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Moran JL, Duke GJ, Santamaria JD, Linden A. Modelling of intensive care unit (ICU) length of stay as a quality measure: a problematic exercise. BMC Med Res Methodol 2023; 23:207. [PMID: 37710162 PMCID: PMC10500937 DOI: 10.1186/s12874-023-02028-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Accepted: 09/01/2023] [Indexed: 09/16/2023] Open
Abstract
BACKGROUND Intensive care unit (ICU) length of stay (LOS) and the risk adjusted equivalent (RALOS) have been used as quality metrics. The latter measures entail either ratio or difference formulations or ICU random effects (RE), which have not been previously compared. METHODS From calendar year 2016 data of an adult ICU registry-database (Australia & New Zealand Intensive Care Society (ANZICS) CORE), LOS predictive models were established using linear (LMM) and generalised linear (GLMM) mixed models. Model fixed effects quality-metric formulations were estimated as RALOSR for LMM (geometric mean derived from log(ICU LOS)) and GLMM (day) and observed minus expected ICU LOS (OMELOS from GLMM). Metric confidence intervals (95%CI) were estimated by bootstrapping; random effects (RE) were predicted for LMM and GLMM. Forest-plot displays of ranked quality-metric point-estimates (95%CI) were generated for ICU hospital classifications (metropolitan, private, rural/regional, and tertiary). Robust rank confidence sets (point estimate and 95%CI), both marginal (pertaining to a singular ICU) and simultaneous (pertaining to all ICU differences), were established. RESULTS The ICU cohort was of 94,361 patients from 125 ICUs (metropolitan 16.9%, private 32.8%, rural/regional 6.4%, tertiary 43.8%). Age (mean, SD) was 61.7 (17.5) years; 58.3% were male; APACHE III severity-of-illness score 54.6 (25.7); ICU annual patient volume 1192 (702) and ICU LOS 3.2 (4.9). There was no concordance of ICU ranked model predictions, GLMM versus LMM, nor for the quality metrics used, RALOSR, OMELOS and site-specific RE for each of the ICU hospital classifications. Furthermore, there was no concordance between ICU ranking confidence sets, marginal and simultaneous for models or quality metrics. CONCLUSIONS Inference regarding adjusted ICU LOS was dependent upon the statistical estimator and the quality index used to quantify any LOS differences across ICUs. That is, there was no "one best model"; thus, ICU "performance" is determined by model choice and any rankings thereupon should be circumspect.
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Affiliation(s)
- John L Moran
- Department of Intensive Care Medicine, The Queen Elizabeth Hospital, Woodville, Australia.
| | - Graeme J Duke
- Department of Intensive Care, Eastern Health, Box Hill, Australia
| | - John D Santamaria
- Department of Critical Care Medicine, St Vincent's Hospital (Melbourne), Fitzroy, Australia
| | - Ariel Linden
- Linden Consulting Group, LLC, San Francisco, CA, USA
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Nebe S, Reutter M, Baker DH, Bölte J, Domes G, Gamer M, Gärtner A, Gießing C, Gurr C, Hilger K, Jawinski P, Kulke L, Lischke A, Markett S, Meier M, Merz CJ, Popov T, Puhlmann LMC, Quintana DS, Schäfer T, Schubert AL, Sperl MFJ, Vehlen A, Lonsdorf TB, Feld GB. Enhancing precision in human neuroscience. eLife 2023; 12:e85980. [PMID: 37555830 PMCID: PMC10411974 DOI: 10.7554/elife.85980] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 07/23/2023] [Indexed: 08/10/2023] Open
Abstract
Human neuroscience has always been pushing the boundary of what is measurable. During the last decade, concerns about statistical power and replicability - in science in general, but also specifically in human neuroscience - have fueled an extensive debate. One important insight from this discourse is the need for larger samples, which naturally increases statistical power. An alternative is to increase the precision of measurements, which is the focus of this review. This option is often overlooked, even though statistical power benefits from increasing precision as much as from increasing sample size. Nonetheless, precision has always been at the heart of good scientific practice in human neuroscience, with researchers relying on lab traditions or rules of thumb to ensure sufficient precision for their studies. In this review, we encourage a more systematic approach to precision. We start by introducing measurement precision and its importance for well-powered studies in human neuroscience. Then, determinants for precision in a range of neuroscientific methods (MRI, M/EEG, EDA, Eye-Tracking, and Endocrinology) are elaborated. We end by discussing how a more systematic evaluation of precision and the application of respective insights can lead to an increase in reproducibility in human neuroscience.
