The Youden index is a widely used measure in the framework of medical diagnostics, where the effectiveness of a biomarker (screening marker or predictor) for classifying a disease status is studied. When the biomarker is continuous, it is important to determine the threshold or cut-off point to be used in practice for the discrimination between diseased and healthy populations. We introduce two methods aimed at estimating the Youden index and its associated threshold. The first one is a modified version of a recent approach based on the delta method, and the second one is based on the adjusted empirical likelihood for quantiles in the setting of a two-sample problem. We also include CIs for both of them. In the simulation study, we compare both methods under different scenarios. Finally, a real example of prostatic cancer, well known in the literature, is analysed to provide the reader with a better understanding of the new methodology.

Continuous diagnostic tests are often used for discriminating between healthy and diseased populations. For this reason, it is useful to select an appropriate discrimination threshold. There are several optimality criteria: the North-West corner, the Youden index, the concordance probability and the symmetry point, among others. In this paper, we focus on the symmetry point that maximizes simultaneously the two types of correct classifications. We construct confidence intervals for this optimal cutpoint and its associated specificity and sensitivity indexes using two approaches: one based on the generalized pivotal quantity and the other on empirical likelihood. We perform a simulation study to check the practical behaviour of both methods and illustrate their use by means of three real biomedical datasets on melanoma, prostate cancer and coronary artery disease.

The literature in engineering and statistics is abounding in techniques for detecting and properly processing anomalous observations in the data. Most of these techniques have been developed in the framework of static models and it is only in recent years that we have seen attempts that address the presence of outliers in nonlinear time series. For a target tracking problem described by a nonlinear state-space model, we propose the online detection of outliers by including an outlier detection step within the standard particle filtering algorithm. The outlier detection step is implemented by a test involving a statistic of the predictive distribution of the observations, such as a concentration measure or an extreme upper quantile. We also provide asymptotic results about the convergence of the particle approximations of the predictive distribution (and its statistics) and assess the performance of the resulting algorithms by computer simulations of target tracking problems with signal power observations.

HLA-G is a non-classical class I HLA molecule that induces tolerance by acting on receptors of both innate and adaptive immune cells. When overexpressed in tumors, limits surveillance by the immune system. The HLA-G gene shows several polymorphisms involved in mRNA and protein levels. We decided to study the implication of two polymorphisms (rs371194629; 14bp INS/DEL and rs1063320; +3142 C/G) in paired tissue samples (tumoral and non-tumoral) from 107 Spanish patients with gastric adenocarcinoma and 58 healthy control individuals, to assess the possible association of the HLA-G gene with gastric adenocarcinoma susceptibility, disease progression and survival. The presence of somatic mutations involving these polymorphisms was also analyzed. The frequency of the 14bp DEL allele was increased in patients (70.0%) compared to controls (57.0%, p=0.025). In addition, the haplotype formed by the combination of the 14bp DEL/+3142 C variants is also increased in patients (54.1% vs 44.4%, p=0.034, OR=1.74 CI95% 1.05-2.89). Kaplan-Meier analysis revealed that 14bp DEL/DEL patients showed lower 5-year life-expectancy than INS/DEL or INS/INS (p=0.041). Adjusting for TNM staging (Cox regression analysis) disclosed a significant difference in death risk (p=0.03) with an expected hazard 2.6 times higher. Finally, no somatic mutations were found when comparing these polymorphisms in tumoral vs non-tumoral tissues, which indicates that this is a preexisting condition in patients and not a de novo, tumor-restricted, event. In conclusion, the variants predominant in patients were those increasing HLA-G mRNA stability and HLA-G expression, clearly involving this molecule in gastric adenocarcinoma susceptibility, disease progression and survival and making it a potential target for immunotherapeutic approaches.

Gastric cancer is the fifth most common type of malignancy in the world (over one million cases in 2018) and the third leading cause of cancer-related deaths worldwide (783 000 deaths in 2018). 1 In Spain, expected figures are 7963 estimated new cases in 2020 and 5809 deaths. Clinical symptoms of gastric cancer appear late in the evolution of the disease, and this may limit the early detection of the pathology and the patients' therapeutic options; thus, upon diagnosis of gastric cancer, prognosis is poor (5-year survival rate below 29%). The need for early diagnosis and prognosis criteria has led recent research to focus on the investigation of novel biomarkers which could help identify patients at risk of developing more threatening