Ranked set sampling (RSS) is known to increase the efficiency of the estimators while comparing it with simple random sampling. The problem of missingness creates a gap in the information that needs to be addressed before proceeding for estimation. Negligible amount of work has been carried out to deal with missingness utilizing RSS. This paper proposes some logarithmic type methods of imputation for the estimation of population mean under RSS using auxiliary information. The properties of the suggested imputation procedures are examined. A simulation study is accomplished to show that the proposed imputation procedures exhibit better results in comparison to some of the existing imputation procedures. Few real applications of the proposed imputation procedures is also provided to generalize the simulation study.

UNASSIGNED: Racial disparities in dementia outcomes persist in the United States. Targeting modifiable risk factors, including cardiovascular risk factors (CVRFs), is a conceivable way to reduce health disparities. Life course CVRFs are often higher in non-White adults and are associated with risk of dementia, but it is unknown whether they contribute to racial disparities in dementia and cognition.
UNASSIGNED: Using a pooled cohort of 4,159 White and 939 Black participants aged 65-95 years, we conducted a mediation analysis to estimate the proportional effect of race on dementia that is explained by four CVRFs imputed over the life course (20-49, 50-69, and 70-89 years of age): body mass index, fasting glucose, systolic blood pressure, and low-density lipoprotein cholesterol.
UNASSIGNED: Compared to White participants, Black participants had greater risk of dementia (adjusted OR = 1.37; 95% CI: 1.17-1.60). BMI and fasting glucose over the life course were significant mediators of the effect of race on dementia risk, mediating 39.1% (95% CI: 10.5-67.8%) and 8.2% (95% CI: 0.1-16.2%) of the effect, adjusted for sex and age. All four CVRFs together were also significant mediators of the effect of race on scores on global cognition and processing speed, accounting for approximately 11% of the effect.
UNASSIGNED: We found that CVRFs across the life course partially explain disparities in dementia risk and cognition in late-life. Improved prevention and treatment of CVRFs across the life course may be important to reduce health disparities for dementia.

Cephalosporin antibiotics are widely used in clinical settings, but they can cause hypersensitivity reactions, which may be influenced by genetic factors such as the expression of Human leukocyte antigen (HLA) molecules. This study aimed to investigate whether specific HLA alleles were associated with an increased risk of adverse reactions to cephalosporins among individuals in the Taiwanese population. This retrospective case-control study analyzed data from the Taiwan Precision Medicine Initiative (TPMI) on 27,933 individuals who received cephalosporin exposure and had HLA allele genotyping information available. Using logistic regression analyses, we examined the associations between HLA genotypes, comorbidities, allergy risk, and severity. Among the study population, 278 individuals had cephalosporin allergy and 2780 were in the control group. Our results indicated that certain HLA alleles, including HLA-B*55:02 (OR = 1.76, 95% CI 1.18-2.61, p = 0.005), HLA-C*01:02 (OR = 1.36, 95% CI 1.05-1.77, p = 0.018), and HLA-DQB1*06:09 (OR = 2.58, 95% CI 1.62-4.12, p < 0.001), were significantly associated with an increased risk of cephalosporin allergy reactions. Additionally, the HLA-C*01:02 allele genotype was significantly associated with a higher risk of severe allergy (OR = 2.33, 95% CI 1.05-5.15, p = 0.04). This study identified significant associations between HLA alleles and an increased risk of cephalosporin allergy, which can aid in early detection and prediction of adverse drug reactions to cephalosporins. Furthermore, our study highlights the importance of HLA typing in drug safety and expanding our knowledge of drug hypersensitivity syndromes.

Single-cell transcriptomics (scRNA-seq) is revolutionizing biological research, yet it faces challenges such as inefficient transcript capture and noise. To address these challenges, methods like neighbor averaging or graph diffusion are used. These methods often rely on k-nearest neighbor graphs from low-dimensional manifolds. However, scRNA-seq data suffer from the \'curse of dimensionality\', leading to the over-smoothing of data when using imputation methods. To overcome this, sc-PHENIX employs a PCA-UMAP diffusion method, which enhances the preservation of data structures and allows for a refined use of PCA dimensions and diffusion parameters (e.g., k-nearest neighbors, exponentiation of the Markov matrix) to minimize noise introduction. This approach enables a more accurate construction of the exponentiated Markov matrix (cell neighborhood graph), surpassing methods like MAGIC. sc-PHENIX significantly mitigates over-smoothing, as validated through various scRNA-seq datasets, demonstrating improved cell phenotype representation. Applied to a multicellular tumor spheroid dataset, sc-PHENIX identified known extreme phenotype states, showcasing its effectiveness. sc-PHENIX is open-source and available for use and modification.

