Meningococcal X polysaccharide quantification by high-performance anion-exchange chromatography using synthetic N-acetylglucosamine-4-phosphate as standard

A method for meningococcal X (MenX) polysaccharide quantification by high-performance anion-exchange chromatography with pulsed amperometric detection (HPAEC–PAD) is described. The polysaccharide is hydrolyzed by strong acidic treatment, and the peak of glucosamine-4-phosphate (4P-GlcN) is detected and measured after chromatography. In the selected conditions of hydrolysis, 4P-GlcN is the prevalent species formed, with GlcN detected for less than 5% in moles. As standard for the analysis, the monomeric unit of MenX polysaccharide, N-acetylglucosamine-4-phosphate (4P-GlcNAc), was used. This method for MenX quantification is highly selective and sensitive, and it constitutes an important analytical tool for the development of a conjugate vaccine against MenX.     

A preliminary method for estimating the age of brown kiwi (Apteryx mantelli) embryos

Morphological features of a collection of unknown-age wild kiwi (Apteryx mantelli) embryos from early development to point of hatch are described. Using these features, we assign developmental stages to each embryo and compare the progress of development to similar-staged ostrich (Struthio camelus) and chicken (Gallus gallus) embryos. Two ageing schemes for the kiwi embryos are developed by comparing measurements of their hindlimb segments, bills and crown–rump lengths with those of ostrich and chicken embryos at various stages of development. One of the 20 kiwi embryos was of known age. Both the ostrich model and the chicken model gave identical predictions for the marker and four other embryos. Developmental timing of some features differed between all three species, most markedly in the bill, with growth in the kiwi bill being relatively faster to achieve its larger relative and absolute size at hatch.     

Nonparametric estimation in an “illness‐death” model when all transition times are interval censored

We develop nonparametric maximum likelihood estimation for the parameters of an irreversible Markov chain on states from the observations with interval censored times of 0 → 1, 0 → 2 and 1 → 2 transitions. The distinguishing aspect of the data is that, in addition to all transition times being interval censored, the times of two events (0 → 1 and 1 → 2 transitions) can be censored into the same interval. This development was motivated by a common data structure in oral health research, here specifically illustrated by the data from a prospective cohort study on the longevity of dental veneers. Using the self‐consistency algorithm we obtain the maximum likelihood estimators of the cumulative incidences of the times to events 1 and 2 and of the intensity of the 1 → 2 transition. This work generalizes previous results on the estimation in an “illness‐death” model from interval censored observations.     

Transience in the humpback whale population of New Caledonia and implications for abundance estimation

A phenomenon of transience in the humpback whale population breeding in New Caledonia has been highlighted in recent analyses. We used these data to illustrate the risk of flawed inference when transience is not properly accounted for in abundance estimation of resident populations. Transients are commonly defined as individuals that pass through the sampling area once, i.e., have a null probability of being caught again, and therefore induce heterogeneity in the detection process. The presence of transients can lead to severe bias in the estimation of abundance and we demonstrate how to correct for this feature when estimating abundance of resident populations. In New Caledonia, very different conclusions about the number of resident whales in the southern lagoon between 1999 and 2005 are obtained when the abundance estimate accounts for the transient whales. Without correction, the estimates of the abundance were up to twice as high across all years compared to the estimates of the resident population when a correction for transients had been incorporated. Having reliable population estimates when assessing the status of endangered species is essential in documenting recovery and monitoring of population trends. Therefore, we encourage researchers to account for transients when reporting abundances of resident populations.     

Experimental Parameter Estimation Method for Nonlinear Viscoelastic Composite Material Models: An Application on Arterial Tissue

This study is aimed at setting a method of experimental parameter estimation for large-deforming nonlinear viscoelastic continuous fibre-reinforced composite material model. Specifically, arterial tissue was investigated during experimental research and parameter estimation studies, due to medical, scientific and socio-economic importance of soft tissue research. Using analytical formulations for specimens under combined inflation/extension/torsion on thick-walled cylindrical tubes, in vitro experiments were carried out with fresh sheep arterial segments, and parameter estimation procedures were carried out on experimental data. Model restrictions were pointed out using outcomes from parameter estimation. Needs for further studies that can be developed are discussed.     

