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Whittle R, Ensor J, Hattle M, Dhiman P, Collins GS, Riley RD. Calculating the power of a planned individual participant data meta-analysis to examine prognostic factor effects for a binary outcome. Res Synth Methods 2024. [PMID: 39046258 DOI: 10.1002/jrsm.1737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Revised: 05/09/2024] [Accepted: 06/26/2024] [Indexed: 07/25/2024]
Abstract
Collecting data for an individual participant data meta-analysis (IPDMA) project can be time consuming and resource intensive and could still have insufficient power to answer the question of interest. Therefore, researchers should consider the power of their planned IPDMA before collecting IPD. Here we propose a method to estimate the power of a planned IPDMA project aiming to synthesise multiple cohort studies to investigate the (unadjusted or adjusted) effects of potential prognostic factors for a binary outcome. We consider both binary and continuous factors and provide a three-step approach to estimating the power in advance of collecting IPD, under an assumption of the true prognostic effect of each factor of interest. The first step uses routinely available (published) aggregate data for each study to approximate Fisher's information matrix and thereby estimate the anticipated variance of the unadjusted prognostic factor effect in each study. These variances are then used in step 2 to estimate the anticipated variance of the summary prognostic effect from the IPDMA. Finally, step 3 uses this variance to estimate the corresponding IPDMA power, based on a two-sided Wald test and the assumed true effect. Extensions are provided to adjust the power calculation for the presence of additional covariates correlated with the prognostic factor of interest (by using a variance inflation factor) and to allow for between-study heterogeneity in prognostic effects. An example is provided for illustration, and Stata code is supplied to enable researchers to implement the method.
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Affiliation(s)
- Rebecca Whittle
- Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
- National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre, UK
| | - Joie Ensor
- Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
- National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre, UK
| | - Miriam Hattle
- Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
- National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre, UK
| | - Paula Dhiman
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Gary S Collins
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Richard D Riley
- Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
- National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre, UK
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2
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Marlin N, Godolphin PJ, Hooper RL, Riley RD, Rogozińska E. Nonlinear effects and effect modification at the participant-level in IPD meta-analysis part 2: methodological guidance is available. J Clin Epidemiol 2023; 159:319-329. [PMID: 37146657 DOI: 10.1016/j.jclinepi.2023.04.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 03/20/2023] [Accepted: 04/26/2023] [Indexed: 05/07/2023]
Abstract
OBJECTIVES To review methodological guidance for nonlinear covariate-outcome associations (NL), and linear effect modification and nonlinear effect modification (LEM and NLEM) at the participant level in individual participant data meta-analyses (IPDMAs) and their power requirements. STUDY DESIGN AND SETTING We searched Medline, Embase, Web of Science, Scopus, PsycINFO and the Cochrane Library to identify methodology publications on IPDMA of LEM, NL or NLEM (PROSPERO CRD42019126768). RESULTS Through screening 6,466 records we identified 54 potential articles of which 23 full texts were relevant. Nine further relevant publications were published before or after the literature search and were added. Of these 32 references, 21 articles considered LEM, 6 articles NL or NLEM and 6 articles described sample size calculations. A book described all four. Sample size may be calculated through simulation or closed form. Assessments of LEM or NLEM at the participant level need to be based on within-trial information alone. Nonlinearity (NL or NLEM) can be modeled using polynomials or splines to avoid categorization. CONCLUSION Detailed methodological guidance on IPDMA of effect modification at participant-level is available. However, methodology papers for sample size and nonlinearity are rarer and may not cover all scenarios. On these aspects, further guidance is needed.
