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Hu F, Chen AA, Horng H, Bashyam V, Davatzikos C, Alexander-Bloch A, Li M, Shou H, Satterthwaite TD, Yu M, Shinohara RT. Image harmonization: A review of statistical and deep learning methods for removing batch effects and evaluation metrics for effective harmonization. Neuroimage 2023; 274:120125. [PMID: 37084926 PMCID: PMC10257347 DOI: 10.1016/j.neuroimage.2023.120125] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 04/12/2023] [Accepted: 04/19/2023] [Indexed: 04/23/2023] Open
Abstract
Magnetic resonance imaging and computed tomography from multiple batches (e.g. sites, scanners, datasets, etc.) are increasingly used alongside complex downstream analyses to obtain new insights into the human brain. However, significant confounding due to batch-related technical variation, called batch effects, is present in this data; direct application of downstream analyses to the data may lead to biased results. Image harmonization methods seek to remove these batch effects and enable increased generalizability and reproducibility of downstream results. In this review, we describe and categorize current approaches in statistical and deep learning harmonization methods. We also describe current evaluation metrics used to assess harmonization methods and provide a standardized framework to evaluate newly-proposed methods for effective harmonization and preservation of biological information. Finally, we provide recommendations to end-users to advocate for more effective use of current methods and to methodologists to direct future efforts and accelerate development of the field.
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Affiliation(s)
- Fengling Hu
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104, United States.
| | - Andrew A Chen
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104, United States
| | - Hannah Horng
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104, United States
| | - Vishnu Bashyam
- Center for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine, United States
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine, United States
| | - Aaron Alexander-Bloch
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, United States; Penn-CHOP Lifespan Brain Institute, United States; Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, United States
| | - Mingyao Li
- Statistical Center for Single-Cell and Spatial Genomics, Perelman School of Medicine, University of Pennsylvania, United States
| | - Haochang Shou
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104, United States; Center for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine, United States
| | - Theodore D Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, United States; Penn-CHOP Lifespan Brain Institute, United States; The Penn Lifespan Informatics and Neuroimaging Center, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, United States
| | - Meichen Yu
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, United States
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104, United States; Center for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine, United States
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Choi YYC, Fineberg M, Kassavou A. Effectiveness of Remote Interventions to Improve Medication Adherence in Patients after Stroke: A Systematic Literature Review and Meta-Analysis. Behav Sci (Basel) 2023; 13:246. [PMID: 36975271 PMCID: PMC10044982 DOI: 10.3390/bs13030246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 03/04/2023] [Accepted: 03/07/2023] [Indexed: 03/15/2023] Open
Abstract
BACKGROUND Stroke affects more than 30 million people every year, but only two-thirds of patients comply with prescribed medication, leading to high stroke recurrence rates. Digital technologies can facilitate interventions to support treatment adherence. PURPOSE This study evaluates the effectiveness of remote interventions and their mechanisms of action in supporting medication adherence after stroke. METHODS PubMed, MEDLINE via Ovid, Cochrane CENTRAL, the Web of Science, SCOPUS, and PsycINFO were searched, and meta-analysis was performed using the Review Manager Tool. Intervention content analysis was conducted based on the COM-B model. RESULTS Ten eligible studies were included in the review and meta-analysis. The evidence suggested that patients who received remote interventions had significantly better medication adherence (SMD 0.49, 95% CI [0.04, 0.93], and p = 0.03) compared to those who received the usual care. The adherence ratio also indicated the interventions' effectiveness (odds ratio 1.30, 95% CI [0.55, 3.10], and p = 0.55). The systolic and diastolic blood pressure (MD -3.73 and 95% CI [-5.35, -2.10])/(MD -2.16 and 95% CI [-3.09, -1.22]) and cholesterol levels (MD -0.36 and 95% CI [-0.52, -0.20]) were significantly improved in the intervention group compared to the control. Further behavioural analysis demonstrated that enhancing the capability within the COM-B model had the largest impact in supporting improvements in adherence behaviour and relevant clinical outcomes. Patients' satisfaction and the interventions' usability were both high, suggesting the interventions' acceptability. CONCLUSION Telemedicine and mHealth interventions are effective in improving medication adherence and clinical indicators in stroke patients. Future studies could usefully investigate the effectiveness and cost-effectiveness of theory-based and remotely delivered interventions as an adjunct to stroke rehabilitation programmers.
