cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.19111441
title: Variable Selection with Multiply-Imputed Datasets: Choosing Between Stacked and Grouped Methods

description: Penalized regression methods are used in many biomedical applications for variable selection and simultaneous coefficient estimation. However, missing data complicates the implementation of these methods, particularly when missingness is handled using multiple imputation. Applying a variable selection algorithm on each imputed dataset will likely lead to different sets of selected predictors. This article considers a general class of penalized objective functions which, by construction, force selection of the same variables across imputed datasets. By pooling objective functions across imputations, optimization is then performed jointly over all imputed datasets rather than separately for each dataset. We consider two objective function formulations that exist in the literature, which we will refer to as “stacked” and “grouped” objective functions. Building on existing work, we (i) derive and implement efficient cyclic coordinate descent and majorization-minimization optimization algorithms for continuous and binary outcome data, (ii) incorporate adaptive shrinkage penalties, (iii) compare these methods through simulation, and (iv) develop an R package <i>miselect</i>. Simulations demonstrate that the “stacked” approaches are more computationally efficient and have better estimation and selection properties. We apply these methods to data from the University of Michigan ALS Patients Biorepository aiming to identify the association between environmental pollutants and ALS risk. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.19111441.v2
title: Variable Selection with Multiply-Imputed Datasets: Choosing Between Stacked and Grouped Methods

description: Penalized regression methods are used in many biomedical applications for variable selection and simultaneous coefficient estimation. However, missing data complicates the implementation of these methods, particularly when missingness is handled using multiple imputation. Applying a variable selection algorithm on each imputed dataset will likely lead to different sets of selected predictors. This article considers a general class of penalized objective functions which, by construction, force selection of the same variables across imputed datasets. By pooling objective functions across imputations, optimization is then performed jointly over all imputed datasets rather than separately for each dataset. We consider two objective function formulations that exist in the literature, which we will refer to as “stacked” and “grouped” objective functions. Building on existing work, we (i) derive and implement efficient cyclic coordinate descent and majorization-minimization optimization algorithms for continuous and binary outcome data, (ii) incorporate adaptive shrinkage penalties, (iii) compare these methods through simulation, and (iv) develop an R package <i>miselect</i>. Simulations demonstrate that the “stacked” approaches are more computationally efficient and have better estimation and selection properties. We apply these methods to data from the University of Michigan ALS Patients Biorepository aiming to identify the association between environmental pollutants and ALS risk. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.19111441.v1
title: Variable selection with multiply-imputed datasets: choosing between stacked and grouped methods

description: Penalized regression methods are used in many biomedical applications for variable selection and simultaneous coefficient estimation. However, missing data complicates the implementation of these methods, particularly when missingness is handled using multiple imputation. Applying a variable selection algorithm on each imputed dataset will likely lead to different sets of selected predictors. This paper considers a general class of penalized objective functions which, by construction, force selection of the same variables across imputed datasets. By pooling objective functions across imputations, optimization is then performed jointly over all imputed datasets rather than separately for each dataset. We consider two objective function formulations that exist in the literature, which we will refer to as “stacked” and “grouped” objective functions. Building on existing work, we (a) derive and implement efficient cyclic coordinate descent and majorization-minimization optimization algorithms for continuous and binary outcome data, (b) incorporate adaptive shrinkage penalties, (c) compare these methods through simulation, and (d) develop an R package <i>miselect</i>. Simulations demonstrate that the “stacked” approaches are more computationally efficient and have better estimation and selection properties. We apply these methods to data from the University of Michigan ALS Patients Biorepository aiming to identify the association between environmental pollutants and ALS risk. Supplementary materials are available online.
DOI: 10.6084/m9.figshare.20424237
title: DATA

description: Raw data statistics content
DOI: 10.6084/m9.figshare.19470251
title: Are remote clinical assessments a feasible and acceptable method of assessment? A systematic review

