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Sharma R, Tsiamyrtzis P, Webb AG, Leiss EL, Tsekos NV. Learning to deep learning: statistics and a paradigm test in selecting a UNet architecture to enhance MRI. MAGMA 2023:10.1007/s10334-023-01127-6. [PMID: 37989921 DOI: 10.1007/s10334-023-01127-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 09/30/2023] [Accepted: 10/16/2023] [Indexed: 11/23/2023]
Abstract
OBJECTIVE This study aims to assess the statistical significance of training parameters in 240 dense UNets (DUNets) used for enhancing low Signal-to-Noise Ratio (SNR) and undersampled MRI in various acquisition protocols. The objective is to determine the validity of differences between different DUNet configurations and their impact on image quality metrics. MATERIALS AND METHODS To achieve this, we trained all DUNets using the same learning rate and number of epochs, with variations in 5 acquisition protocols, 24 loss function weightings, and 2 ground truths. We calculated evaluation metrics for two metric regions of interest (ROI). We employed both Analysis of Variance (ANOVA) and Mixed Effects Model (MEM) to assess the statistical significance of the independent parameters, aiming to compare their efficacy in revealing differences and interactions among fixed parameters. RESULTS ANOVA analysis showed that, except for the acquisition protocol, fixed variables were statistically insignificant. In contrast, MEM analysis revealed that all fixed parameters and their interactions held statistical significance. This emphasizes the need for advanced statistical analysis in comparative studies, where MEM can uncover finer distinctions often overlooked by ANOVA. DISCUSSION These findings highlight the importance of utilizing appropriate statistical analysis when comparing different deep learning models. Additionally, the surprising effectiveness of the UNet architecture in enhancing various acquisition protocols underscores the potential for developing improved methods for characterizing and training deep learning models. This study serves as a stepping stone toward enhancing the transparency and comparability of deep learning techniques for medical imaging applications.
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Affiliation(s)
- Rishabh Sharma
- Medical Robotics and Imaging Lab, Department of Computer Science, 501, Philip G. Hoffman Hall, University of Houston, 4800 Calhoun Road, Houston, TX, 77204, USA
| | - Panagiotis Tsiamyrtzis
- Department of Mechanical Engineering, Politecnico Di Milano, Milan, Italy
- Department of Statistics, Athens University of Economics and Business, Athens, Greece
| | - Andrew G Webb
- C.J. Gorter Center for High Field MRI, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Ernst L Leiss
- Department of Computer Science, University of Houston, Houston, TX, USA
| | - Nikolaos V Tsekos
- Medical Robotics and Imaging Lab, Department of Computer Science, 501, Philip G. Hoffman Hall, University of Houston, 4800 Calhoun Road, Houston, TX, 77204, USA.
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Brown CH, Hedeker D, Gibbons RD, Duan N, Almirall D, Gallo C, Burnett-Zeigler I, Prado G, Young SD, Valido A, Wyman PA. Accounting for Context in Randomized Trials after Assignment. Prev Sci 2022; 23:1321-1332. [PMID: 36083435 PMCID: PMC9461380 DOI: 10.1007/s11121-022-01426-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/05/2022] [Indexed: 10/25/2022]
Abstract
Many preventive trials randomize individuals to intervention condition which is then delivered in a group setting. Other trials randomize higher levels, say organizations, and then use learning collaboratives comprised of multiple organizations to support improved implementation or sustainment. Other trials randomize or expand existing social networks and use key opinion leaders to deliver interventions through these networks. We use the term contextually driven to refer generally to such trials (traditionally referred to as clustering, where groups are formed either pre-randomization or post-randomization - i.e., a cluster-randomized trial), as these groupings or networks provide fixed or time-varying contexts that matter both theoretically and practically in the delivery of interventions. While such contextually driven trials can provide efficient and effective ways to deliver and evaluate prevention programs, they all require analytical procedures that take appropriate account of non-independence, something not always appreciated. Published analyses of many prevention trials have failed to take this into account. We discuss different types of contextually driven designs and then show that even small amounts of non-independence can inflate actual Type I error rates. This inflation leads to rejecting the null hypotheses too often, and erroneously leading us to conclude that there are significant differences between interventions when they do not exist. We describe a procedure to account for non-independence in the important case of a two-arm trial that randomizes units of individuals or organizations in both arms and then provides the active treatment in one arm through groups formed after assignment. We provide sample code in multiple programming languages to guide the analyst, distinguish diverse contextually driven designs, and summarize implications for multiple audiences.