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Affiliation(s)
- Yan Yee Cherizza Choi
- Department of Public Health and Primary Care, Clinical Medical School, University of Cambridge, Cambridge CB2 0SR, UK
| | - Micah Fineberg
- Department of Public Health and Primary Care, Clinical Medical School, University of Cambridge, Cambridge CB2 0SR, UK
| | - Aikaterini Kassavou
- Department of Public Health and Primary Care, Clinical Medical School, University of Cambridge, Cambridge CB2 0SR, UK
- UCL Queen Square Institute of Neurology, University College London, London NW3 2PF, UK
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Reitz KM, Althouse AD, Forman DE, Zuckerbraun BS, Vodovotz Y, Zamora R, Raffai RL, Hall DE, Tzeng E. MetfOrmin BenefIts Lower Extremities with Intermittent Claudication (MOBILE IC): randomized clinical trial protocol. BMC Cardiovasc Disord 2023; 23:38. [PMID: 36681798 PMCID: PMC9862509 DOI: 10.1186/s12872-023-03047-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Accepted: 01/05/2023] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND Peripheral artery disease (PAD) affects over 230 million people worldwide and is due to systemic atherosclerosis with etiology linked to chronic inflammation, hypertension, and smoking status. PAD is associated with walking impairment and mobility loss as well as a high prevalence of coronary and cerebrovascular disease. Intermittent claudication (IC) is the classic presenting symptom for PAD, although many patients are asymptomatic or have atypical presentations. Few effective medical therapies are available, while surgical and exercise therapies lack durability. Metformin, the most frequently prescribed oral medication for Type 2 diabetes, has salient anti-inflammatory and promitochondrial properties. We hypothesize that metformin will improve function, retard the progression of PAD, and improve systemic inflammation and mitochondrial function in non-diabetic patients with IC. METHODS 200 non-diabetic Veterans with IC will be randomized 1:1 to 180-day treatment with metformin extended release (1000 mg/day) or placebo to evaluate the effect of metformin on functional status, PAD progression, cardiovascular disease events, and systemic inflammation. The primary outcome is 180-day maximum walking distance on the 6-min walk test (6MWT). Secondary outcomes include additional assessments of functional status (cardiopulmonary exercise testing, grip strength, Walking Impairment Questionnaires), health related quality of life (SF-36, VascuQoL), macro- and micro-vascular assessment of lower extremity blood flow (ankle brachial indices, pulse volume recording, EndoPAT), cardiovascular events (amputations, interventions, major adverse cardiac events, all-cause mortality), and measures of systemic inflammation. All outcomes will be assessed at baseline, 90 and 180 days of study drug exposure, and 180 days following cessation of study drug. We will evaluate the primary outcome with linear mixed-effects model analysis with covariate adjustment for baseline 6MWT, age, baseline ankle brachial indices, and smoking status following an intention to treat protocol. DISCUSSION MOBILE IC is uniquely suited to evaluate the use of metformin to improve both systematic inflammatory responses, cellular energetics, and functional outcomes in patients with PAD and IC. TRIAL REGISTRATION The prospective MOBILE IC trial was publicly registered (NCT05132439) November 24, 2021.
