow the finite island model, obtaining the Shannon entropy of the equilibrium allele distributions of the subpopulations and of the total population. We also derive the expected mutual information and normalized mutual information (“Shannon differentiation”) between subpopulations at equilibrium, and identify the model parameters that determine them. We apply our measures to data from the common starling (Sturnus vulgaris) in Australia. Our measures provide a test for neutrality that is robust to violations of equilibrium assumptions, as verified on real world data from starlings.     

Measures and limits of models of fixation selection

Models of fixation selection are a central tool in the quest to understand how the human mind selects relevant information. Using this tool in the evaluation of competing claims often requires comparing different models' relative performance in predicting eye movements. However, studies use a wide variety of performance measures with markedly different properties, which makes a comparison difficult. We make three main contributions to this line of research: First we argue for a set of desirable properties, review commonly used measures, and conclude that no single measure unites all desirable properties. However the area under the ROC curve (a classification measure) and the KL-divergence (a distance measure of probability distributions) combine many desirable properties and allow a meaningful comparison of critical model performance. We give an analytical proof of the linearity of the ROC measure with respect to averaging over subjects and demonstrate an appropriate correction of entropy-based measures like KL-divergence for small sample sizes in the context of eye-tracking data. Second, we provide a lower bound and an upper bound of these measures, based on image-independent properties of fixation data and between subject consistency respectively. Based on these bounds it is possible to give a reference frame to judge the predictive power of a model of fixation selection. We provide open-source python code to compute the reference frame. Third, we show that the upper, between subject consistency bound holds only for models that predict averages of subject populations. Departing from this we show that incorporating subject-specific viewing behavior can generate predictions which surpass that upper bound. Taken together, these findings lay out the required information that allow a well-founded judgment of the quality of any model of fixation selection and should therefore be reported when a new model is introduced.     

The nonparametric Behrens–Fisher problem in partially complete clustered data

In randomized trials or observational studies involving clustered units, the assumption of independence within clusters is not practical. Existing parametric or semiparametric methods assume specific dependence structures within a cluster. Furthermore, parametric model assumptions may not even be realistic when data are measured in a nonmetric scale as commonly happens, for example, in quality‐of‐life outcomes. In this paper, nonparametric effect‐size measures for clustered data that allow meaningful and interpretable probabilistic comparisons of treatments or intervention programs will be introduced. The dependence among observations within a cluster can be arbitrary. Point estimators along with their asymptotic properties for computing confidence intervals and performing hypothesis test will be discussed. Small sample approximations that retain some of the optimal asymptotic behaviors will be presented. In our setup, some clusters may involve observations coming from both intervention groups (referred to as complete clusters), while others may contain observations from one group only (referred to as incomplete clusters). In deriving the asymptotic theories, we do not impose any relation in the rate of divergence of the numbers of complete and incomplete clusters. Simulations show favorable performance of the methods for arbitrary combinations of complete and incomplete clusters. The developed nonparametric methods are illustrated using data from a randomized trial of indoor wood smoke reduction to improve asthma symptoms and a cluster‐randomized trial for smoking cessation.     

Mechanical and gas-distribution behavior of a collateral ventilation model

We extended the theoretical analysis of Otis et al. (J. Appl. Physiol. 8: 427-443, 1956) to study the effects of collateral ventilation on lung mechanics and gas distribution. Equations were developed to express the effective compliance, the effective resistance, and the distribution of airflow and tidal volume in a two-compartment model incorporating a collateral communication. The analysis of the model showed that, in general, collateral ventilation tends to attenuate the degree of frequency dependence of compliance and resistance, the magnitude of this effect being dependent on the mechanical properties of the model, including collateral resistance. The influence of collateral ventilation is important when the model simulates the mechanical characteristics of the emphysematous lung (marked time-constant inequality with regionally high airway resistance, and relatively low collateral resistance). Under these conditions, a large fraction of the tidal volume of the high airway resistance lung compartment is contributed by the collateral communication. The effects of collateral ventilation on the mechanical behavior of the model are negligible when collateral resistance largely exceeds airway resistance (simulating experimental findings in normal lungs). The present theoretical data suggest that the use of equations based on a model incorporating collateral ventilation is justified, at least in predicting the mechanical and gas-distribution behavior of the lung in emphysema.     