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Affiliation(s)
- Nadine Marlin
- Methodology Research Unit, Centre for Evaluation and Methods, Wolfson Institute of Population Health, Queen Mary University of London, 58 Turner Street, London E1 2AB, UK.
| | - Peter J Godolphin
- MRC Clinical Trials Unit at University College London, Institute of Clinical Trials and Methodology, 90 High Holborn, London WC1V 6LJ, UK
| | - Richard L Hooper
- Methodology Research Unit, Centre for Evaluation and Methods, Wolfson Institute of Population Health, Queen Mary University of London, 58 Turner Street, London E1 2AB, UK
| | - Richard D Riley
- Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham B15 2TT, UK
| | - Ewelina Rogozińska
- MRC Clinical Trials Unit at University College London, Institute of Clinical Trials and Methodology, 90 High Holborn, London WC1V 6LJ, UK
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3
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Marlin N, Godolphin PJ, Hooper RL, Riley RD, Rogozińska E. Nonlinear effects and effect modification at the participant-level in IPD meta-analysis part 1: analysis methods are often substandard. J Clin Epidemiol 2023; 159:309-318. [PMID: 37146661 DOI: 10.1016/j.jclinepi.2023.04.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 03/20/2023] [Accepted: 04/26/2023] [Indexed: 05/07/2023]
Abstract
OBJECTIVES To review analysis methods used for linear effect modification (LEM), nonlinear covariate-outcome associations (NL) and nonlinear effect modification (NLEM) at the participant-level in individual participant data meta-analysis (IPDMA). STUDY DESIGN AND SETTING We searched Medline, Embase, Web of Science, Scopus, PsycINFO and the Cochrane Library to identify IPDMA of randomized controlled trials (PROSPERO CRD42019126768). We investigated if and how IPDMA examined LEM, NL and NLEM, including whether aggregation bias was addressed and if power was considered. RESULTS We screened 6,466 records, randomly sampled 207 and identified 100 IPDMA of LEM, NL or NLEM. Power for LEM was calculated a priori in 3 IPDMA. Of 100 IPDMA, 94 analyzed LEM, 4 NLEM and 8 NL. One-stage models were favoured for all three (56%, 100%, 50%, respectively). Two-stage models were used in 15%, 0% and 25% of IPDMA with unclear descriptions in 30%, 0% and 25%, respectively. Only 12% of one-stage LEM and NLEM IPDMA provided sufficient detail to confirm they had addressed aggregation bias. CONCLUSION Investigation of effect modification at the participant-level is common in IPDMA projects, but methods are often open to bias or lack detailed descriptions. Nonlinearity of continuous covariates and power of IPDMA are rarely assessed.
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Affiliation(s)
- Nadine Marlin
- Methodology Research Unit, Centre for Evaluation and Methods, Wolfson Institute of Population Health, Queen Mary University of London, 58 Turner Street, London E1 2AB, UK.
| | - Peter J Godolphin
- MRC Clinical Trials Unit at University College London, Institute of Clinical Trials and Methodology, 90 High Holborn, London WC1V 6LJ, UK
| | - Richard L Hooper
- Methodology Research Unit, Centre for Evaluation and Methods, Wolfson Institute of Population Health, Queen Mary University of London, 58 Turner Street, London E1 2AB, UK
| | - Richard D Riley
- Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham B15 2TT, UK
| | - Ewelina Rogozińska
- MRC Clinical Trials Unit at University College London, Institute of Clinical Trials and Methodology, 90 High Holborn, London WC1V 6LJ, UK
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Rocha T, Allotey J, Palacios A, Vogel JP, Smits L, Carroli G, Mistry H, Young T, Qureshi ZP, Cormick G, Snell KIE, Abalos E, Pena-Rosas JP, Khan KS, Larbi KK, Thorson A, Singata-Madliki M, Hofmeyr GJ, Bohren M, Riley R, Betran AP, Thangaratinam S. Calcium supplementation to prevent pre-eclampsia: protocol for an individual participant data meta-analysis, network meta-analysis and health economic evaluation. BMJ Open 2023; 13:e065538. [PMID: 37169508 PMCID: PMC10186423 DOI: 10.1136/bmjopen-2022-065538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Accepted: 04/11/2023] [Indexed: 05/13/2023] Open
Abstract