Graphical procedures for evaluating overall and subject-specific incremental values from new predictors with censored event time data

Quantitative procedures for evaluating added values from new markers over a conventional risk scoring system for predicting event rates at specific time points have been extensively studied. However, a single summary statistic, for example, the area under the receiver operating characteristic curve or its derivatives, may not provide a clear picture about the relationship between the conventional and the new risk scoring systems. When there are no censored event time observations in the data, two simple scatterplots with individual conventional and new scores for "cases" and "controls" provide valuable information regarding the overall and the subject-specific level incremental values from the new markers. Unfortunately, in the presence of censoring, it is not clear how to construct such plots. In this article, we propose a nonparametric estimation procedure for the distributions of the differences between two risk scores conditional on the conventional score. The resulting quantile curves of these differences over the subject-specific conventional score provide extra information about the overall added value from the new marker. They also help us to identify a subgroup of future subjects who need the new predictors, especially when there is no unified utility function available for cost-risk-benefit decision making. The procedure is illustrated with two data sets. The first is from a well-known Mayo Clinic primary biliary cirrhosis liver study. The second is from a recent breast cancer study on evaluating the added value from a gene score, which is relatively expensive to measure compared with the routinely used clinical biomarkers for predicting the patient's survival after surgery.     

Estimating Subject‐Specific Dependent Competing Risk Profile with Censored Event Time Observations

Summary In a longitudinal study, suppose that the primary endpoint is the time to a specific event. This response variable, however, may be censored by an independent censoring variable or by the occurrence of one of several dependent competing events. For each study subject, a set of baseline covariates is collected. The question is how to construct a reliable prediction rule for the future subject's profile of all competing risks of interest at a specific time point for risk‐benefit decision making. In this article, we propose a two‐stage procedure to make inferences about such subject‐specific profiles. For the first step, we use a parametric model to obtain a univariate risk index score system. We then estimate consistently the average competing risks for subjects who have the same parametric index score via a nonparametric function estimation procedure. We illustrate this new proposal with the data from a randomized clinical trial for evaluating the efficacy of a treatment for prostate cancer. The primary endpoint for this study was the time to prostate cancer death, but had two types of dependent competing events, one from cardiovascular death and the other from death of other causes.     

Variance estimation for systematic designs in spatial surveys

Summary In spatial surveys for estimating the density of objects in a survey region, systematic designs will generally yield lower variance than random designs. However, estimating the systematic variance is well known to be a difficult problem. Existing methods tend to overestimate the variance, so although the variance is genuinely reduced, it is over‐reported, and the gain from the more efficient design is lost. The current approaches to estimating a systematic variance for spatial surveys are to approximate the systematic design by a random design, or approximate it by a stratified design. Previous work has shown that approximation by a random design can perform very poorly, while approximation by a stratified design is an improvement but can still be severely biased in some situations. We develop a new estimator based on modeling the encounter process over space. The new “striplet” estimator has negligible bias and excellent precision in a wide range of simulation scenarios, including strip‐sampling, distance‐sampling, and quadrat‐sampling surveys, and including populations that are highly trended or have strong aggregation of objects. We apply the new estimator to survey data for the spotted hyena (Crocuta crocuta) in the Serengeti National Park, Tanzania, and find that the reported coefficient of variation for estimated density is 20% using approximation by a random design, 17% using approximation by a stratified design, and 11% using the new striplet estimator. This large reduction in reported variance is verified by simulation.     

Estimating mean cost using auxiliary covariates

We study the estimation of mean medical cost when censoring is dependent and a large amount of auxiliary information is present. Under missing at random assumption, we propose semiparametric working models to obtain low-dimensional summarized scores. An estimator for the mean total cost can be derived nonparametrically conditional on the summarized scores. We show that when either the two working models for cost-survival process or the model for censoring distribution is correct, the estimator is consistent and asymptotically normal. Small-sample performance of the proposed method is evaluated via simulation studies. Finally, our approach is applied to analyze a real data set in health economics.     

A likelihood approach to estimating animal density from binary acoustic transects

We propose an approximate maximum likelihood method for estimating animal density and abundance from binary passive acoustic transects, when both the probability of detection and the range of detection are unknown. The transect survey is purposely designed so that successive data points are dependent, and this dependence is exploited to simultaneously estimate density, range of detection, and probability of detection. The data are assumed to follow a homogeneous Poisson process in space, and a second-order Markov approximation to the likelihood is used. Simulations show that this method has small bias under the assumptions used to derive the likelihood, although it performs better when the probability of detection is close to 1. The effects of violations of these assumptions are also investigated, and the approach is found to be sensitive to spatial trends in density and clustering. The method is illustrated using real acoustic data from a survey of sperm and humpback whales.