Category: Nevin Manimala

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Bayesian group selection in logistic regression with application to MRI data analysis.

Biometrics. 2020 May 04;:

Authors: Lee K, Cao X

Abstract
We consider Bayesian logistic regression models with group-structured covariates. In high-dimensional settings, it is often assumed that only a small portion of groups are significant, thus consistent group selection is of significant importance. While consistent frequentist group selection methods have been proposed, theoretical properties of Bayesian group selection methods for logistic regression models have not been investigated yet. In this paper, we consider a hierarchical group spike and slab prior for logistic regression models in high-dimensional settings. Under mild conditions, we establish strong group selection consistency of the induced posterior, which is the first theoretical result in the Bayesian literature. Through simulation studies, we demonstrate that the proposed method outperforms existing state-of-the-art methods in various settings. We further apply our method to an MRI data set for predicting Parkinson’s disease and show its benefits over other contenders. This article is protected by copyright. All rights reserved.

PMID: 32365231 [PubMed – as supplied by publisher]

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Dynamic statistical model for predicting the risk of death among older Chinese people, using longitudinal repeated measures of the frailty index: a prospective cohort study.

Age Ageing. 2020 May 04;:

Authors: Chen Q, Tang B, Zhai Y, Chen Y, Jin Z, Han H, Gao Y, Wu C, Chen T, He J

Abstract
BACKGROUND: Frailty is a common characteristic of older people with the ageing process. We aimed to develop and validate a dynamic statistical prediction model to calculate the risk of death in people aged ≥65 years, using a longitudinal frailty index (FI).
METHODS: One training dataset and three validation datasets from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) were used in our study. The training dataset and validation datasets 1 to 3 included data from 9,748, 7,459, 9,093 and 6,368 individuals, respectively. We used 35 health deficits to construct the FI and a longitudinal FI based on repeated measurement of FI at every wave of the CLHLS. A joint model was used to build a dynamic prediction model considering both baseline covariates and the longitudinal FI. Areas under time-dependent receiver operating characteristic curves (AUCs) and calibration curves were employed to assess the predictive performance of the model.
RESULTS: A linear mixed-effects model used time, sex, residence (city, town, or rural), living alone, smoking and alcohol consumption to calculate a subject-specific longitudinal FI. The dynamic prediction model was built using the longitudinal FI, age, residence, sex and an FI-age interaction term. The AUCs ranged from 0.64 to 0.84, and both the AUCs and the calibration curves showed good predictive ability.
CONCLUSIONS: We developed a dynamic prediction model that was able to update predictions of the risk of death as updated measurements of FI became available. This model could be used to estimate the risk of death in individuals aged >65 years.

PMID: 32365173 [PubMed – as supplied by publisher]

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Evaluation of participant reluctance, confidence, and self-reported behaviors since being trained in a pharmacy Mental Health First Aid initiative.

PLoS One. 2020;15(5):e0232627

Authors: Witry M, Karamese H, Pudlo A

Abstract
In the U.S., an estimated one in five individuals experience a mental illness annually which contribute to significant human and economic cost. Pharmacists serving in a public health capacity are positioned to provide first aid level intervention to people experiencing a mental health crisis. Research on pharmacy professionals (pharmacists, technicians, students) undergoing training in Mental Health First Aid (MHFA) can provide evidence of the potential benefits of such training. The objectives of this study were to 1) describe the reluctance and confidence to intervene in mental health crises of pharmacy professionals previously trained in MHFA, 2) describe their self-reported use of MHFA behaviors since becoming trained, and 3) describe participant open-ended feedback on their MHFA training.
MATERIALS AND METHODS: An electronic survey was disseminated in May and June, 2019 using a four-email sequence to pharmacy professionals who had completed MHFA training from one of five pharmacist MHFA trainers throughout 2018. Domains included demographics, six Likert-type reluctance items, seven Likert-type confidence items for performing MHFA skills, and frequency of using a set of nine MHFA skills since being trained. Prompts collected open-ended feedback related to MHFA experiences and training. Descriptive statistics were used for scaled and multiple-choice items and a basic content analysis was performed on the open-ended items to group them into similar topics.
RESULTS: Ninety-eight out of 227 participants responded to the survey yielding a response rate of 44%. Participants reported high levels of disagreement to a set of reluctance items for intervening and overall high levels of confidence in performing a range of MHFA skills. Participant self-reported use of a set of MHFA skills ranged from 19% to 82% since being trained in MHFA. Almost half (44%) of participants had asked someone if they were considering suicide. A majority (61%) also had referred someone to resources because of a mental health crisis. Open-ended responses included positive experiences alongside important challenges to using MHFA in practice and recommendations including additional training focused on the pharmacy setting.
CONCLUSIONS: Pharmacy professionals in this evaluation reported little reluctance and high confidence related to using MHFA training and reported use of MHFA skills since being trained.