description: The COVID-19 pandemic presented an enormous and immediate challenge to assessing clinical skills in healthcare professionals. Many institutions were unable to deliver established face-to-face assessment methods such as Objective Structured Clinical Examinations (OSCEs). Assessors needed to rapidly institute alternative assessment methods to ensure that candidates met the clinical competences required for progression. Using a systematic review, we aimed to evaluate the feasibility, and acceptability of remote methods of clinical skills assessment, including remote structured clinical assessments and the submission of video recordings. We searched for studies reporting on Remote Clinical Assessments or its variants in MEDLINE, Embase and The Cochrane library from 2000 to March 2021. Twenty eight studies were included in the review; 20 studies related to remote structured clinical examinations or OSCEs and 8 reported the use of video submissions. The participants of the different studies included medical students, nursing students, dental students and doctors in training. A variety of different online platforms were utilised including Zoom, Skype, webcams, and Adobe Connect online. The studies found that delivery of remote clinical assessments is possible and provides an alternative method of assessing many clinical skills, but most also acknowledge limitations and challenges. They are acceptable to both candidates and examiners, and where measured, show moderate agreement with on-site clinical assessments. Current evidence is based on studies with low methodological quality and for the most part, small sample sizes.
DOI: 10.6084/m9.figshare.19470251.v1
title: Are remote clinical assessments a feasible and acceptable method of assessment? A systematic review

description: The COVID-19 pandemic presented an enormous and immediate challenge to assessing clinical skills in healthcare professionals. Many institutions were unable to deliver established face-to-face assessment methods such as Objective Structured Clinical Examinations (OSCEs). Assessors needed to rapidly institute alternative assessment methods to ensure that candidates met the clinical competences required for progression. Using a systematic review, we aimed to evaluate the feasibility, and acceptability of remote methods of clinical skills assessment, including remote structured clinical assessments and the submission of video recordings. We searched for studies reporting on Remote Clinical Assessments or its variants in MEDLINE, Embase and The Cochrane library from 2000 to March 2021. Twenty eight studies were included in the review; 20 studies related to remote structured clinical examinations or OSCEs and 8 reported the use of video submissions. The participants of the different studies included medical students, nursing students, dental students and doctors in training. A variety of different online platforms were utilised including Zoom, Skype, webcams, and Adobe Connect online. The studies found that delivery of remote clinical assessments is possible and provides an alternative method of assessing many clinical skills, but most also acknowledge limitations and challenges. They are acceptable to both candidates and examiners, and where measured, show moderate agreement with on-site clinical assessments. Current evidence is based on studies with low methodological quality and for the most part, small sample sizes.
DOI: 10.6084/m9.figshare.17425991
title: An orifice shape-based reduced order model of patient-specific mitral valve regurgitation

description: Mitral valve regurgitation (MR) is one of the most prevalent valvular heart diseases. Its quantitative assessment is challenging but crucial for treatment decisions. Using computational fluid dynamics (CFD), we developed a reduced order model (ROM) describing the relationship between MR flow rates, transvalvular pressure differences, and the size and shape of the regurgitant valve orifice. Due to its low computational cost, this ROM could easily be implemented into clinical workflows to support the assessment of MR. We reconstructed mitral valves of 43 patients from 3D transesophageal echocardiographic images and estimated the 3D anatomic regurgitant orifice areas using a shrink-wrap algorithm. The orifice shapes were quantified with three dimensionless shape parameters. Steady-state CFD simulations in the reconstructed mitral valves were performed to analyse the relationship between the regurgitant orifice geometry and the regurgitant hemodynamics. Based on the results, three ROMs with increasing complexity were defined, all of which revealed very good agreement with CFD results with a mean bias below 3% for the MR flow rate. Classifying orifices into two shape groups and assigning group-specific flow coefficients in the ROM reduced the limit of agreement predicting regurgitant volumes from 9.0 ml to 5.7 ml at a mean regurgitant volume of 57 ml.
DOI: 10.6084/m9.figshare.17425991.v1
title: An orifice shape-based reduced order model of patient-specific mitral valve regurgitation

description: Mitral valve regurgitation (MR) is one of the most prevalent valvular heart diseases. Its quantitative assessment is challenging but crucial for treatment decisions. Using computational fluid dynamics (CFD), we developed a reduced order model (ROM) describing the relationship between MR flow rates, transvalvular pressure differences, and the size and shape of the regurgitant valve orifice. Due to its low computational cost, this ROM could easily be implemented into clinical workflows to support the assessment of MR. We reconstructed mitral valves of 43 patients from 3D transesophageal echocardiographic images and estimated the 3D anatomic regurgitant orifice areas using a shrink-wrap algorithm. The orifice shapes were quantified with three dimensionless shape parameters. Steady-state CFD simulations in the reconstructed mitral valves were performed to analyse the relationship between the regurgitant orifice geometry and the regurgitant hemodynamics. Based on the results, three ROMs with increasing complexity were defined, all of which revealed very good agreement with CFD results with a mean bias below 3% for the MR flow rate. Classifying orifices into two shape groups and assigning group-specific flow coefficients in the ROM reduced the limit of agreement predicting regurgitant volumes from 9.0 ml to 5.7 ml at a mean regurgitant volume of 57 ml.
DOI: 10.6084/m9.figshare.17089315
title: Numerical optimization of three-cavity magneto mercury reciprocating (MMR) micropump