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Affiliation(s)
- C Hendricks Brown
- Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
| | - Donald Hedeker
- Center for Health Statistics, The University of Chicago, Chicago, IL, USA
| | - Robert D Gibbons
- Center for Health Statistics, The University of Chicago, Chicago, IL, USA
| | - Naihua Duan
- Department of Psychiatry, Columbia University Medical Center, New York, NY, USA
| | - Daniel Almirall
- Institute for Social Research and Department of Statistics, University of Michigan, Ann Arbor, MI, USA
| | - Carlos Gallo
- Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Inger Burnett-Zeigler
- Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | | | - Sean D Young
- Department of Emergency Medicine, School of Medicine, Department of Informatics, Bren School of Information and Computer Sciences, University of California, Irvine, CA, USA
| | - Alberto Valido
- School of Education, University of North Carolina at Chapel Hill, Chapel Hill, Orange, NC, USA
| | - Peter A Wyman
- Department of Psychiatry, University of Rochester School of Medicine, Rochester, NY, USA
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Leander J, Sunnåker M, Rekić D, Aksenov S, Eriksson UG, Johansson S, Parkinson J. A semi-mechanistic exposure-response model to assess the effects of verinurad, a potent URAT1 inhibitor, on serum and urine uric acid in patients with hyperuricemia-associated diseases. J Pharmacokinet Pharmacodyn 2021; 48:525-541. [PMID: 33728547 PMCID: PMC8225519 DOI: 10.1007/s10928-021-09747-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Accepted: 03/02/2021] [Indexed: 01/08/2023]
Abstract
Verinurad, a uric acid transporter 1 (URAT1) inhibitor, lowers serum uric acid by promoting its urinary excretion. Co-administration with a xanthine oxidase inhibitor (XOI) to simultaneously reduce uric acid production rate reduces the potential for renal tubular precipitation of uric acid, which can lead to acute kidney injury. The combination is currently in development for chronic kidney disease and heart failure. The aim of this work was to apply and extend a previously developed semi-mechanistic exposure–response model for uric acid kinetics to include between-subject variability to verinurad and its combinations with XOIs, and to provide predictions to support future treatment strategies. The model was developed using data from 12 clinical studies from a total of 434 individuals, including healthy volunteers, patients with hyperuricemia, and renally impaired subjects. The model described the data well, taking into account the impact of various patient characteristics such as renal function, baseline fractional excretion of uric acid, and race. The potencies (EC50s) of verinurad (reducing uric acid reuptake), febuxostat (reducing uric acid production), and oxypurinol (reducing uric acid production) were: 29, 128, and 13,030 ng/mL, respectively. For verinurad, symptomatic hyperuricemic (gout) subjects showed a higher EC50 compared with healthy volunteers (37 ng/mL versus 29 ng/mL); while no significant difference was found for asymptomatic hyperuricemic patients. Simulations based on the uric acid model were performed to assess dose–response of verinurad in combination with XOI, and to investigate the impact of covariates. The simulations demonstrated application of the model to support dose selection for verinurad.
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Affiliation(s)
- Jacob Leander
- Clinical Pharmacology and Quantitative Pharmacology, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Gothenburg, Sweden
| | - Mikael Sunnåker
- Clinical Pharmacology and Quantitative Pharmacology, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Gothenburg, Sweden
| | - Dinko Rekić
- Clinical Pharmacology and Quantitative Pharmacology, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Gothenburg, Sweden
| | - Sergey Aksenov
- Clinical Pharmacology and Quantitative Pharmacology, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Waltham, MA, USA
| | - Ulf G Eriksson
- Clinical Pharmacology and Quantitative Pharmacology, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Gothenburg, Sweden
| | - Susanne Johansson
- Clinical Pharmacology and Quantitative Pharmacology, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Gothenburg, Sweden
| | - Joanna Parkinson
- Clinical Pharmacology and Quantitative Pharmacology, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Gothenburg, Sweden.