INTRODUCTION Low dietary calcium intake is a risk factor for pre-eclampsia, a major contributor to maternal and perinatal mortality and morbidity worldwide. Calcium supplementation can prevent pre-eclampsia in women with low dietary calcium. However, the optimal dose and timing of calcium supplementation are not known. We plan to undertake an individual participant data (IPD) meta-analysis of randomised trials to determine the effects of various calcium supplementation regimens in preventing pre-eclampsia and its complications and rank these by effectiveness. We also aim to evaluate the cost-effectiveness of calcium supplementation to prevent pre-eclampsia. METHODS AND ANALYSIS We will identify randomised trials on calcium supplementation before and during pregnancy by searching major electronic databases including Embase, CINAHL, MEDLINE, CENTRAL, PubMed, Scopus, AMED, LILACS, POPLINE, AIM, IMSEAR, ClinicalTrials.gov and the WHO International Clinical Trials Registry Platform, without language restrictions, from inception to February 2022. Primary researchers of the identified trials will be invited to join the International Calcium in Pregnancy Collaborative Network and share their IPD. We will check each study's IPD for consistency with the original authors before standardising and harmonising the data. We will perform a series of one-stage and two-stage IPD random-effect meta-analyses to obtain the summary intervention effects on pre-eclampsia with 95% CIs and summary treatment-covariate interactions (maternal risk status, dietary intake, timing of intervention, daily dose of calcium prescribed and total intake of calcium). Heterogeneity will be summarised using tau2, I2 and 95% prediction intervals for effect in a new study. Sensitivity analysis to explore robustness of statistical and clinical assumptions will be carried out. Minor study effects (potential publication bias) will be investigated using funnel plots. A decision analytical model for use in low-income and middle-income countries will assess the cost-effectiveness of calcium supplementation to prevent pre-eclampsia. ETHICS AND DISSEMINATION No ethical approvals are required. We will store the data in a secure repository in an anonymised format. The results will be published in peer-reviewed journals. PROSPERO REGISTRATION NUMBER CRD42021231276.
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Affiliation(s)
- Thaís Rocha
- WHO Collaborating Centre for Global Women's Health, Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK
| | - John Allotey
- WHO Collaborating Centre for Global Women's Health, Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK
| | - Alfredo Palacios
- Health Economics, Institute for Clinical Effectiveness and Health Policy, Buenos Aires, Argentina
| | - Joshua Peter Vogel
- Maternal, Child and Adolescent Health Program, Burnet Institute, Melbourne, Victoria, Australia
| | - Luc Smits
- Department of Epidemiology, Care and Public Health Research Institute, Maastricht University, Maastricht, The Netherlands
| | | | - Hema Mistry
- Warwick Evidence, University of Warwick, Coventry, UK
| | - Taryn Young
- Centre for Evidence-Based Health Care, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
- South African Cochrane Centre, South African Medical Research Council, Cape Town, South Africa
| | - Zahida P Qureshi
- Department of Obstetrics and Gynecology, University of Nairobi, Nairobi, Kenya
| | - Gabriela Cormick
- Department of Health Technology Assessment and Health Economics, Institute for Clinical Effectiveness and Health Policy, Buenos Aires, Argentina
| | - Kym I E Snell
- Centre for Prognosis Research, School of Medicine, Keele University, Keele, UK
| | - Edgardo Abalos
- Centro Rosarino de Estudios Perinatales (CREP), Rosario, Argentina
| | | | - Khalid Saeed Khan
- Public Health, University of Granada Faculty of Medicine, Granada, Spain
| | | | - Anna Thorson
- Reproductive Health and Research, World Health Organization, Geneva, Switzerland
| | - Mandisa Singata-Madliki
- Effective Care Research Unit (ECRU), East London Hospital Complex, East London, South Africa
| | | | - Meghan Bohren
- Centre for Health Equity, University of Melbourne School of Population and Global Health, Carlton, Victoria, Australia
| | - Richard Riley
- Centre for Prognosis Research, School of Medicine, Keele University, Keele, UK
| | - Ana Pilar Betran
- Reproductive Health and Research, World Health Organization, Geneva, Switzerland
| | - Shakila Thangaratinam
- WHO Collaborating Centre for Global Women's Health, Institute of Metabolism and Systems Research, University of Birmingham College of Medical and Dental Sciences, Birmingham, UK