PMID: 32365115 [PubMed – in process]

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Evaluation of biochemical profile of Chronic Kidney Disease of uncertain etiology in Sri Lanka.

PLoS One. 2020;15(5):e0232522

Authors: Fernando BNTW, Sudeshika TSH, Hettiarachchi TW, Badurdeen Z, Abeysekara TDJ, Abeysundara HTK, Jayasinghe S, Ranasighe S, Nanayakkara N

Abstract
Chronic Kidney Disease of uncertain etiology (CKDu) is an endemic, disease that mostly affects young agricultural workers in the rural dry zone of Sri Lanka. This study was designed to identify specific biochemical manifestations of CKDu cases. All (119) non-dialysis definite CKDu patients in Girandurukotte and Wilgamuwa were selected. Blood and urine samples were collected and measured biochemical parameters. All analyses were performed in IBM SPSS statistics version 23 (IBM Corp, USA). The median blood pressure was normal though nearly half of the patients (45.4%) who were in the advanced stages (Stage 3b, 4 and 5) of CKDu. Patients without a history of hypertension before the diagnosis of CKDu (100%) and minimal proteinuria (26%) are similar to the previous findings. Patients without a history of diabetes before the CKDu diagnosis had high percentages of diabetes (15.7%) and pre-diabetes (59.8%) and hence indicated the possibility of uremia induced impaired glucose intolerance in the rural areas of the country. There were 62.2% patients who had low vitamin D and only a minority had evidence of bone mineral diseases. Out of liver disease markers serum glutamic pyruvic transaminases (SGPT), serum glutamic oxaloacetic transaminases (SGOT), gamma-glutamyl transferase (GGT), and Lactic acid degydrogenase (LDH) had an inverse correlation with the advancement of the disease indicating subclinical liver disease. Osmolality in serum and urine showed a discrepancy despite > 50% of CKDu patients had increased their serum osmolality. The current study supports most of the previously described manifestations of CKDu. Moreover, some specific patterns have been identified which need to be validated in a larger group.

PMID: 32365131 [PubMed – in process]

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scDoc: correcting drop-out events in single-cell RNA-seq data.

Bioinformatics. 2020 May 04;:

Authors: Ran D, Zhang S, Lytal N, An L

Abstract
MOTIVATION: Single-cell RNA sequencing (scRNA-seq) has become an important tool to unravel cellular heterogeneity, discover new cell (sub)types, and understand cell development at single-cell resolution. However, one major challenge to scRNA-seq research is the presence of “drop-out” events, which usually is due to extremely low mRNA input or the stochastic nature of gene expression. In this paper, we present a novel Single-Cell RNA-seq Drop-Out Correction (scDoc) method, imputing drop-out events by borrowing information for the same gene from highly similar cells.
RESULTS: scDoc is the first method that directly involves drop-out information to accounting for cell-to-cell similarity estimation, which is crucial in scRNA-seq drop-out imputation but has not been appropriately examined. We evaluated the performance of scDoc using both simulated data and real scRNA-seq studies. Results show that scDoc outperforms the existing imputation methods in reference to data visualization, cell subpopulation identification, and differential expression detection in scRNA-seq data.
AVAILABILITY: R code is available at https://github.com/anlingUA/scDoc.
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID: 32365169 [PubMed – as supplied by publisher]

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Prediction of attempted suicide in men and women with crack-cocaine use disorder in Brazil.