description: The operation of the three-cavity magneto mercury reciprocating (MMR) micropump, whose prototype were presented in an earlier companion paper, was numerically explored. In the three-cavity MMR micropump, three mercury slugs are moved by a periodic Lorentz force with a phase difference in three separate cavities. A consecutive motion of the slugs in their cavities transfer air from the inlet to the outlet. Two-dimensional OpenFOAM simulations were carried out to explore the influence of electric current excitation phase difference and back-pressure. The numerical simulations predicted the MMR micropump (with no valve) with a phase difference of 90∘ and 120∘ produces a mean pumping flow rate of 2.7 and 6.1 mL/min at a back-pressure of 10 Pa and maintains a maximum back-pressure of 17.8 and 20 Pa, respectively. However, it was found that there was a reverse flow at large back-pressures with an excitation phase difference of 90∘. The numerical results showed that employing a diffuser/nozzle valve with a length of 5 mm and an angle of 10∘ improves the mean flow rate of the micropump with a phase difference of 90∘ at a back-pressure of 10 Pa by 140% from 2.7 to 6.5 mL/min, and its maximum back-pressure by 125% from 17.8 to 40 Pa.
DOI: 10.6084/m9.figshare.16955777
title: Community Detection in General Hypergraph Via Graph Embedding

description: Conventional network data have largely focused on pairwise interactions between two entities, yet multi-way interactions among multiple entities have been frequently observed in real-life hypergraph networks. In this article, we propose a novel method for detecting community structure in general hypergraph networks, uniform or non-uniform. The proposed method introduces a null vertex to augment a nonuniform hypergraph into a uniform multi-hypergraph, and then embeds the multi-hypergraph in a low-dimensional vector space such that vertices within the same community are close to each other. The resultant optimization task can be efficiently tackled by an alternative updating scheme. The asymptotic consistencies of the proposed method are established in terms of both community detection and hypergraph estimation, which are also supported by numerical experiments on some synthetic and real-life hypergraph networks. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.16955777.v2
title: Community Detection in General Hypergraph Via Graph Embedding

description: Conventional network data have largely focused on pairwise interactions between two entities, yet multi-way interactions among multiple entities have been frequently observed in real-life hypergraph networks. In this article, we propose a novel method for detecting community structure in general hypergraph networks, uniform or non-uniform. The proposed method introduces a null vertex to augment a nonuniform hypergraph into a uniform multi-hypergraph, and then embeds the multi-hypergraph in a low-dimensional vector space such that vertices within the same community are close to each other. The resultant optimization task can be efficiently tackled by an alternative updating scheme. The asymptotic consistencies of the proposed method are established in terms of both community detection and hypergraph estimation, which are also supported by numerical experiments on some synthetic and real-life hypergraph networks. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.16955777.v1
title: Community Detection in General Hypergraph via Graph Embedding

description: Conventional network data has largely focused on pairwise interactions between two entities, yet multi-way interactions among multiple entities have been frequently observed in real-life hypergraph networks. In this article, we propose a novel method for detecting community structure in general hypergraph networks, uniform or non-uniform. The proposed method introduces a null vertex to augment a non-uniform hypergraph into a uniform multi-hypergraph, and then embeds the multi-hypergraph in a low-dimensional vector space such that vertices within the same community are close to each other. The resultant optimization task can be efficiently tackled by an alternative updating scheme. The asymptotic consistencies of the proposed method are established in terms of both community detection and hypergraph estimation, which are also supported by numerical experiments on some synthetic and real-life hypergraph networks.
DOI: 10.6084/m9.figshare.13237006.v2
title: Shrinkage Estimation for Dose–Response Modeling in Phase II Trials With Multiple Schedules

description: Recently, phase II trials with multiple schedules (frequency of administrations) have become more popular, for instance, in the development of treatments for atopic dermatitis. If the relationship of the dose and response is described by a parametric model, a simplistic approach is to scale doses from different schedules to a common unit and pool all rescaled doses. However, this approach ignores the potential heterogeneity in dose–response curves between schedules. A more reasonable approach is the partial pooling, that is, certain parameters of the dose–response curves are shared, while others are allowed to vary. Rather than using schedule-specific fixed-effects, we propose a Bayesian hierarchical model with random-effects to model the between-schedule heterogeneity with regard to certain parameters. Schedule-specific dose–response relationships can then be estimated using shrinkage estimation. Considering Emax models, the proposed method displayed desirable performance in terms of the mean absolute error and the coverage probabilities for the dose–response curve compared to the complete pooling. Furthermore, it outperformed the partial pooling with schedule-specific fixed-effects by producing lower mean absolute error and shorter credible intervals. The methods are illustrated using simulations and a phase II trial example in atopic dermatitis. A publicly available R package, ModStan, is developed to automate the implementation of the proposed method (https://github.com/gunhanb/ModStan).
DOI: 10.6084/m9.figshare.20424216
title: Supplementary tables