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Affiliation(s)
- Katherine M Reitz
- Department of Surgery, University of Pittsburgh, South Tower, Rm 351.6, 200 Lothrop Street, Pittsburgh, PA, 15213, USA
- Division of Vascular Surgery, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Surgery, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA, USA
| | | | - Daniel E Forman
- Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Geriatrics Research, Education, and Clinical Care, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA, USA
| | - Brian S Zuckerbraun
- Department of Surgery, University of Pittsburgh, South Tower, Rm 351.6, 200 Lothrop Street, Pittsburgh, PA, 15213, USA
- Department of Surgery, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA, USA
- School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Yoram Vodovotz
- Department of Surgery, University of Pittsburgh, South Tower, Rm 351.6, 200 Lothrop Street, Pittsburgh, PA, 15213, USA
- Center for Inflammation and Regeneration Modeling, McGowan Institute for Regenerative Medicine, Pittsburgh, PA, USA
- Center for Systems Immunology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ruben Zamora
- Department of Surgery, University of Pittsburgh, South Tower, Rm 351.6, 200 Lothrop Street, Pittsburgh, PA, 15213, USA
| | | | - Daniel E Hall
- Department of Surgery, University of Pittsburgh, South Tower, Rm 351.6, 200 Lothrop Street, Pittsburgh, PA, 15213, USA
- Department of Surgery, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA, USA
- School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Geriatrics Research, Education, and Clinical Care, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA, USA
- Wolff Center, UPMC, Pittsburgh, PA, USA
- Center for Health Equity Research and Promotion, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA, USA
| | - Edith Tzeng
- Department of Surgery, University of Pittsburgh, South Tower, Rm 351.6, 200 Lothrop Street, Pittsburgh, PA, 15213, USA.
- Division of Vascular Surgery, University of Pittsburgh, Pittsburgh, PA, USA.
- Department of Surgery, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA, USA.
- School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
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Dwivedi AK. How to write statistical analysis section in medical research. J Investig Med 2022; 70:1759-1770. [PMID: 35710142 DOI: 10.1136/jim-2022-002479] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/01/2022] [Indexed: 12/15/2022]
Abstract
Reporting of statistical analysis is essential in any clinical and translational research study. However, medical research studies sometimes report statistical analysis that is either inappropriate or insufficient to attest to the accuracy and validity of findings and conclusions. Published works involving inaccurate statistical analyses and insufficient reporting influence the conduct of future scientific studies, including meta-analyses and medical decisions. Although the biostatistical practice has been improved over the years due to the involvement of statistical reviewers and collaborators in research studies, there remain areas of improvement for transparent reporting of the statistical analysis section in a study. Evidence-based biostatistics practice throughout the research is useful for generating reliable data and translating meaningful data to meaningful interpretation and decisions in medical research. Most existing research reporting guidelines do not provide guidance for reporting methods in the statistical analysis section that helps in evaluating the quality of findings and data interpretation. In this report, we highlight the global and critical steps to be reported in the statistical analysis of grants and research articles. We provide clarity and the importance of understanding study objective types, data generation process, effect size use, evidence-based biostatistical methods use, and development of statistical models through several thematic frameworks. We also provide published examples of adherence or non-adherence to methodological standards related to each step in the statistical analysis and their implications. We believe the suggestions provided in this report can have far-reaching implications for education and strengthening the quality of statistical reporting and biostatistical practice in medical research.