PLoS One. 2020;15(5):e0232242

Authors: Roglio VS, Borges EN, Rabelo-da-Ponte FD, Ornell F, Scherer JN, Schuch JB, Passos IC, Sanvicente-Vieira B, Grassi-Oliveira R, von Diemen L, Pechansky F, Kessler FHP

Abstract
BACKGROUND: Suicide is a severe health problem, with high rates in individuals with addiction. Considering the lack of studies exploring suicide predictors in this population, we aimed to investigate factors associated with attempted suicide in inpatients diagnosed with cocaine use disorder using two analytical approaches.
METHODS: This is a cross-sectional study using a secondary database with 247 men and 442 women hospitalized for cocaine use disorder. Clinical assessment included the Addiction Severity Index, the Childhood Trauma Questionnaire, and the Structured Clinical Interview for the Diagnostic and Statistical Manual of Mental Disorders, totalling 58 variables. Descriptive Poisson regression and predictive Random Forest algorithm were used complementarily to estimate prevalence ratios and to build prediction models, respectively. All analyses were stratified by gender.
RESULTS: The prevalence of attempted suicide was 34% for men and 50% for women. In both genders, depression (PRM = 1.56, PRW = 1.27) and hallucinations (PRM = 1.80, PRW = 1.39) were factors associated with attempted suicide. Other specific factors were found for men and women, such as childhood trauma, aggression, and drug use severity. The men’s predictive model had prediction statistics of AUC = 0.68, Acc. = 0.66, Sens. = 0.82, Spec. = 0.50, PPV = 0.47 and NPV = 0.84. This model identified several variables as important predictors, mainly related to drug use severity. The women’s model had higher predictive power (AUC = 0.73 and all other statistics were equal to 0.71) and was parsimonious.
CONCLUSIONS: Our findings indicate that attempted suicide is associated with depression, hallucinations and childhood trauma in both genders. Also, it suggests that severity of drug use may be a moderator between predictors and suicide among men, while psychiatric issues shown to be more important for women.

PMID: 32365094 [PubMed – in process]

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A new linear regression-like residual for survival analysis, with application to genome wide association studies of time-to-event data.

PLoS One. 2020;15(5):e0232300

Authors: Vieland VJ, Seok SC, Stewart WCL

Abstract
In linear regression, a residual measures how far a subject’s observation is from expectation; in survival analysis, a subject’s Martingale or deviance residual is sometimes interpreted similarly. Here we consider ways in which a linear regression-like interpretation is not appropriate for Martingale and deviance residuals, and we develop a novel time-to-event residual which does have a linear regression-like interpretation. We illustrate the utility of this new residual via simulation of a time-to-event genome-wide association study, motivated by a real study seeking genetic modifiers of Duchenne Muscular Dystrophy. By virtue of its linear regression-like characteristics, our new residual may prove useful in other contexts as well.

PMID: 32365095 [PubMed – in process]

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Simultaneous SNP selection and adjustment for population structure in high dimensional prediction models.

PLoS Genet. 2020 May 04;16(5):e1008766

Authors: Bhatnagar SR, Yang Y, Lu T, Schurr E, Loredo-Osti JC, Forest M, Oualkacha K, Greenwood CMT

Abstract
Complex traits are known to be influenced by a combination of environmental factors and rare and common genetic variants. However, detection of such multivariate associations can be compromised by low statistical power and confounding by population structure. Linear mixed effects models (LMM) can account for correlations due to relatedness but have not been applicable in high-dimensional (HD) settings where the number of fixed effect predictors greatly exceeds the number of samples. False positives or false negatives can result from two-stage approaches, where the residuals estimated from a null model adjusted for the subjects’ relationship structure are subsequently used as the response in a standard penalized regression model. To overcome these challenges, we develop a general penalized LMM with a single random effect called ggmix for simultaneous SNP selection and adjustment for population structure in high dimensional prediction models. We develop a blockwise coordinate descent algorithm with automatic tuning parameter selection which is highly scalable, computationally efficient and has theoretical guarantees of convergence. Through simulations and three real data examples, we show that ggmix leads to more parsimonious models compared to the two-stage approach or principal component adjustment with better prediction accuracy. Our method performs well even in the presence of highly correlated markers, and when the causal SNPs are included in the kinship matrix. ggmix can be used to construct polygenic risk scores and select instrumental variables in Mendelian randomization studies. Our algorithms are available in an R package available on CRAN (https://cran.r-project.org/package=ggmix).