- Birmingham Women's and Children's Hospitals NHS Foundation Trust, Birmingham, UK
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Do better nights lead to better days? Guided internet-based cognitive behavioral therapy for insomnia in people suffering from a range of mental health problems: Protocol of a pragmatic randomized clinical trial. Contemp Clin Trials 2023; 127:107122. [PMID: 36813085 DOI: 10.1016/j.cct.2023.107122] [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: 12/09/2022] [Revised: 02/10/2023] [Accepted: 02/14/2023] [Indexed: 02/22/2023]
Abstract
BACKGROUND Insomnia is the transdiagnostically shared most common complaint in disorders of anxiety, stress and emotion regulation. Current cognitive behavioral therapies (CBT) for these disorders do not address sleep, while good sleep is essential for regulating emotions and learning new cognitions and behaviours: the core fundaments of CBT. This transdiagnostic randomized control trial (RCT) evaluates whether guided internet-delivered cognitive behavioral therapy for insomnia (iCBT-I) (1) improves sleep, (2) affects the progression of emotional distress and (3) enhances the effectiveness of regular treatment of people with clinically relevant symptoms of emotional disorders across all mental health care (MHC) echelons. METHODS We aim for 576 completers with clinically relevant symptoms of insomnia as well as at least one of the dimensions of generalized anxiety disorder (GAD), social anxiety disorder (SAD), panic disorder (PD), posttraumatic stress disorder (PTSD) or borderline personality disorder (BPD). Participants are either pre-clinical, unattended, or referred to general- or specialized MHC. Using covariate-adaptive randomization, participants will be assigned to a 5 to 8-week iCBT-I (i-Sleep) or a control condition (sleep diary only) and assessed at baseline, and after two and eight months. The primary outcome is insomnia severity. Secondary outcomes address sleep, severity of mental health symptoms, daytime functioning, mental health protective lifestyles, well-being, and process evaluation measures. Analyses use linear mixed-effect regression models. DISCUSSION This study can reveal for whom, and at which stage of disease progression, better nights could mean substantially better days. TRIAL REGISTRATION International Clinical Trial Registry Platform (NL9776). Registered on 2021-10-07.
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Riley RD, Hattle M, Collins GS, Whittle R, Ensor J. Calculating the power to examine treatment-covariate interactions when planning an individual participant data meta-analysis of randomized trials with a binary outcome. Stat Med 2022; 41:4822-4837. [PMID: 35932153 PMCID: PMC9805219 DOI: 10.1002/sim.9538] [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: 03/17/2022] [Revised: 06/30/2022] [Accepted: 07/12/2022] [Indexed: 01/09/2023]
Abstract
Before embarking on an individual participant data meta-analysis (IPDMA) project, researchers and funders need assurance it is worth their time and cost. This should include consideration of how many studies are promising their IPD and, given the characteristics of these studies, the power of an IPDMA including them. Here, we show how to estimate the power of a planned IPDMA of randomized trials to examine treatment-covariate interactions at the participant level (ie, treatment effect modifiers). We focus on a binary outcome with binary or continuous covariates, and propose a three-step approach, which assumes the true interaction size is common to all trials. In step one, the user must specify a minimally important interaction size and, for each trial separately (eg, as obtained from trial publications), the following aggregate data: the number of participants and events in control and treatment groups, the mean and SD for each continuous covariate, and the proportion of participants in each category for each binary covariate. This allows the variance of the interaction estimate to be calculated for each trial, using an analytic solution for Fisher's information matrix from a logistic regression model. Step 2 calculates the variance of the summary interaction estimate from the planned IPDMA (equal to the inverse of the sum of the inverse trial variances from step 1), and step 3 calculates the corresponding power based on a two-sided Wald test. Stata and R code are provided, and two examples given for illustration. Extension to allow for between-study heterogeneity is also considered.