Network embedding is a general-purpose machine learning technique that converts network data from non-Euclidean space to Euclidean space, facilitating downstream analyses for the networks. However, existing embedding methods are often optimization-based, with the embedding dimension determined in a heuristic or ad hoc way, which can cause potential bias in downstream statistical inference. Additionally, existing deep embedding methods can suffer from a nonidentifiability issue due to the universal approximation power of deep neural networks. We address these issues within a rigorous statistical framework. We treat the embedding vectors as missing data, reconstruct the network features using a sparse decoder, and simultaneously impute the embedding vectors and train the sparse decoder using an adaptive stochastic gradient Markov chain Monte Carlo (MCMC) algorithm. Under mild conditions, we show that the sparse decoder provides a parsimonious mapping from the embedding space to network features, enabling effective selection of the embedding dimension and overcoming the nonidentifiability issue encountered by existing deep embedding methods. Furthermore, we show that the embedding vectors converge weakly to a desired posterior distribution in the 2-Wasserstein distance, addressing the potential bias issue experienced by existing embedding methods. This work lays down the first theoretical foundation for network embedding within the framework of missing data imputation.

暂无摘要。

Tensor factorization is a dimensionality reduction method applied to multidimensional arrays. These methods are useful for identifying patterns within a variety of biomedical datasets due to their ability to preserve the organizational structure of experiments and therefore aid in generating meaningful insights. However, missing data in the datasets being analyzed can impose challenges. Tensor factorization can be performed with some level of missing data and reconstruct a complete tensor. However, while tensor methods may impute these missing values, the choice of fitting algorithm may influence the fidelity of these imputations. Previous approaches, based on alternating least squares with prefilled values or direct optimization, suffer from introduced bias or slow computational performance. In this study, we propose that censored least squares can better handle missing values with data structured in tensor form. We ran censored least squares on four different biological datasets and compared its performance against alternating least squares with prefilled values and direct optimization. We used the error of imputation and the ability to infer masked values to benchmark their missing data performance. Censored least squares appeared best suited for the analysis of high-dimensional biological data by accuracy and convergence metrics across several studies.

Kidney stone disease is a widespread urological disorder affecting millions globally. Timely diagnosis is crucial to avoid severe complications. Traditionally, renal stones are detected using computed tomography (CT), which, despite its effectiveness, is costly, resource-intensive, exposes patients to unnecessary radiation, and often results in delays due to radiology report wait times. This study presents a novel approach leveraging machine learning to detect renal stones early using routine laboratory test results. We utilized an extensive dataset comprising 2156 patient records from a Saudi Arabian hospital, featuring 15 attributes with challenges such as missing data and class imbalance. We evaluated various machine learning algorithms and imputation methods, including single and multiple imputations, as well as oversampling and undersampling techniques. Our results demonstrate that ensemble tree-based classifiers, specifically random forest (RF) and extra tree classifiers (ETree), outperform others with remarkable accuracy rates of 99%, recall rates of 98%, and F1 scores of 99% for RF, and 92% for ETree. This study underscores the potential of non-invasive, cost-effective laboratory tests for renal stone detection, promoting prompt and improved medical support.

Current methodologies of genome-wide single-nucleotide polymorphism (SNP) genotyping produce large amounts of missing data that may affect statistical inference and bias the outcome of experiments. Genotype imputation is routinely used in well-studied species to buffer the impact in downstream analysis, and several algorithms are available to fill in missing genotypes. The lack of reference haplotype panels precludes the use of these methods in genomic studies on non-model organisms. As an alternative, machine learning algorithms are employed to explore the genotype data and to estimate the missing genotypes. Here, we propose an imputation method based on self-organizing maps (SOM), a widely used neural networks formed by spatially distributed neurons that cluster similar inputs into close neurons. The method explores genotype datasets to select SNP loci to build binary vectors from the genotypes, and initializes and trains neural networks for each query missing SNP genotype. The SOM-derived clustering is then used to impute the best genotype. To automate the imputation process, we have implemented gtImputation, an open-source application programmed in Python3 and with a user-friendly GUI to facilitate the whole process. The method performance was validated by comparing its accuracy, precision and sensitivity on several benchmark genotype datasets with other available imputation algorithms. Our approach produced highly accurate and precise genotype imputations even for SNPs with alleles at low frequency and outperformed other algorithms, especially for datasets from mixed populations with unrelated individuals.

The landscape of survival analysis is constantly being revolutionized to answer biomedical challenges, most recently the statistical challenge of censored covariates rather than outcomes. There are many promising strategies to tackle censored covariates, including weighting, imputation, maximum likelihood, and Bayesian methods. Still, this is a relatively fresh area of research, different from the areas of censored outcomes (i.e., survival analysis) or missing covariates. In this review, we discuss the unique statistical challenges encountered when handling censored covariates and provide an in-depth review of existing methods designed to address those challenges. We emphasize each method\'s relative strengths and weaknesses, providing recommendations to help investigators pinpoint the best approach to handling censored covariates in their data.