Bias-corrected estimators for proportion of true null hypotheses: application of adaptive FDR-controlling in segmented failure data.

Two recently introduced model-based bias-corrected estimators for proportion of true null hypotheses ( π 0 ) under multiple hypotheses testing scenario have been restructured for random observations under a suitable failure model, available for each of the common hypotheses. Based on stochastic ordering, a new motivation behind formulation of some related estimators for π 0 is given. The reduction of bias for the model-based estimators are theoretically justified and algorithms for computing the estimators are also presented. The estimators are also used to formulate a popular adaptive multiple testing procedure. Extensive numerical study supports superiority of the bias-corrected estimators. The necessity of the proper distributional assumption for the failure data in the context of the model-based bias-corrected method has been highlighted. A case-study is done with a real-life dataset in connection with reliability and warranty studies to demonstrate the applicability of the procedure, under a non-Gaussian setup. The results obtained are in line with the intuition and experience of the subject expert. An intriguing discussion has been attempted to conclude the article that also indicates the future scope of study.

An analysis of COVID-19 clusters in India : Two case studies on Nizamuddin and Dharavi.

BACKGROUND: In this study we cluster the districts of India in terms of the spread of COVID-19 and related variables such as population density and the number of specialty hospitals. Simulation using a compartment model is used to provide insight into differences in response to public health interventions. Two case studies of interest from Nizamuddin and Dharavi provide contrasting pictures of the success in curbing spread. METHODS: A cluster analysis of the worst affected districts in India provides insight about the similarities between them. The effects of public health interventions in flattening the curve in their respective states is studied using the individual contact SEIQHRF model, a stochastic individual compartment model which simulates disease prevalence in the susceptible, infected, recovered and fatal compartments. RESULTS: The clustering of hotspot districts provide homogeneous groups that can be discriminated in terms of number of cases and related covariates. The cluster analysis reveal that the distribution of number of COVID-19 hospitals in the districts does not correlate with the distribution of confirmed COVID-19 cases. From the SEIQHRF model for Nizamuddin we observe in the second phase the number of infected individuals had seen a multitudinous increase in the states where Nizamuddin attendees returned, increasing the risk of the disease spread. However, the simulations reveal that implementing administrative interventions, flatten the curve. In Dharavi, through tracing, tracking, testing and treating, massive breakout of COVID-19 was brought under control. CONCLUSIONS: The cluster analysis performed on the districts reveal homogeneous groups of districts that can be ranked based on the burden placed on the healthcare system in terms of number of confirmed cases, population density and number of hospitals dedicated to COVID-19 treatment. The study rounds up with two important case studies on Nizamuddin basti and Dharavi to illustrate the growth curve of COVID-19 in two very densely populated regions in India. In the case of Nizamuddin, the study showed that there was a manifold increase in the risk of infection. In contrast it is seen that there was a rapid decline in the number of cases in Dharavi within a span of about one month.

A powerful method to integrate genotype and gene expression data for dissecting the genetic architecture of a disease.

To decipher the genetic architecture of human disease, various types of omics data are generated. Two common omics data are genotypes and gene expression. Often genotype data for a large number of individuals and gene expression data for a few individuals are generated due to biological and technical reasons, leading to unequal sample sizes for different omics data. Unavailability of standard statistical procedure for integrating such datasets motivates us to propose a two-step multi-locus association method using latent variables. Our method is powerful than single/separate omics data analysis and it unravels comprehensively deep-seated signals through a single statistical model. Extensive simulation confirms that it is robust to various genetic models as its power increases with sample size and number of associated loci. It provides p-values very fast. Application to real dataset on psoriasis identifies 17 novel SNPs, functionally related to psoriasis-associated genes, at much smaller sample size than standard GWAS.