description: <strong>Table S1. Primers and oligonTcleotides Tsed in this work.</strong> <strong>Table S2. </strong> <strong>Antibody information in this work.</strong> <strong>Table S3. Expression of MAD2L1 in Pan-cancer analyzed by TCGA and GTEx database.</strong>
DOI: 10.6084/m9.figshare.20424216.v1
title: Supplementary tables

description: <strong>Table S1. Primers and oligonTcleotides Tsed in this work.</strong> <strong>Table S2. </strong> <strong>Antibody information in this work.</strong> <strong>Table S3. Expression of MAD2L1 in Pan-cancer analyzed by TCGA and GTEx database.</strong>
DOI: 10.6084/m9.figshare.13237006
title: Shrinkage Estimation for Dose–Response Modeling in Phase II Trials With Multiple Schedules

description: Recently, phase II trials with multiple schedules (frequency of administrations) have become more popular, for instance, in the development of treatments for atopic dermatitis. If the relationship of the dose and response is described by a parametric model, a simplistic approach is to scale doses from different schedules to a common unit and pool all rescaled doses. However, this approach ignores the potential heterogeneity in dose–response curves between schedules. A more reasonable approach is the partial pooling, that is, certain parameters of the dose–response curves are shared, while others are allowed to vary. Rather than using schedule-specific fixed-effects, we propose a Bayesian hierarchical model with random-effects to model the between-schedule heterogeneity with regard to certain parameters. Schedule-specific dose–response relationships can then be estimated using shrinkage estimation. Considering Emax models, the proposed method displayed desirable performance in terms of the mean absolute error and the coverage probabilities for the dose–response curve compared to the complete pooling. Furthermore, it outperformed the partial pooling with schedule-specific fixed-effects by producing lower mean absolute error and shorter credible intervals. The methods are illustrated using simulations and a phase II trial example in atopic dermatitis. A publicly available R package, ModStan, is developed to automate the implementation of the proposed method (https://github.com/gunhanb/ModStan).
DOI: 10.6084/m9.figshare.12981707.v1
title: Effect of a natural supplement containing glucosinolates, phytosterols and citrus flavonoids on body weight and metabolic parameters in a menopausal murine model induced by bilateral ovariectomy

description: To evaluate the effect of a herbal preparation containing glucosinolates, phytosterols and citrus flavonoids (supplement) on body weight and metabolic parameters usually impaired by menopause. A pre-clinical experimental study carried out in twenty-five Swiss strain mice (<i>Mus musculus</i>) randomly distributed (1:1:1:1:1 ratio) to five groups to receive for ten weeks: (1) oral gelatinized maca extract 0.5625 mg/kg/day + bilateral ovariectomy (Maca + OVX); (2) oral supplement 0.5625 mg/kg/day + bilateral ovariectomy (S1 + OVX); (3) oral supplement 1.6875 mg/kg/day + bilateral ovariectomy (S2 + OVX); (4) oral saline 100 µl/kg/day + bilateral ovariectomy (OVX); and (5) oral saline 100 µl/kg/day + sham surgery (sham). The primary endpoint was change in body weight gain from baseline to final. Secondary endpoints were uterine weight and cholesterol, triglyceride, glucose, and glucose/triglycerides index values at the end of the study. A modified intention-to-treat analysis was performed through linear regression models and using the Bonferroni method to penalized <i>p</i>-values by multiple comparisons. Twenty-three animals completed the study. There was a significant average difference in weight gain, with a greater reduction in the S2 + OVX group compared to the OVX group (difference= −3.5; 95% CI (–5.27; −1.74); <i>p</i> &lt; .001). S2 + OVX group also displayed a significant average reduction of total blood cholesterol (difference: −16.94; 95% CI (–33.73; −0.15); <i>p</i> = .037). No significant effects of the supplement were found on other secondary endpoints. In this murine menopausal model, triple oral supplement dose resulted in an average reduction of weight gain and total cholesterol levels, suggesting that the compound could have a potential effect at regulating menopausal altered metabolism.