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Affiliation(s)
- Alok Kumar Dwivedi
- Department of Molecular and Translational Medicine, Division of Biostatistics and Epidemiology, Texas Tech University Health Sciences Center El Paso, El Paso, Texas, USA
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Cook JA, Julious SA, Sones W, Hampson LV, Hewitt C, Berlin JA, Ashby D, Emsley R, Fergusson DA, Walters SJ, Wilson EC, MacLennan G, Stallard N, Rothwell JC, Bland M, Brown L, Ramsay CR, Cook A, Armstrong D, Altman D, Vale LD. Practical help for specifying the target difference in sample size calculations for RCTs: the DELTA 2 five-stage study, including a workshop. Health Technol Assess 2019; 23:1-88. [PMID: 31661431 PMCID: PMC6843113 DOI: 10.3310/hta23600] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND The randomised controlled trial is widely considered to be the gold standard study for comparing the effectiveness of health interventions. Central to its design is a calculation of the number of participants needed (the sample size) for the trial. The sample size is typically calculated by specifying the magnitude of the difference in the primary outcome between the intervention effects for the population of interest. This difference is called the 'target difference' and should be appropriate for the principal estimand of interest and determined by the primary aim of the study. The target difference between treatments should be considered realistic and/or important by one or more key stakeholder groups. OBJECTIVE The objective of the report is to provide practical help on the choice of target difference used in the sample size calculation for a randomised controlled trial for researchers and funder representatives. METHODS The Difference ELicitation in TriAls2 (DELTA2) recommendations and advice were developed through a five-stage process, which included two literature reviews of existing funder guidance and recent methodological literature; a Delphi process to engage with a wider group of stakeholders; a 2-day workshop; and finalising the core document. RESULTS Advice is provided for definitive trials (Phase III/IV studies). Methods for choosing the target difference are reviewed. To aid those new to the topic, and to encourage better practice, 10 recommendations are made regarding choosing the target difference and undertaking a sample size calculation. Recommended reporting items for trial proposal, protocols and results papers under the conventional approach are also provided. Case studies reflecting different trial designs and covering different conditions are provided. Alternative trial designs and methods for choosing the sample size are also briefly considered. CONCLUSIONS Choosing an appropriate sample size is crucial if a study is to inform clinical practice. The number of patients recruited into the trial needs to be sufficient to answer the objectives; however, the number should not be higher than necessary to avoid unnecessary burden on patients and wasting precious resources. The choice of the target difference is a key part of this process under the conventional approach to sample size calculations. This document provides advice and recommendations to improve practice and reporting regarding this aspect of trial design. Future work could extend the work to address other less common approaches to the sample size calculations, particularly in terms of appropriate reporting items. FUNDING Funded by the Medical Research Council (MRC) UK and the National Institute for Health Research as part of the MRC-National Institute for Health Research Methodology Research programme.
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Affiliation(s)
- Jonathan A Cook
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Steven A Julious
- Medical Statistics Group, School of Health and Related Research, University of Sheffield, Sheffield, UK
| | - William Sones
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Lisa V Hampson
- Statistical Methodology and Consulting, Novartis Pharma AG, Basel, Switzerland
| | - Catherine Hewitt
- York Trials Unit, Department of Health Sciences, University of York, York, UK
| | | | - Deborah Ashby
- Imperial Clinical Trials Unit, Imperial College London, London, UK
| | - Richard Emsley
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Dean A Fergusson
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, ON, Canada
| | - Stephen J Walters
- Medical Statistics Group, School of Health and Related Research, University of Sheffield, Sheffield, UK
| | - Edward Cf Wilson
- Cambridge Centre for Health Services Research, Cambridge Clinical Trials Unit University of Cambridge, Cambridge, UK
- Health Economics Group, Norwich Medical School, University of East Anglia, Norwich, UK
| | - Graeme MacLennan
- Centre for Healthcare Randomised Trials, University of Aberdeen, Aberdeen, UK
| | - Nigel Stallard
- Warwick Medical School, Statistics and Epidemiology, University of Warwick, Coventry, UK
| | - Joanne C Rothwell
- Medical Statistics Group, School of Health and Related Research, University of Sheffield, Sheffield, UK
| | - Martin Bland
- Department of Health Sciences, University of York, York, UK