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Affiliation(s)
- Richard D. Riley
- Centre for Prognosis Research, School of MedicineKeele UniversityKeeleStaffordshireUK
| | - Miriam Hattle
- Centre for Prognosis Research, School of MedicineKeele UniversityKeeleStaffordshireUK
| | - Gary S. Collins
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal SciencesUniversity of OxfordOxfordUK
- NIHR Oxford Biomedical Research CentreOxford University Hospitals NHS Foundation TrustOxfordUK
| | - Rebecca Whittle
- Centre for Prognosis Research, School of MedicineKeele UniversityKeeleStaffordshireUK
| | - Joie Ensor
- Centre for Prognosis Research, School of MedicineKeele UniversityKeeleStaffordshireUK
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7
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Kokosi T, Harron K. Synthetic data in medical research. BMJ MEDICINE 2022; 1:e000167. [PMID: 36936569 PMCID: PMC9951365 DOI: 10.1136/bmjmed-2022-000167] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 08/26/2022] [Indexed: 06/18/2023]
Affiliation(s)
- Theodora Kokosi
- Population, Policy, and Practice Department, UCL Great Ormond Street Institute of Child Health, London, UK
| | - Katie Harron
- Population, Policy, and Practice Department, UCL Great Ormond Street Institute of Child Health, London, UK
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8
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Abstract
In research, policy, and practice, continuous variables are often categorized. Statisticians have generally advised against categorization for many reasons, such as loss of information and precision as well as distortion of estimated statistics. Here, a different kind of problem with categorization is considered: the idea that, for a given continuous variable, there is a unique set of cut points that is the objectively correct or best categorization. It is shown that this is unlikely to be the case because categorized variables typically exist in webs of statistical relationships with other variables. The choice of cut points for a categorized variable can influence the values of many statistics relating that variable to others. This essay explores the substantive trade‐offs that can arise between different possible cut points to categorize a continuous variable, making it difficult to say that any particular categorization is objectively best. Limitations of different approaches to selecting cut points are discussed. Contextual trade‐offs may often be an argument against categorization. At the very least, such trade‐offs mean that research inferences, or decisions about policy or practice, that involve categorized variables should be framed and acted upon with flexibility and humility. In practical settings, the choice of cut points for categorizing a continuous variable is likely to entail trade‐offs across multiple statistical relationships between the categorized variable and other variables. These trade‐offs mean that no single categorization is objectively best or correct.
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Affiliation(s)
- Evan L Busch
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts.,Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts
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van Tuijl LA, Voogd AC, de Graeff A, Hoogendoorn AW, Ranchor AV, Pan KY, Basten M, Lamers F, Geerlings MI, Abell JG, Awadalla P, Bakker MF, Beekman ATF, Bjerkeset O, Boyd A, Cui Y, Galenkamp H, Garssen B, Hellingman S, Huisman M, Huss A, Keats MR, Kok AAL, Luik AI, Noisel N, Onland-Moret NC, Payette Y, Penninx BWJH, Portengen L, Rissanen I, Roest AM, Rosmalen JGM, Ruiter R, Schoevers RA, Soave DM, Spaan M, Steptoe A, Stronks K, Sund ER, Sweeney E, Teyhan A, Vaartjes I, van der Willik KD, van Leeuwen FE, van Petersen R, Verschuren WMM, Visseren F, Vermeulen R, Dekker J. Psychosocial factors and cancer incidence (PSY-CA): Protocol for individual participant data meta-analyses. Brain Behav 2021; 11:e2340. [PMID: 34473425 PMCID: PMC8553309 DOI: 10.1002/brb3.2340] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Accepted: 08/12/2021] [Indexed: 01/12/2023] Open
Abstract
OBJECTIVES Psychosocial factors have been hypothesized to increase the risk of cancer. This study aims (1) to test whether psychosocial factors (depression, anxiety, recent loss events, subjective social support, relationship status, general distress, and neuroticism) are associated with the incidence of any cancer (any, breast, lung, prostate, colorectal, smoking-related, and alcohol-related); (2) to test the interaction between psychosocial factors and factors related to cancer risk (smoking, alcohol use, weight, physical activity, sedentary behavior, sleep, age, sex, education, hormone replacement therapy, and menopausal status) with regard to the incidence of cancer; and (3) to test the mediating role of health behaviors (smoking, alcohol use, weight, physical activity, sedentary behavior, and sleep) in the relationship between psychosocial factors and the incidence of cancer. METHODS The psychosocial factors and cancer incidence (PSY-CA) consortium was established involving experts in the field of (psycho-)oncology, methodology, and epidemiology. Using data collected in 18 cohorts (N = 617,355), a preplanned two-stage individual participant data (IPD) meta-analysis is proposed. Standardized analyses will be conducted on harmonized datasets for each cohort (stage 1), and meta-analyses will be performed on the risk estimates (stage 2). CONCLUSION PSY-CA aims to elucidate the relationship between psychosocial factors and cancer risk by addressing several shortcomings of prior meta-analyses.