PMID: 32365090 [PubMed – as supplied by publisher]

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Association test using Copy Number Profile Curves (CONCUR) enhances power in rare copy number variant analysis.

PLoS Comput Biol. 2020 May 04;16(5):e1007797

Authors: Brucker A, Lu W, Marceau West R, Yu QY, Hsiao CK, Hsiao TH, Lin CH, Magnusson PKE, Sullivan PF, Szatkiewicz JP, Lu TP, Tzeng JY

Abstract
Copy number variants (CNVs) are the gain or loss of DNA segments in the genome that can vary in dosage and length. CNVs comprise a large proportion of variation in human genomes and impact health conditions. To detect rare CNV associations, kernel-based methods have been shown to be a powerful tool due to their flexibility in modeling the aggregate CNV effects, their ability to capture effects from different CNV features, and their accommodation of effect heterogeneity. To perform a kernel association test, a CNV locus needs to be defined so that locus-specific effects can be retained during aggregation. However, CNV loci are arbitrarily defined and different locus definitions can lead to different performance depending on the underlying effect patterns. In this work, we develop a new kernel-based test called CONCUR (i.e., copy number profile curve-based association test) that is free from a definition of locus and evaluates CNV-phenotype associations by comparing individuals’ copy number profiles across the genomic regions. CONCUR is built on the proposed concepts of “copy number profile curves” to describe the CNV profile of an individual, and the “common area under the curve (cAUC) kernel” to model the multi-feature CNV effects. The proposed method captures the effects of CNV dosage and length, accounts for the numerical nature of copy numbers, and accommodates between- and within-locus etiological heterogeneity without the need to define artificial CNV loci as required in current kernel methods. In a variety of simulation settings, CONCUR shows comparable or improved power over existing approaches. Real data analyses suggest that CONCUR is well powered to detect CNV effects in the Swedish Schizophrenia Study and the Taiwan Biobank.

PMID: 32365089 [PubMed – as supplied by publisher]

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Integration of cortical thickness data in a statistical shape model of the scapula.

Comput Methods Biomech Biomed Engin. 2020 May 04;:1-7

Authors: Pitocchi J, Wirix-Speetjens R, van Lenthe GH, Pérez MÁ

Abstract
Knowledge about bone morphology and bone quality of the scapula throughout the population is fundamental in the design of shoulder implants. In particular, regions with the best bone stock (cortical bone) are taken into account when planning the supporting screws, aiming for an optimal fixation. As an alternative to manual measurements, statistical shape models (SSMs) have been commonly used to describe shape variability within a population. However, explicitly including cortical thickness information in an SSM of the scapula still remains a challenge. Therefore, the goal of this study is to combine scapular bone shape and cortex morphology in an SSM. First, a method to estimate cortical thickness, based on HU (Hounsfield Unit) profile analysis, was developed and validated. Then, based on the manual segmentations of 32 healthy scapulae, a statistical shape model including cortical information was created and evaluated. Generalization, specificity and compactness were calculated in order to assess the quality of the SSM. The average cortical thickness of the SSM was 2.0 ± 0.63 mm. Generalization, specificity and compactness performances confirmed that the combined SSM was able to capture the bone quality changes in the population. In this work we integrated information on the cortical thickness in an SSM for the scapula. From the results we conclude that this methodology is a valuable tool for automatically generating a large population of scapulae and deducing statistics on the cortex. Hence, this SSM can be useful to automate implant design and screw placement in shoulder arthroplasty.

PMID: 32364819 [PubMed – as supplied by publisher]