Survival analysis with delayed entry in selected families with application to human longevity

In the field of aging research, family-based sampling study designs are commonly used to study the lifespans of long-lived family members. However, the specific sampling procedure should be carefully taken into account in order to avoid biases. This work is motivated by the Leiden Longevity Study, a family-based cohort of long-lived siblings. Families were invited to participate in the study if at least two siblings were ‘long-lived’, where ‘long-lived’ meant being older than 89 years for men or older than 91 years for women. As a result, more than 400 families were included in the study and followed for around 10 years. For estimation of marker-specific survival probabilities and correlations among life times of family members, delayed entry due to outcome-dependent sampling mechanisms has to be taken into account. We consider shared frailty models to model left-truncated correlated survival data. The treatment of left truncation in shared frailty models is still an open issue and the literature on this topic is scarce. We show that the current approaches provide, in general, biased estimates and we propose a new method to tackle this selection problem by applying a correction on the likelihood estimation by means of inverse probability weighting at the family level

Discussion on the paper ‘Statistical contributions to bioinformatics: Design, modelling, structure learning and integration’ by Jeffrey S. Morris and Veerabhadran Baladandayuthapani

Bioinformatics is an important research area for statisticians. This discussion provides some additional topics to the paper, namely on statistical contributions to detect differential expressed genes, for protein structure prediction, and for the analysis of highly correlated features in Glycomics datasets

Gene analysis for longitudinal family data using random-effects models

We have extended our recently developed 2-step approach for gene-based analysis to the family design and to the analysis of rare variants. The goal of this approach is to study the joint effect of multiple single-nucleotide polymorphisms that belong to a gene. First, the information in a gene is summarized by 2 variables, namely the empirical Bayes estimate capturing common variation and the number of rare variants. By using random effects for the common variants, our approach acknowledges the within-gene correlations. In the second step, the 2 summaries were included as covariates in linear mixed models. To test the null hypothesis of no association, a multivariate Wald test was applied. We analyzed the simulated data sets to assess the performance of the method. Then we applied the method to the real data set and identified a significant association between FRMD4B and diastolic blood pressure (p-value = 8.3 × 10(-12))

Genetic, household and spatial clustering of leprosy on an island in Indonesia: a population-based study

Abstract Background It is generally accepted that genetic factors play a role in susceptibility to both leprosy per se and leprosy type, but only few studies have tempted to quantify this. Estimating the contribution of genetic factors to clustering of leprosy within families is difficult since these persons often share the same environment. The first aim of this study was to test which correlation structure (genetic, household or spatial) gives the best explanation for the distribution of leprosy patients and seropositive persons and second to quantify the role of genetic factors in the occurrence of leprosy and seropositivity. Methods The three correlation structures were proposed for population data (n = 560), collected on a geographically isolated island highly endemic for leprosy, to explain the distribution of leprosy per se, leprosy type and persons harbouring Mycobacterium leprae-specific antibodies. Heritability estimates and risk ratios for siblings were calculated to quantify the genetic effect. Leprosy was clinically diagnosed and specific anti-M. leprae antibodies were measured using ELISA. Results For leprosy per se in the total population the genetic correlation structure fitted best. In the population with relative stable household status (persons under 21 years and above 39 years) all structures were significant. For multibacillary leprosy (MB) genetic factors seemed more important than for paucibacillary leprosy. Seropositivity could be explained best by the spatial model, but the genetic model was also significant. Heritability was 57% for leprosy per se and 31% for seropositivity. Conclusion Genetic factors seem to play an important role in the clustering of patients with a more advanced form of leprosy, and they could explain more than half of the total phenotypic variance.</p

On estimation of covariance function for functional data with detection limits

In many studies on disease progression, biomarkers are restricted by detection limits, hence informatively missing. Current approaches ignore the problem by just filling in the value of the detection limit for the missing observations for the estimation of the mean and covariance function, which yield inaccurate estimation. Inspired by our recent work [Liu and Houwing-Duistermaat (2022), ‘Fast Estimators for the Mean Function for Functional Data with Detection Limits’, Stat, e467.] in which novel estimators for mean function for data subject to detection limit are proposed, in this paper, we will propose a novel estimator for the covariance function for sparse and dense data subject to a detection limit. We will derive the asymptotic properties of the estimator. We will compare our method to the standard method, which ignores the detection limit, via simulations. We will illustrate the new approach by analysing biomarker data subject to a detection limit. In contrast to the standard method, our method appeared to provide more accurate estimates of the covariance. Moreover its computation time is small

Рідке мило на основі соапстоків після нейтралізації олій та жирів в нейтралізуючому розчині, що містить етанол

Development and application of statistical models for medical scientific researc

Community deworming alleviates geohelminth-induced immune hyporesponsiveness

In cross-sectional studies, chronic helminth infections have been associated with immunological hyporesponsiveness that can affect responses to unrelated antigens. To study the immunological effects of deworming, we conducted a cluster-randomized, double-blind, placebo-controlled trial in Indonesia and assigned 954 households to receive albendazole or placebo once every 3 mo for 2 y. Helminth-specific and nonspecific whole-blood cytokine responses were assessed in 1,059 subjects of all ages, whereas phenotyping of regulatory molecules was undertaken in 121 school-aged children. All measurements were performed before and at 9 and 21 mo after initiation of treatment. Anthelmintic treatment resulted in significant increases in proinflammatory cytokine responses to Plasmodium falciparum-infected red blood cells (PfRBCs) and mitogen, with the largest effect on TNF responses to PfRBCs at 9 mo—estimate [95% confidence interval], 0.37 [0.21–0.53], P value over time (Ptime) < 0.0001. Although the frequency of regulatory T cells did not change after treatment, there was a significant decline in the expression of the inhibitory molecule cytotoxic T lymphocyte-associated antigen 4 (CTLA-4) on CD4+ T cells of albendazole-treated individuals, –0.060 [–0.107 to –0.013] and –0.057 [–0.105 to –0.008] at 9 and 21 mo, respectively; Ptime = 0.017. This trial shows the capacity of helminths to up-regulate inhibitory molecules and to suppress proinflammatory immune responses in humans. This could help to explain the inferior immunological responses to vaccines and lower prevalence of inflammatory diseases in low- compared with high-income countries