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Affiliation(s)
- Lonneke A van Tuijl
- Department of Internal Medicine, Maasstad Hospital, Rotterdam, The Netherlands
| | - Adri C Voogd
- Department of Internal Medicine, Division of Medical Oncology, GROW, Maastricht University Medical Centre, Maastricht, The Netherlands.,Department of Epidemiology, GROW, Maastricht University, Maastricht, The Netherlands.,Department of Research, Netherlands Comprehensive Cancer Organization (IKNL), Utrecht, The Netherlands
| | - Alexander de Graeff
- Department of Medical Oncology, Cancer Center University Medical Center, University of Utrecht, Utrecht, The Netherlands
| | - Adriaan W Hoogendoorn
- Amsterdam UMC, Department of Psychiatry, Amsterdam Public Health Research Institute, Vrije Universiteit, Amsterdam, The Netherlands.,GGZ inGeest Specialized Mental Health Care, Amsterdam, The Netherlands
| | - Adelita V Ranchor
- Department of Internal Medicine, Maasstad Hospital, Rotterdam, The Netherlands
| | - Kuan-Yu Pan
- Amsterdam UMC, Department of Psychiatry, Amsterdam Public Health Research Institute, Vrije Universiteit, Amsterdam, The Netherlands
| | - Maartje Basten
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands
| | - Femke Lamers
- Amsterdam UMC, Department of Psychiatry, Amsterdam Public Health Research Institute, Vrije Universiteit, Amsterdam, The Netherlands
| | - Mirjam I Geerlings
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands
| | - Jessica G Abell
- Department of Behavioural Science and Health, University College London, London, UK
| | - Philip Awadalla
- Ontario Institute for Cancer Research, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Marije F Bakker
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands
| | - Aartjan T F Beekman
- Amsterdam UMC, Department of Psychiatry, Amsterdam Public Health Research Institute, Vrije Universiteit, Amsterdam, The Netherlands
| | - Ottar Bjerkeset
- Faculty of Nursing and Health Sciences, Nord University, Bodø, Norway.,Faculty of Medicine and Health Sciences, Department of Mental Health, Norwegian University of Science and Technology, Trondheim, Norway
| | - Andy Boyd
- Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK
| | - Yunsong Cui
- Atlantic Partnership for Tomorrow's Health, Faculty of Health, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Henrike Galenkamp
- Department of Public and Occupational Health, Amsterdam UMC, and Amsterdam Public Health Research Institute, University of Amsterdam, Amsterdam, Netherlands
| | - Bert Garssen
- Department of Internal Medicine, Maasstad Hospital, Rotterdam, The Netherlands
| | - Sean Hellingman
- Department of Mathematics, Wilfrid Laurier University, Waterloo, Canada
| | - Martijn Huisman
- Amsterdam UMC, Department of Epidemiology & Data Science, Amsterdam Public Health institute, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.,Department of Sociology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Anke Huss
- Institute for Risk Assessment Sciences, Utrecht University, Utrecht, The Netherlands
| | - Melanie R Keats
- School of Health and Human Performance, Faculty of Health, Dalhousie University, Halifax, Canada
| | - Almar A L Kok
- Amsterdam UMC, Department of Psychiatry, Amsterdam Public Health Research Institute, Vrije Universiteit, Amsterdam, The Netherlands.,Amsterdam UMC, Department of Epidemiology & Data Science, Amsterdam Public Health institute, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Annemarie I Luik
- Department of Epidemiology, Erasmus MC-University Medical Center, Rotterdam, The Netherlands.,Department of Child and Adolescent Psychiatry/Psychology, Erasmus MC-University Medical Center, Rotterdam, The Netherlands
| | - Nolwenn Noisel
- CARTaGENE, CHU Sainte-Justine, 3175, Chemin de la Côte-Sainte-Catherine, Montréal, Québec, Canada
| | - N Charlotte Onland-Moret
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands
| | - Yves Payette
- CARTaGENE, CHU Sainte-Justine, 3175, Chemin de la Côte-Sainte-Catherine, Montréal, Québec, Canada
| | - Brenda W J H Penninx
- Amsterdam UMC, Department of Psychiatry, Amsterdam Public Health Research Institute, Vrije Universiteit, Amsterdam, The Netherlands
| | - Lützen Portengen