Saccadic Eye Movement Characteristics in Adult Niemann-Pick Type C Disease: Relationships with Disease Severity and Brain Structural Measures

Niemann-Pick Type C disease (NPC) is a rare genetic disorder of lipid metabolism. A parameter related to horizontal saccadic peak velocity was one of the primary outcome measures in the clinical trial assessing miglustat as a treatment for NPC. Neuropathology is widespread in NPC, however, and could be expected to affect other saccadic parameters. We compared horizontal saccadic velocity, latency, gain, antisaccade error percentage and self-paced saccade generation in 9 adult NPC patients to data from 10 age-matched controls. These saccadic measures were correlated with appropriate MRI-derived brain structural measures (e.g., dorsolateral prefrontal cortex, frontal eye fields, supplemental eye fields, parietal eye fields, pons, midbrain and cerebellar vermis) and with measures of disease severity and duration. The best discriminators between groups were reflexive saccade gain and the two volitional saccade measures. Gain was also the strongest correlate with disease severity and duration. Most of the saccadic measures showed strongly significant correlations with neurophysiologically appropriate brain regions. While our patient sample is small, the apparent specificity of these relationships suggests that as new diagnostic methods and treatments become available for NPC, a broader range of saccadic measures may be useful tools for the assessment of disease progression and treatment efficacy.     

A simple model with myofilament compliance predicts activation-dependent crossbridge kinetics in skinned skeletal fibers

The contribution of thick and thin filaments to skeletal muscle fiber compliance has been shown to be significant. If similar to the compliance of cycling cross-bridges, myofilament compliance could explain the difference in time course of stiffness and force during the rise of tension in a tetanus as well as the difference in Ca(2+) sensitivity of force and stiffness and more rapid phase 2 tension recovery (r) at low Ca(2+) activation. To characterize the contribution of myofilament compliance to sarcomere compliance and isometric force kinetics, the Ca(2+)-activation dependence of sarcomere compliance in single glycerinated rabbit psoas fibers, in the presence of ATP (5.0 mM), was measured using rapid length steps. At steady sarcomere length, the dependence of sarcomere compliance on the level of Ca(2+)-activated force was similar in form to that observed for fibers in rigor where force was varied by changing length. Additionally, the ratio of stiffness/force was elevated at lower force (low [Ca(2+)]) and r was faster, compared with maximum activation. A simple series mechanical model of myofilament and cross-bridge compliance in which only strong cross-bridge binding was activation dependent was used to describe the data. The model fit the data and predicted that the observed activation dependence of r can be explained if myofilament compliance contributes 60-70% of the total fiber compliance, with no requirement that actomyosin kinetics be [Ca(2+)] dependent or that cooperative interactions contribute to strong cross-bridge binding.     

Additive mixed effect model for clustered failure time data

We propose an additive mixed effect model to analyze clustered failure time data. The proposed model assumes an additive structure and includes a random effect as an additional component. Our model imitates the commonly used mixed effect models in repeated measurement analysis but under the context of hazards regression; our model can also be considered as a parallel development of the gamma-frailty model in additive model structures. We develop estimating equations for parameter estimation and propose a way of assessing the distribution of the latent random effect in the presence of large clusters. We establish the asymptotic properties of the proposed estimator. The small sample performance of our method is demonstrated via a large number of simulation studies. Finally, we apply the proposed model to analyze data from a diabetic study and a treatment trial for congestive heart failure.     

Bayesian approaches to modeling the conditional dependence between multiple diagnostic tests

Many analyses of results from multiple diagnostic tests assume the tests are statistically independent conditional on the true disease status of the subject. This assumption may be violated in practice, especially in situations where none of the tests is a perfectly accurate gold standard. Classical inference for models accounting for the conditional dependence between tests requires that results from at least four different tests be used in order to obtain an identifiable solution, but it is not always feasible to have results from this many tests. We use a Bayesian approach to draw inferences about the disease prevalence and test properties while adjusting for the possibility of conditional dependence between tests, particularly when we have only two tests. We propose both fixed and random effects models. Since with fewer than four tests the problem is nonidentifiable, the posterior distributions are strongly dependent on the prior information about the test properties and the disease prevalence, even with large sample sizes. If the degree of correlation between the tests is known a priori with high precision, then our methods adjust for the dependence between the tests. Otherwise, our methods provide adjusted inferences that incorporate all of the uncertainty inherent in the problem, typically resulting in wider interval estimates. We illustrate our methods using data from a study on the prevalence of Strongyloides infection among Cambodian refugees to Canada.     

