Posture Development in Infants at Heightened versus Low Risk for Autism Spectrum Disorders

Evidence suggests that children and adults diagnosed with autism spectrum disorders (ASD) exhibit difficulties with postural control. Retrospective video studies of infants later diagnosed with ASD indicate that infants who eventually receive an ASD diagnosis exhibit delays in postural development. This study investigates early posture development prospectively and longitudinally in 22 infants at heightened biological risk for ASD (HR) and 18 infants with no such risk (Low Risk; LR). Four HR infants received an autism diagnosis (AD infants) at 36 months. Infants were videotaped at home at 6, 9, 12, and 14 months during everyday activities and play. All infant postures were coded and classified as to whether or not they were infant‐initiated. Relative to LR infants, HR infants were slower to develop skill in sitting and standing postures. AD infants exhibited substantial delays in the emergence of more advanced postures and initiated fewer posture changes. Because posture advances create opportunities for infants to interact with objects and people in new and progressively more sophisticated ways, postural delays may have cascading effects on opportunities for infant exploration and learning. These effects may be greater for infants with ASD, for whom posture delays are more significant.     

Entwickeln im Team – Effekte für den Einzelnen

This study tested the effects of a 2-day team building intervention on team members’ team identification, social support and collective self-efficacy. Members (n?=?32) of three teams from different sectors completed questionnaires before, directly after and six weeks after the team building intervention. Two teams served as control groups. Results showed lasting positive effects of team building on identification, support and collective self-efficacy in the training groups but no changes in the control groups.     

Efficient heterogeneous sampling for stochastic simulation with an illustration in health care applications

In modeling disease transmission, contacts are assumed to have different infection rates. A proper simulation must model the heterogeneity in the transmission rates. In this article, we present a computationally efficient algorithm that can be applied to a population with heterogeneous transmission rates. We conducted a simulation study to show that the algorithm is more efficient than other algorithms for sampling the disease transmission in a subset of the heterogeneous population. We use a valid stochastic model of pandemic influenza to illustrate the algorithm and to estimate the overall infection attack rates of influenza A (H1N1) in a Canadian city.     

Confounding in mixed factorial experiments through isomorphisms

The derivation of a simpie mexhoa for confounding in mixed factorial experiments from an isomorphism of finite abelian groups is presented. The theoretical bases of confounding procedures that use modular arithmetic for such experiments are compared.     

Using power transformations when approximating quantiles

Estimators are obtained tor quantiles of survival distributions. This is accomplished by approximating Lritr distribution of the transtorrneri data, where the transformation used is that of Box and Cox (1964). The normal approximation as in Box and Cox and, in addition, the extreme value approximation are considered. More generally, to use the methods given, the approximating distribution must come from a location-scale family. For some commonly used survival random variables T the performance of the above approximations are evaluated in terms of the ratio of the true quantiles of T to the estimated one, in the long run. This performance is also evaluated for lower quantiles using simulated lognormai, Weibull and gamma data. Several examples are given to illustrate the methodology herein, including one with actual data.     

Bayesian analysis of a multiple-recapture model

In the Bayesian analysis of a multiple-recapture census, different diffuse prior distributions can lead to markedly different inferences about the population size N. Through consideration of the Fisher information matrix it is shown that the number of captures in each sample typically provides little information about N. This suggests that if there is no prior information about capture probabilities, then knowledge of just the sample sizes and not the number of recaptures should leave the distribution of Nunchanged. A prior model that has this property is identified and the posterior distribution is examined. In particular, asymptotic estimates of the posterior mean and variance are derived. Differences between Bayesian and classical point and interval estimators are illustrated through examples.     

Computation of finite population parameters and approximate probability values for multi-response randomized block permutation procedures (mrbp)

An algorithm is presented for computing the finite population parameters and the approximate probability values associated with a recently-developed class of statistical inference techniques termed multi-response randomized block permutation procedures (MRBP).     

A monte carlo evaluation of response surface analysis based on paired-comparison data

The use of the logit transformation on paired-comparison data in the weighted least squares analysis of response surfaces for aesthetic qualities of products is discussed. Monte Carlo simulations are employed to investigate the small sample properties of the estimators and test statistics. A secondary objective of the Monte Carlo simulations is the comparison of two transformation procedures. The simulations are of standard-item paired-compar-ison experiments in which ties are not allowed.     

Model diagnostics for marginal regression analysis of correlated binary data

We propose several diagnostic methods for checking the adequacy of marginal regression models for analyzing correlated binary data. We use a parametric marginal model based on latent variables and derive the projection (hat) matrix, Cook's distance, various residuals and Mahalanobis distance between the observed binary responses and the estimated probabilities for a cluster. Emphasized are several graphical methods including the simulated Q-Q plot, the half-normal probability plot with a simulated envelope, and the partial residual plot. The methods are illustrated with a real life example.