- Institute for Risk Assessment Sciences, Utrecht University, Utrecht, The Netherlands
| | - Ina Rissanen
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands
| | - Annelieke M Roest
- Department of Developmental Psychology, University of Groningen, Groningen, The Netherlands
| | - Judith G M Rosmalen
- Departments of Psychiatry and Internal Medicine, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Rikje Ruiter
- Department of Epidemiology, Erasmus MC-University Medical Center, Rotterdam, The Netherlands.,Department of Internal Medicine, Maasstad, Rotterdam, The Netherlands
| | - Robert A Schoevers
- Department of Psychiatry, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - David M Soave
- Ontario Institute for Cancer Research, Toronto, Ontario, Canada.,Department of Mathematics, Wilfrid Laurier University, Waterloo, Canada
| | - Mandy Spaan
- Division of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Andrew Steptoe
- Department of Behavioural Science and Health, University College London, London, UK
| | - Karien Stronks
- Department of Public and Occupational Health, Amsterdam UMC, and Amsterdam Public Health Research Institute, University of Amsterdam, Amsterdam, Netherlands
| | - Erik R Sund
- Faculty of Nursing and Health Sciences, Nord University, Bodø, Norway.,Department of Public Health and Nursing, HUNT Research Centre, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.,Levanger hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway
| | - Ellen Sweeney
- Atlantic Partnership for Tomorrow's Health, Faculty of Health, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Alison Teyhan
- Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK
| | - Ilonca Vaartjes
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands
| | - Kimberly D van der Willik
- Department of Epidemiology, Erasmus MC-University Medical Center, Rotterdam, The Netherlands.,Division of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Flora E van Leeuwen
- Division of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Rutger van Petersen
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands
| | - W M Monique Verschuren
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands.,Centre for Nutrition, Prevention and Health Services, National Institute for Public Health and the Environment, Utrecht, the Netherlands
| | - Frank Visseren
- Department of Vascular Medicine, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Roel Vermeulen
- Institute for Risk Assessment Sciences, Utrecht University, Utrecht, The Netherlands
| | - Joost Dekker
- Amsterdam Public Health Research Institute, Amsterdam, Noord-Holland, The Netherlands.,Department of Rehabilitation Medicine and Department of Psychiatry, Amsterdam UMC - VUMC, Amsterdam, Noord-Holland, The Netherlands
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10
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Coomar D, Hazlehurst JM, Austin F, Foster C, Hitman GA, Heslehurst N, Iliodromiti S, Betran AP, Moss N, Poston L, Nirantharakumar K, Roberts T, Simpson SA, Teede HJ, Riley R, Allotey J, Thangaratinam S. Diet and physical activity in pregnancy to prevent gestational diabetes: a protocol for an individual participant data (IPD) meta-analysis on the differential effects of interventions with economic evaluation. BMJ Open 2021; 11:e048119. [PMID: 34117047 PMCID: PMC8202105 DOI: 10.1136/bmjopen-2020-048119] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/19/2020] [Revised: 03/15/2021] [Accepted: 03/16/2021] [Indexed: 12/28/2022] Open
Abstract
INTRODUCTION Mothers with gestational diabetes mellitus (GDM) are at increased risk of pregnancy-related complications and developing type 2 diabetes after delivery. Diet and physical activity-based interventions may prevent GDM, but variations in populations, interventions and outcomes in primary trials have limited the translation of available evidence into practice. We plan to undertake an individual participant data (IPD) meta-analysis of randomised trials to assess the differential effects and cost-effectiveness of diet and physical activity-based interventions in preventing GDM and its complications. METHODS The International Weight Management in Pregnancy Collaborative Network database is