Semiparametric modelling and estimation of covariate-adjusted dependence between bivariate recurrent events

A time-dependent measure, termed the rate ratio, was proposed to assess the local dependence between two types of recurrent event processes in one-sample settings. However, the one-sample work does not consider modeling the dependence by covariates such as subject characteristics and treatments received. The focus of this paper is to understand how and in what magnitude the covariates influence the dependence strength for bivariate recurrent events. We propose the covariate-adjusted rate ratio, a measure of covariate-adjusted dependence. We propose a semiparametric regression model for jointly modeling the frequency and dependence of bivariate recurrent events: the first level is a proportional rates model for the marginal rates and the second level is a proportional rate ratio model for the dependence structure. We develop a pseudo-partial likelihood to estimate the parameters in the proportional rate ratio model. We establish the asymptotic properties of the estimators and evaluate the finite sample performance via simulation studies. We illustrate the proposed models and methods using a soft tissue sarcoma study that examines the effects of initial treatments on the marginal frequencies of local/distant sarcoma recurrence and the dependence structure between the two types of cancer recurrence.     

Dynamic model of the bronchial tree

We present a distributed model of the bronchial tree which simulates the global dynamic characteristics of the lung. Local mechanical characteristics of each airway are represented by RCL circuits and parameters of the electrical components are determined from local physiological data. The bronchi geometry is described by Weibel's symmetric model, the flow in each airway is assumed laminar and mixing effects at the bifurcations are neglected; the transpulmonary pressure is assumed to be sinusoidal. In simulations of quiet breathing the resistance to airflow is found to be dominant, the flow amplitude decreasing as breathing frequency increases, but remaining almost constant in all the generations. Simulations of ventilation through obstructed lungs show frequency dependence of the dynamic characteristics in very compliant lungs. The global resistance to airflow and the dynamic compliance of the bronchi decrease as the forced oscillation frequency increases in a pattern similar to in vivo measurements in diseased lungs. This may be an outcome of the RCL properties of the network rather than due to uneven distribution of mechanical properties of the lung.     

An information ratio-based goodness-of-fit test for copula models on censored data

Copula is a popular method for modeling the dependence among marginal distributions in multivariate censored data. As many copula models are available, it is essential to check if the chosen copula model fits the data well for analysis. Existing approaches to testing the fitness of copula models are mainly for complete or right-censored data. No formal goodness-of-fit (GOF) test exists for interval-censored or recurrent events data. We develop a general GOF test for copula-based survival models using the information ratio (IR) to address this research gap. It can be applied to any copula family with a parametric form, such as the frequently used Archimedean, Gaussian, and D-vine families. The test statistic is easy to calculate, and the test procedure is straightforward to implement. We establish the asymptotic properties of the test statistic. The simulation results show that the proposed test controls the type-I error well and achieves adequate power when the dependence strength is moderate to high. Finally, we apply our method to test various copula models in analyzing multiple real datasets. Our method consistently separates different copula models for all these datasets in terms of model fitness.     

Viscoelastic properties of skin in Mov-13 and Tsk mice

Viscoelastic properties of skin samples were measured in three types of mice (tight skin, Tsk, control and Mov-13), that are known to differ with regard to content of type I collagen. The experimental design used uniaxial stretching and measured the creep response and the complex compliance. The creep response was measured directly. The complex compliance was determined using a Wiener-Volterra constitutive model for each sample. The models were calculated from data obtained by applying a stress input having a pseudo-Gaussian waveform and measuring the strain response. The storage compliance of Mov-13 and control skin were similar and were greater than Tsk (p<0.001). The loss compliance of each group was significantly different (p<0.001) from each other group; Tsk had the lowest and control had the highest loss compliance. The phase angle of the Mov-13 and Tsk were similar and were less than the controls (p<0.001). The creep response was fit with a linear viscoelastic model. None of the parameters in the creep model differed between groups. The results indicate that gene-targeted and mutant animals have soft tissue mechanical phenotypes that differ in complex ways. Caution should be exercised when using such animals as models to explore the role of specific constituents on tissue properties.     