| | - Louise Brown
- MRC Clinical Trials Unit, Institute of Clinical Trials and Methodology, University College London, London, UK
| | - Craig R Ramsay
- Health Services Research Unit, University of Aberdeen, Aberdeen, UK
| | - Andrew Cook
- Wessex Institute, University of Southampton, Southampton, UK
| | - David Armstrong
- School of Population Health and Environmental Sciences, King's College London, London, UK
| | - Douglas Altman
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Luke D Vale
- Health Economics Group, Institute of Health & Society, Newcastle University, Newcastle upon Tyne, UK
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Spiegel RJ, Donnelly JP, Radecki RP. Over-EXTENDing the Window for Thrombolytic Therapy in Cerebrovascular Accident: September 2019 Annals of Emergency Medicine Journal Club. Ann Emerg Med 2019; 74:457-461. [PMID: 31445549 DOI: 10.1016/j.annemergmed.2019.07.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Rory J Spiegel
- Department of Emergency Medicine, Washington Hospital Center, Washington, DC
| | - John P Donnelly
- Department of Learning Health Sciences, Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI
| | - Ryan P Radecki
- Department of Emergency Medicine, Northwest Permanente, Portland, OR; The University of Texas Health Science Center at Houston, Houston, TX
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10-day vs 5-day decitabine: equivalence cannot be concluded. LANCET HAEMATOLOGY 2019; 6:e177. [DOI: 10.1016/s2352-3026(19)30024-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2019] [Accepted: 01/29/2019] [Indexed: 11/23/2022]
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Device Closure of Patent Foramen Ovale After Stroke: Pooled Analysis of Completed Randomized Trials. J Am Coll Cardiol 2016; 67:907-917. [PMID: 26916479 DOI: 10.1016/j.jacc.2015.12.023] [Citation(s) in RCA: 152] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/29/2015] [Revised: 11/23/2015] [Accepted: 12/01/2015] [Indexed: 12/21/2022]
Abstract
BACKGROUND The comparative effectiveness of percutaneous closure of patent foramen ovale (PFO) plus medical therapy versus medical therapy alone for cryptogenic stroke is uncertain. OBJECTIVES The authors performed the first pooled analysis of individual participant data from completed randomized trials comparing PFO closure versus medical therapy in patients with cryptogenic stroke. METHODS The analysis included data on 2 devices (STARFlex [umbrella occluder] [NMT Medical, Inc., Boston, Massachusetts] and Amplatzer PFO Occluder [disc occluder] [AGA Medical/St. Jude Medical, St. Paul, Minnesota]) evaluated in 3 trials. The primary composite outcome was stroke, transient ischemic attack, or death; the secondary outcome was stroke. We used log-rank tests and unadjusted and covariate-adjusted Cox regression models to compare device closure versus medical therapy. RESULTS Among 2,303 patients, closure was not significantly associated with the primary composite outcome. The difference became significant after covariate adjustment (hazard ratio [HR]: 0.68; p = 0.049). For the outcome of stroke, all comparisons were statistically significant, with unadjusted and adjusted HRs of 0.58 (p = 0.043) and 0.58 (p = 0.044), respectively. In analyses limited to the 2 disc occluder device trials, the effect of closure was not significant for the composite outcome, but was for the stroke outcome (unadjusted HR: 0.39; p = 0.013). Subgroup analyses did not identify significant heterogeneity of treatment effects. Atrial fibrillation was more common among closure patients. CONCLUSIONS Among patients with PFO and cryptogenic stroke, closure reduced recurrent stroke and had a statistically significant effect on the composite of stroke, transient ischemic attack, and death in adjusted but not unadjusted analyses.
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Su YS, Lee WC. False Appearance of Gene-Environment Interactions in Genetic Association Studies. Medicine (Baltimore) 2016; 95:e2743. [PMID: 26945360 PMCID: PMC4782844 DOI: 10.1097/md.0000000000002743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
Under the assumption of gene-environment independence, unknown/unmeasured environmental factors, irrespective of what they may be, cannot confound the genetic effects. This may lead many people to believe that genetic heterogeneity across different levels of the studied environmental exposure should only mean gene-environment interaction--even though other environmental factors are not adjusted for. However, this is not true if the odds ratio is the effect measure used for quantifying genetic effects. This is because the odds ratio is a "noncollapsible" measure--a marginal odds ratio is not a weighted average of the conditional odds ratios, but instead has a tendency toward the null. In this study, the authors derive formulae for gene-environment interaction bias due to noncollapsibility. They use computer simulation and real data example to show that the bias can be substantial for common diseases. For genetic association study of nonrare diseases, researchers are advised to use collapsible measures, such as risk ratio or peril ratio.