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Affiliation(s)
- Stephan Nebe
- Zurich Center for Neuroeconomics, Department of Economics, University of ZurichZurichSwitzerland
| | - Mario Reutter
- Department of Psychology, Julius-Maximilians-UniversityWürzburgGermany
| | - Daniel H Baker
- Department of Psychology and York Biomedical Research Institute, University of YorkYorkUnited Kingdom
| | - Jens Bölte
- Institute for Psychology, University of Münster, Otto-Creuzfeldt Center for Cognitive and Behavioral NeuroscienceMünsterGermany
| | - Gregor Domes
- Department of Biological and Clinical Psychology, University of TrierTrierGermany
- Institute for Cognitive and Affective NeuroscienceTrierGermany
| | - Matthias Gamer
- Department of Psychology, Julius-Maximilians-UniversityWürzburgGermany
| | - Anne Gärtner
- Faculty of Psychology, Technische Universität DresdenDresdenGermany
| | - Carsten Gießing
- Biological Psychology, Department of Psychology, School of Medicine and Health Sciences, Carl von Ossietzky University of OldenburgOldenburgGermany
| | - Caroline Gurr
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital, Goethe UniversityFrankfurtGermany
- Brain Imaging Center, Goethe UniversityFrankfurtGermany
| | - Kirsten Hilger
- Department of Psychology, Julius-Maximilians-UniversityWürzburgGermany
- Department of Psychology, Psychological Diagnostics and Intervention, Catholic University of Eichstätt-IngolstadtEichstättGermany
| | - Philippe Jawinski
- Department of Psychology, Humboldt-Universität zu BerlinBerlinGermany
| | - Louisa Kulke
- Department of Developmental with Educational Psychology, University of BremenBremenGermany
| | - Alexander Lischke
- Department of Psychology, Medical School HamburgHamburgGermany
- Institute of Clinical Psychology and Psychotherapy, Medical School HamburgHamburgGermany
| | - Sebastian Markett
- Department of Psychology, Humboldt-Universität zu BerlinBerlinGermany
| | - Maria Meier
- Department of Psychology, University of KonstanzKonstanzGermany
- University Psychiatric Hospitals, Child and Adolescent Psychiatric Research Department (UPKKJ), University of BaselBaselSwitzerland
| | - Christian J Merz
- Department of Cognitive Psychology, Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr University BochumBochumGermany
| | - Tzvetan Popov
- Department of Psychology, Methods of Plasticity Research, University of ZurichZurichSwitzerland
| | - Lara MC Puhlmann
- Leibniz Institute for Resilience ResearchMainzGermany
- Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | - Daniel S Quintana
- Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- NevSom, Department of Rare Disorders & Disabilities, Oslo University HospitalOsloNorway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of OsloOsloNorway
- Norwegian Centre for Mental Disorders Research (NORMENT), University of OsloOsloNorway
| | - Tim Schäfer
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital, Goethe UniversityFrankfurtGermany
- Brain Imaging Center, Goethe UniversityFrankfurtGermany
| | | | - Matthias FJ Sperl
- Department of Clinical Psychology and Psychotherapy, University of GiessenGiessenGermany
- Center for Mind, Brain and Behavior, Universities of Marburg and GiessenGiessenGermany
| | - Antonia Vehlen
- Department of Biological and Clinical Psychology, University of TrierTrierGermany
| | - Tina B Lonsdorf
- Department of Systems Neuroscience, University Medical Center Hamburg-EppendorfHamburgGermany
- Department of Psychology, Biological Psychology and Cognitive Neuroscience, University of BielefeldBielefeldGermany
| | - Gordon B Feld
- Department of Clinical Psychology, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg UniversityMannheimGermany
- Department of Psychology, Heidelberg UniversityHeidelbergGermany
- Department of Addiction Behavior and Addiction Medicine, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg UniversityMannheimGermany
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg UniversityMannheimGermany
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Evangelista AL, Alonso AC, Ritti-Dias R, Barros BM, de Souza CR, Braz TV, Bocalini DS, Greve JMD. Commentary: Effects of Whole Body Electrostimulation Associated With Body Weight Training on Functional Capacity and Body Composition in Inactive Older People. Front Physiol 2021; 12:735818. [PMID: 34539453 PMCID: PMC8446544 DOI: 10.3389/fphys.2021.735818] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2021] [Accepted: 08/09/2021] [Indexed: 11/24/2022] Open
Affiliation(s)
- Alexandre Lopes Evangelista
- Laboratório de Fisiologia e Bioquímica Experimental, Centro de Educação Física e Esporte, Universidade Federal do Espirito Santo, Vitoria, Brazil