Relative accuracy of spatial predictive models for lynx Lynx canadensis derived using logistic regression-AIC,multiple criteria evaluation and Bayesian approaches

We compared probability surfaces derived using one set of environmental variables in three Geographic Information Systems (GIS) -based approaches: logistic regression and Akaike's Information Criterion (AIC),Multiple Criteria Evaluation (MCE),and Bayesian Analysis (specifically Dempster-Shafer theory). We used lynx Lynx canadensis as our focal species,and developed our environment relationship model using track data collected in Banff National Park,Alberta,Canada,during winters from 1997 to 2000. The accuracy of the three spatial models were compared using a contingency table method. We determined the percentage of cases in which both presence and absence points were correctly classified (overall accuracy),the failure to predict a species where it occurred (omission error) and the prediction of presence where there was absence (commission error). Our overall accuracy showed the logistic regression approach was the most accurate (74.51% ). The multiple criteria evaluation was intermediate (39.22%),while the Dempster-Shafer (D-S) theory model was the poorest (29.90%). However,omission and commission error tell us a different story: logistic regression had the lowest commission error,while D-S theory produced the lowest omission error. Our results provide evidence that habitat modellers should evaluate all three error measures when ascribing confidence in their model. We suggest that for our study area at least,the logistic regression model is optimal. However,where sample size is small or the species is very rare,it may also be useful to explore and/or use a more ecologically cautious modelling approach (e.g. Dempster-Shafer) that would over-predict,protect more sites,and thereby minimize the risk of missing critical habitat in conservation plans.     

Arterial remodeling in response to increased blood flow using a constituent-based model

Previous theoretical models of arterial remodeling in response to changes in blood flow were based on the assumption that material properties of the arterial wall remain unchanged during the remodeling process. According to experimental findings, however, remodeling due to increased flow is accompanied by alteration in the structural properties of elastin, which results in a decrease in its effective elastic stiffness. To account for these effects, we propose a predictive model of arterial remodeling hypothesizing that the variation in mechanical properties of elastin is initiated and driven by the deviation of the intimal shear stress from its baseline value. Geometrical remodeling restores the wall stress distribution as it was under normal flow conditions. A constrained mixture approach is followed. Artery is modeled as a thick-walled cylindrical tube made of non-linear, elastic, anisotropic and incompressible material. Data for a rabbit thoracic aorta have been employed. At the final adapted state, the model predicts a non-monotonic dependence of arterial compliance on the magnitude of flow. This result is in agreement with available experimental data in the literature.     

Analysis of Repeated Measurements with Ante-Dependence Covariance Models

This paper considers the use of ante-dependence models in problems with repeated measures through time. These are conditional regression models which reflect the dependence of a measure on some of the previous observations from the same subject. We present maximum likelihood estimators of the covariance matrix and procedures for selecting the order of ante-degendence based on penalized like-lihoods. Extensions to missing data situations are discussed. We propose Wald-type test statistics and apply them in two situations common in experiments with repeated measures: one with pre-study observations and another one with small sample size relative to the number of time periods. In these examples, tests assuming ante-dependence find effects which are not detected using competing procedures.     

Effect of lung volume on pulmonary mechanics in guinea pigs

The lung volume (VL) dependence of several dynamic pulmonary mechanical properties of the guinea pig lung were determined over the range of the vital capacity (10-100% VC) with the vagi intact and sectioned. We found dynamic compliance to be strongly VL dependent, decreasing as much as 85% between functional residual capacity (FRC) and total lung capacity (TLC). Below FRC, dynamic compliance either remained unchanged or decreased, depending upon the technique used in its measurement. Pulmonary resistance (RL) decreased monotonically with increasing VL, whereas pulmonary conductance was linearly related to VL. Conductance was much less sensitive to VL than compliance, increasing only 28% between FRC and TLC. The sensitivity of pulmonary conductance to VL was substantially increased by subtracting the resistance of the tracheal cannula from RL. Specific pulmonary conductance was not independent of VL but decreased approximately 45% over the range of the VC. Pulmonary inertance was found to be unaffected by VL. Extrapolation from these data indicate that small differences in FRC, which might be expected within and between studies relying on pulmonary mechanical measurements, would most strongly affect compliance estimates and only moderately alter resistance estimates. It also indicates that the use of specific pulmonary conductance does not remove VL as an independent variable.