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Affiliation(s)
- Yi-Shan Su
- From the Institute of Epidemiology and Preventive Medicine (Y-SS, W-CL), College of Public Health, National Taiwan University; and Research Center for Genes, Environment and Human Health (W-CL), College of Public Health, National Taiwan University, Taipei, Taiwan
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Ciolino JD, Martin RH, Zhao W, Jauch EC, Hill MD, Palesch YY. Covariate imbalance and adjustment for logistic regression analysis of clinical trial data. J Biopharm Stat 2014; 23:1383-402. [PMID: 24138438 DOI: 10.1080/10543406.2013.834912] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
In logistic regression analysis for binary clinical trial data, adjusted treatment effect estimates are often not equivalent to unadjusted estimates in the presence of influential covariates. This article uses simulation to quantify the benefit of covariate adjustment in logistic regression. However, International Conference on Harmonization guidelines suggest that covariate adjustment be prespecified. Unplanned adjusted analyses should be considered secondary. Results suggest that if adjustment is not possible or unplanned in a logistic setting, balance in continuous covariates can alleviate some (but never all) of the shortcomings of unadjusted analyses. The case of log binomial regression is also explored.
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Egbewale BE, Lewis M, Sim J. Bias, precision and statistical power of analysis of covariance in the analysis of randomized trials with baseline imbalance: a simulation study. BMC Med Res Methodol 2014; 14:49. [PMID: 24712304 PMCID: PMC3986434 DOI: 10.1186/1471-2288-14-49] [Citation(s) in RCA: 155] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2013] [Accepted: 03/31/2014] [Indexed: 11/17/2022] Open
Abstract
Background Analysis of variance (ANOVA), change-score analysis (CSA) and analysis of covariance (ANCOVA) respond differently to baseline imbalance in randomized controlled trials. However, no empirical studies appear to have quantified the differential bias and precision of estimates derived from these methods of analysis, and their relative statistical power, in relation to combinations of levels of key trial characteristics. This simulation study therefore examined the relative bias, precision and statistical power of these three analyses using simulated trial data. Methods 126 hypothetical trial scenarios were evaluated (126 000 datasets), each with continuous data simulated by using a combination of levels of: treatment effect; pretest-posttest correlation; direction and magnitude of baseline imbalance. The bias, precision and power of each method of analysis were calculated for each scenario. Results Compared to the unbiased estimates produced by ANCOVA, both ANOVA and CSA are subject to bias, in relation to pretest-posttest correlation and the direction of baseline imbalance. Additionally, ANOVA and CSA are less precise than ANCOVA, especially when pretest-posttest correlation ≥ 0.3. When groups are balanced at baseline, ANCOVA is at least as powerful as the other analyses. Apparently greater power of ANOVA and CSA at certain imbalances is achieved in respect of a biased treatment effect. Conclusions Across a range of correlations between pre- and post-treatment scores and at varying levels and direction of baseline imbalance, ANCOVA remains the optimum statistical method for the analysis of continuous outcomes in RCTs, in terms of bias, precision and statistical power.
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Affiliation(s)
| | | | - Julius Sim
- Research Institute for Primary Care and Health Sciences, Keele University, ST5 5BG Staffordshire, UK.