| | - Angelica Castilho Alonso
- Programa de Mestrado Ciências do Envelhecimento, Universidade São Judas Tadeu, São Paulo, Brazil
| | - Raphael Ritti-Dias
- Programa de Pós-Graduação em Ciências da Reabilitação, Universidade Nove de Julho, São Paulo, Brazil
| | - Bruna Massaroto Barros
- Programa de Pós-Graduação em Ciências da Reabilitação, Universidade Nove de Julho, São Paulo, Brazil
| | - Cleison Rodrigues de Souza
- Laboratório de Fisiologia e Bioquímica Experimental, Centro de Educação Física e Esporte, Universidade Federal do Espirito Santo, Vitoria, Brazil
| | - Tiago Volpi Braz
- Laboratório de Avaliação do Movimento Humano, Universidade Metodista de Piracicaba, Piracicaba, Brazil
| | - Danilo Sales Bocalini
- Laboratório de Fisiologia e Bioquímica Experimental, Centro de Educação Física e Esporte, Universidade Federal do Espirito Santo, Vitoria, Brazil
| | - Julia Maria D'andréa Greve
- Departamento de Ortopedia e Traumatologia, Universidade de São Paulo Faculdade de Medicina, São Paulo, Brazil
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Li R, Guan J, Zhou S. Single-cell RNA-seq data clustering: A survey with performance comparison study. J Bioinform Comput Biol 2020; 18:2040005. [DOI: 10.1142/s0219720020400053] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Clustering analysis has been widely applied to single-cell RNA-sequencing (scRNA-seq) data to discover cell types and cell states. Algorithms developed in recent years have greatly helped the understanding of cellular heterogeneity and the underlying mechanisms of biological processes. However, these algorithms often use different techniques, were evaluated on different datasets and compared with some of their counterparts usually using different performance metrics. Consequently, there lacks an accurate and complete picture of their merits and demerits, which makes it difficult for users to select proper algorithms for analyzing their data. To fill this gap, we first do a review on the major existing scRNA-seq data clustering methods, and then conduct a comprehensive performance comparison among them from multiple perspectives. We consider 13 state of the art scRNA-seq data clustering algorithms, and collect 12 publicly available real scRNA-seq datasets from the existing works to evaluate and compare these algorithms. Our comparative study shows that the existing methods are very diverse in performance. Even the top-performance algorithms do not perform well on all datasets, especially those with complex structures. This suggests that further research is required to explore more stable, accurate, and efficient clustering algorithms for scRNA-seq data.
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Affiliation(s)
- Ruiyi Li
- Department of Computer Science and Technology, Tongji University, 4800 Caoan Road, Shanghai, P. R. China
| | - Jihong Guan
- Department of Computer Science and Technology, Tongji University, 4800 Caoan Road, Shanghai, P. R. China
| | - Shuigeng Zhou
- Shanghai Key Lab of Intelligent Information Processing and School of Computer Science, Fudan University, 220 Handan Road, Shanghai, P. R. China
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Curran-Everett D. Explorations in statistics: the log transformation. ADVANCES IN PHYSIOLOGY EDUCATION 2018; 42:343-347. [PMID: 29761718 DOI: 10.1152/advan.00018.2018] [Citation(s) in RCA: 48] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This thirteenth installment of Explorations in Statistics explores the log transformation, an established technique that rescales the actual observations from an experiment so that the assumptions of some statistical analysis are better met. A general assumption in statistics is that the variability of some response Y is homogeneous across groups or across some predictor variable X. If the variability-the standard deviation-varies in rough proportion to the mean value of Y, a log transformation can equalize the standard deviations. Moreover, if the actual observations from an experiment conform to a skewed distribution, then a log transformation can make the theoretical distribution of the sample mean more consistent with a normal distribution. This is important: the results of a one-sample t test are meaningful only if the theoretical distribution of the sample mean is roughly normal. If we log-transform our observations, then we want to confirm the transformation was useful. We can do this if we use the Box-Cox method, if we bootstrap the sample mean and the statistic t itself, and if we assess the residual plots from the statistical model of the actual and transformed sample observations.
