Long-term effects of cochlear implantation on the intelligibility of speech in French-speaking children.

Our study compares the intelligibility of French-speaking children with a cochlear implant (N = 13) and age-matched children with typical hearing (N = 13) in a narrative task. This contrasts with previous studies in which speech intelligibility of children with cochlear implants is most often tested using repetition or reading tasks. Languages other than English are seldom considered. Their productions were graded by naive and expert listeners. The results show that (1) children with CIs have lower intelligibility, (2) early implantation is a predictor of good intelligibility, and (3) late implantation after two years of age does not prevent the children from eventually reaching good intelligibility.

Statistical Methodology for the Analysis of Repeated Duration Data in Behavioral Studies.

Purpose: Repeated duration data are frequently used in behavioral studies. Classical linear or log-linear mixed models are often inadequate to analyze such data, because they usually consist of nonnegative and skew-distributed variables. Therefore, we recommend use of a statistical methodology specific to duration data. Method: We propose a methodology based on Cox mixed models and written under the R language. This semiparametric model is indeed flexible enough to fit duration data. To compare log-linear and Cox mixed models in terms of goodness-of-fit on real data sets, we also provide a procedure based on simulations and quantile-quantile plots. Results: We present two examples from a data set of speech and gesture interactions, which illustrate the limitations of linear and log-linear mixed models, as compared to Cox models. The linear models are not validated on our data, whereas Cox models are. Moreover, in the second example, the Cox model exhibits a significant effect that the linear model does not. Conclusions: We provide methods to select the best-fitting models for repeated duration data and to compare statistical methodologies. In this study, we show that Cox models are best suited to the analysis of our data set.

Methodological approach for substantiating disease freedom in a heterogeneous small population. Application to ovine scrapie, a disease with a strong genetic susceptibility.

Demonstrating disease freedom is becoming important in different fields including animal disease control. Most methods consider sampling only from a homogeneous population in which each animal has the same probability of becoming infected. In this paper, we propose a new methodology to calculate the probability of detecting the disease if it is present in a heterogeneous population of small size with potentially different risk groups, differences in risk being defined using relative risks. To calculate this probability, for each possible arrangement of the infected animals in the different groups, the probability that all the animals tested are test-negative given this arrangement is multiplied by the probability that this arrangement occurs. The probability formula is developed using the assumption of a perfect test and hypergeometric sampling for finite small size populations. The methodology is applied to scrapie, a disease affecting small ruminants and characterized in sheep by a strong genetic susceptibility defining different risk groups. It illustrates that the genotypes of the tested animals influence heavily the confidence level of detecting scrapie. The results present the statistical power for substantiating disease freedom in a small heterogeneous population as a function of the design prevalence, the structure of the sample tested, the structure of the herd and the associated relative risks.

Comparison of strategies for substantiating freedom from scrapie in a sheep flock.

BACKGROUND: The public health threat represented by a potential circulation of bovine spongiform encephalopathy agent in sheep population has led European animal health authorities to launch large screening and genetic selection programmes. If demonstrated, such a circulation would have dramatic economic consequences for sheep breeding sector. In this context, it is important to evaluate the feasibility of qualification procedures that would allow sheep breeders demonstrating their flock is free from scrapie. Classical approaches, based on surveys designed to detect disease presence, do not account for scrapie specificities: the genetic variations of susceptibility and the absence of live diagnostic test routinely available. Adapting these approaches leads to a paradoxical situation in which a greater amount of testing is needed to substantiate disease freedom in genetically resistant flocks than in susceptible flocks, whereas probability of disease freedom is a priori higher in the former than in the latter. The goal of this study was to propose, evaluate and compare several qualification strategies for demonstrating a flock is free from scrapie. RESULTS: A probabilistic framework was defined that accounts for scrapie specificities and allows solving the preceding paradox. Six qualification strategies were defined that combine genotyping data, diagnostic tests results and flock pedigree. These were compared in two types of simulated flocks: resistant and susceptible flocks. Two strategies allowed demonstrating disease freedom in several years, for the majority of simulated flocks: a strategy in which all the flock animals are genotyped, and a strategy in which only founders animals are genotyped, the flock pedigree being known. In both cases, diagnostic tests are performed on culled animals. The less costly strategy varied according to the genetic context (resistant or susceptible) and to the relative costs of a genotyping exam and of a diagnostic test. CONCLUSION: This work demonstrates that combining data sources allows substantiating a flock is free from scrapie within a reasonable time frame. Qualification schemes could thus be a useful tool for voluntary or mandatory scrapie control programmes. However, there is no general strategy that would always minimize the costs and choice of the strategy should be adapted to local genetic conditions.