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Chan WK, Redelmeier DA. Authors' reply. Am J Cardiol 2013; 111:303-4. [PMID: 23290603 DOI: 10.1016/j.amjcard.2012.09.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/04/2012] [Accepted: 09/05/2012] [Indexed: 11/17/2022]
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Roetzheim RG, Freund KM, Corle DK, Murray DM, Snyder FR, Kronman AC, Jean-Pierre P, Raich PC, Holden AE, Darnell JS, Warren-Mears V, Patierno S. Analysis of combined data from heterogeneous study designs: an applied example from the patient navigation research program. Clin Trials 2012; 9:176-87. [PMID: 22273587 DOI: 10.1177/1740774511433284] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND The Patient Navigation Research Program (PNRP) is a cooperative effort of nine research projects, with similar clinical criteria but with different study designs. To evaluate projects such as PNRP, it is desirable to perform a pooled analysis to increase power relative to the individual projects. There is no agreed-upon prospective methodology, however, for analyzing combined data arising from different study designs. Expert opinions were thus solicited from the members of the PNRP Design and Analysis Committee. PURPOSE To review possible methodologies for analyzing combined data arising from heterogeneous study designs. METHODS The Design and Analysis Committee critically reviewed the pros and cons of five potential methods for analyzing combined PNRP project data. The conclusions were based on simple consensus. The five approaches reviewed included the following: (1) analyzing and reporting each project separately, (2) combining data from all projects and performing an individual-level analysis, (3) pooling data from projects having similar study designs, (4) analyzing pooled data using a prospective meta-analytic technique, and (5) analyzing pooled data utilizing a novel simulated group-randomized design. RESULTS Methodologies varied in their ability to incorporate data from all PNRP projects, to appropriately account for differing study designs, and to accommodate differing project sample sizes. LIMITATIONS The conclusions reached were based on expert opinion and not derived from actual analyses performed. CONCLUSIONS The ability to analyze pooled data arising from differing study designs may provide pertinent information to inform programmatic, budgetary, and policy perspectives. Multisite community-based research may not lend itself well to the more stringent explanatory and pragmatic standards of a randomized controlled trial design. Given our growing interest in community-based population research, the challenges inherent in the analysis of heterogeneous study design are likely to become more salient. Discussion of the analytic issues faced by the PNRP and the methodological approaches we considered may be of value to other prospective community-based research programs.
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Affiliation(s)
- Richard G Roetzheim
- University of South Florida Department of Family Medicine, Tampa, FL 33612, USA.
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Saver JL. Optimal end points for acute stroke therapy trials: best ways to measure treatment effects of drugs and devices. Stroke 2011; 42:2356-62. [PMID: 21719772 PMCID: PMC3463240 DOI: 10.1161/strokeaha.111.619122] [Citation(s) in RCA: 81] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND AND PURPOSE Over the past decade, analysis of completed actual trials, model population studies, and theoretical work have improved approaches to selecting and analyzing end points in acute stroke treatment trials. METHODS Narrative review. RESULTS Because stroke affects persons in their biological, functional, social, and experiential dimensions, measures of impairment, disability, handicap, and quality of life are all desirable in pivotal trials, with disability being most important. Scales that are valid, reliable, responsive, and easy to administer are preferred; consequently, the modified Rankin Scale has become the most widely used single clinical efficacy measure. Because stroke cripples and kills, most outcome scales array patient outcome in ordered ranks, spread over the entire range from normal to disabled to dead. Generally, shift analysis, analyzing all health state transitions concurrently, is the most efficient analytic technique to detect treatment effects, with sliding dichotomy less efficient and fixed dichotomy least efficient, unless treatment effects strongly cluster at 1 or a few health state transitions that can be prespecified. Test statistics must also take into account interpretability, ie, how well they can be converted into metrics capturing all outcomes the intervention might alter in proportion to the degree they are valued by the patient; full ordinal analysis is most informative, sliding dichotomy is intermediately informative, and fixed dichotomy is least informative regarding this global outcome. CONCLUSIONS Stroke trial power and interpretation can be substantially enhanced by adherence to the principles delineated in this review. Full ordinal and sliding dichotomy analysis will most often be advantageous compared with fixed dichotomous approaches.
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Affiliation(s)
- Jeffrey L Saver
- Stroke Center and Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
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