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Affiliation(s)
- Douglas Curran-Everett
- Division of Biostatistics and Bioinformatics, National Jewish Health , Denver, Colorado
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Denver , Denver, Colorado
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Francq BG, Cartiaux O. Delta method and bootstrap in linear mixed models to estimate a proportion when no event is observed: application to intralesional resection in bone tumor surgery. Stat Med 2016; 35:3563-82. [PMID: 26990871 DOI: 10.1002/sim.6939] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2014] [Revised: 02/17/2016] [Accepted: 02/22/2016] [Indexed: 11/08/2022]
Abstract
Resecting bone tumors requires good cutting accuracy to reduce the occurrence of local recurrence. This issue is considerably reduced with a navigated technology. The estimation of extreme proportions is challenging especially with small or moderate sample sizes. When no success is observed, the commonly used binomial proportion confidence interval is not suitable while the rule of three provides a simple solution. Unfortunately, these approaches are unable to differentiate between different unobserved events. Different delta methods and bootstrap procedures are compared in univariate and linear mixed models with simulations and real data by assuming the normality. The delta method on the z-score and parametric bootstrap provide similar results but the delta method requires the estimation of the covariance matrix of the estimates. In mixed models, the observed Fisher information matrix with unbounded variance components should be preferred. The parametric bootstrap, easier to apply, outperforms the delta method for larger sample sizes but it may be time costly. Copyright © 2016 John Wiley & Sons, Ltd.
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Affiliation(s)
- Bernard G Francq
- Institut de Statistique, Biostatistique et sciences Actuarielles, Université Catholique de Louvain, Voie du Roman Pays 20, 1348, Louvain-la-Neuve, Belgium.,Robertson Centre for Biostatistics, University of Glasgow, Glasgow, U.K
| | - Olivier Cartiaux
- Institut de Recherche Expérimentale et Clinique Computer Assisted and Robotic Surgery, Université Catholique de Louvain, Avenue Mounier 53, Bruxelles, 1200, Belgium
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Wormser D, White IR, Thompson SG, Wood AM. Within-person variability in calculated risk factors: comparing the aetiological association of adiposity ratios with risk of coronary heart disease. Int J Epidemiol 2013; 42:849-59. [PMID: 23918853 PMCID: PMC3733701 DOI: 10.1093/ije/dyt077] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/08/2013] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Within-person variability in measured values of a risk factor can bias its association with disease. We investigated the extent of regression dilution bias in calculated variables and its implications for comparing the aetiological associations of risk factors. METHODS Using a numerical illustration and repeats from 42,300 individuals (12 cohorts), we estimated regression dilution ratios (RDRs) in calculated risk factors [body-mass index (BMI), waist-to-hip ratio (WHR), and waist-to-height ratio (WHtR)] and in their components (height, weight, waist circumference, and hip circumference), assuming the long-term average exposure to be of interest. Error-corrected hazard ratios (HRs) for risk of coronary heart disease (CHD) were compared across adiposity measures per standard-deviation (SD) change in: (i) baseline and (ii) error-corrected levels. RESULTS RDRs in calculated risk factors depend strongly on the RDRs, correlation, and comparative distributions of the components of these risk factors. For measures of adiposity, the RDR was lower for WHR [RDR: 0.72 (95% confidence interval 0.65-0.80)] than for either of its components [waist circumference: 0.87 (0.85-0.90); hip circumference: 0.90 (0.86-0.93) or for BMI: 0.96 (0.93-0.98) and WHtR: 0.87 (0.85-0.90)], predominantly because of the stronger correlation and more similar distributions observed between waist circumference and hip circumference than between height and weight or between waist circumference and height. Error-corrected HRs for BMI, waist circumference, WHR, and WHtR, were respectively 1.24, 1.30, 1.44, and 1.32 per SD change in baseline levels of these variables, and 1.24, 1.27, 1.35, and 1.30 per SD change in error-corrected levels. CONCLUSIONS The extent of within-person variability relative to between-person variability in calculated risk factors can be considerably larger (or smaller) than in its components. Aetiological associations of risk factors should be compared through the use of error-corrected HRs per SD change in error-corrected levels of these risk factors.
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Affiliation(s)
- David Wormser
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
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