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Mishra AK, Skubic M, Popescu M, Lane K, Rantz M, Despins LA, Abbott C, Keller J, Robinson EL, Miller S. Tracking personalized functional health in older adults using geriatric assessments. BMC Med Inform Decis Mak 2020; 20:270. [PMID: 33081769 PMCID: PMC7576843 DOI: 10.1186/s12911-020-01283-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Accepted: 10/07/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Higher levels of functional health in older adults leads to higher quality of life and improves the ability to age-in-place. Tracking functional health objectively could help clinicians to make decisions for interventions in case of health deterioration. Even though several geriatric assessments capture several aspects of functional health, there is limited research in longitudinally tracking personalized functional health of older adults using a combination of these assessments. METHODS We used geriatric assessment data collected from 150 older adults to develop and validate a functional health prediction model based on risks associated with falls, hospitalizations, emergency visits, and death. We used mixed effects logistic regression to construct the model. The geriatric assessments included were Activities of Daily Living (ADL), Instrumental Activities of Daily Living (IADL), Mini-Mental State Examination (MMSE), Geriatric Depression Scale (GDS), and Short Form 12 (SF12). Construct validators such as fall risks associated with model predictions, and case studies with functional health trajectories were used to validate the model. RESULTS The model is shown to separate samples with and without adverse health event outcomes with an area under the receiver operating characteristic curve (AUC) of > 0.85. The model could predict emergency visit or hospitalization with an AUC of 0.72 (95% CI 0.65-0.79), fall with an AUC of 0.86 (95% CI 0.83-0.89), fall with hospitalization with an AUC of 0.89 (95% CI 0.85-0.92), and mortality with an AUC of 0.93 (95% CI 0.88-0.97). Multiple comparisons of means using Turkey HSD test show that model prediction means for samples with no adverse health events versus samples with fall, hospitalization, and death were statistically significant (p < 0.001). Case studies for individual residents using predicted functional health trajectories show that changes in model predictions over time correspond to critical health changes in older adults. CONCLUSIONS The personalized functional health tracking may provide clinicians with a longitudinal view of overall functional health in older adults to help address the early detection of deterioration trends and decide appropriate interventions. It can also help older adults and family members take proactive steps to improve functional health.
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Affiliation(s)
- Anup K Mishra
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, 65211, USA.
| | - Marjorie Skubic
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, 65211, USA
| | - Mihail Popescu
- Department of Health Management and Informatics, University of Missouri, Columbia, MO, 65211, USA
| | - Kari Lane
- Sinclair School of Nursing, University of Missouri, Columbia, MO, 65211, USA
| | - Marilyn Rantz
- Sinclair School of Nursing, University of Missouri, Columbia, MO, 65211, USA
| | - Laurel A Despins
- Sinclair School of Nursing, University of Missouri, Columbia, MO, 65211, USA
| | - Carmen Abbott
- School of Health Professions, Physical Therapy, University of Missouri, Columbia, MO, 65211, USA
| | - James Keller
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, 65211, USA
| | - Erin L Robinson
- School of Social Work, University of Missouri, Columbia, MO, 65211, USA
| | - Steve Miller
- Sinclair School of Nursing, University of Missouri, Columbia, MO, 65211, USA
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Haem E, Harling K, Ayatollahi SMT, Zare N, Karlsson MO. Adjusted adaptive Lasso for covariate model-building in nonlinear mixed-effect pharmacokinetic models. J Pharmacokinet Pharmacodyn 2017; 44:55-66. [PMID: 28144841 DOI: 10.1007/s10928-017-9504-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2016] [Accepted: 01/17/2017] [Indexed: 10/20/2022]
Abstract
One important aim in population pharmacokinetics (PK) and pharmacodynamics is identification and quantification of the relationships between the parameters and covariates. Lasso has been suggested as a technique for simultaneous estimation and covariate selection. In linear regression, it has been shown that Lasso possesses no oracle properties, which means it asymptotically performs as though the true underlying model was given in advance. Adaptive Lasso (ALasso) with appropriate initial weights is claimed to possess oracle properties; however, it can lead to poor predictive performance when there is multicollinearity between covariates. This simulation study implemented a new version of ALasso, called adjusted ALasso (AALasso), to take into account the ratio of the standard error of the maximum likelihood (ML) estimator to the ML coefficient as the initial weight in ALasso to deal with multicollinearity in non-linear mixed-effect models. The performance of AALasso was compared with that of ALasso and Lasso. PK data was simulated in four set-ups from a one-compartment bolus input model. Covariates were created by sampling from a multivariate standard normal distribution with no, low (0.2), moderate (0.5) or high (0.7) correlation. The true covariates influenced only clearance at different magnitudes. AALasso, ALasso and Lasso were compared in terms of mean absolute prediction error and error of the estimated covariate coefficient. The results show that AALasso performed better in small data sets, even in those in which a high correlation existed between covariates. This makes AALasso a promising method for covariate selection in nonlinear mixed-effect models.
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Affiliation(s)
- Elham Haem
- Department of Biostatistics, Shiraz University of Medical Sciences School of Medicine, Shiraz, Iran.,Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | - Kajsa Harling
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | | | - Najaf Zare
- Department of Biostatistics, Infertility Research Center, Shiraz University of Medical Sciences, Shiraz, Iran.
| | - Mats O Karlsson
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
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