a living repository of IPD from randomised trials on diet and physical activity in pregnancy identified through a systematic literature search. We shall update our existing search on MEDLINE, Embase, BIOSIS, LILACS, Pascal, Science Citation Index, Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials, Database of Abstracts of Reviews of Effects and Health Technology Assessment Database without language restriction to identify relevant trials until March 2021. Primary researchers will be invited to join the Network and share their IPD. Trials including women with GDM at baseline will be excluded. We shall perform a one and two stage random-effect meta-analysis for each intervention type (all interventions, diet-based, physical activity-based and mixed approach) to obtain summary intervention effects on GDM with 95% CIs and summary treatment-covariate interactions. Heterogeneity will be summarised using I2 and tau2 statistics with 95% prediction intervals. Publication and availability bias will be assessed by examining small study effects. Study quality of included trials will be assessed by the Cochrane Risk of Bias tool, and the Grading of Recommendations, Assessment, Development and Evaluations approach will be used to grade the evidence in the results. A model-based economic analysis will be carried out to assess the cost-effectiveness of interventions to prevent GDM and its complications compared with usual care. ETHICS AND DISSEMINATION Ethics approval is not required. The study is registered on the International Prospective Register of Systematic Reviews (CRD42020212884). Results will be submitted for publication in peer-reviewed journals.
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Affiliation(s)
- Dyuti Coomar
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK
| | - Jonathan M Hazlehurst
- WHO Collaborating Centre for Global Women's Health, Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK
| | - Frances Austin
- Maternity Dietetic Service, Women's Health Division, Barts Health NHS Trust, Antenatal Clinic, Royal London Hospital and Newham University Hospital, London, UK
| | - Charlie Foster
- Centre for Exercise, Nutrition and Health Sciences, School for Policy Studies, University of Bristol, Bristol, UK
| | - Graham A Hitman
- Centre for Genomic Medicine and Child Health, Blizard Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Nicola Heslehurst
- Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Stamatina Iliodromiti
- Centre for Women's Health, Institute of Population Health Sciences, Queen Mary University of London, London, UK
| | - Ana Pilar Betran
- UNDP/UNFPA/UNICEF/WHO/World Bank Special Programme of Research, Development and Research Training in Human Reproduction, Department of Reproductive Health and Research, WHO, Geneva, Switzerland
| | - Ngawai Moss
- Katie's Team Patient and Public Involvement Advisory Group, Queen Mary University of London, London, UK
| | - Lucilla Poston
- Department of Women and Children's Health, School of Life Course Sciences, King's College London, London, UK
| | | | - Tracy Roberts
- Health Economics Unit, University of Birmingham, Birmingham, UK
| | - Sharon A Simpson
- MRC/CSO Social and Public Health Sciences Unit, University of Glasgow, Glasgow, UK
| | - Helena J Teede
- Monash Centre for Health Research and Implementation, School of Public Health, Monash University, Melbourne, Victoria, Australia
| | - Richard Riley
- Centre for Prognosis Research, School of Medicine, Keele University, Keele, UK
| | - John Allotey
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK
- Pragmatic Clinical Trials Unit, Institute of Population Health Sciences, Barts and the London, School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Shakila Thangaratinam
- WHO Collaborating Centre for Global Women's Health, Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK
- Birmingham Women's and Children's NHS Foundation Trust, Birmingham, UK
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11
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Buller ID, Brown DW, Myers TA, Jones RR, Machiela MJ. sparrpowR: a flexible R package to estimate statistical power to identify spatial clustering of two groups and its application. Int J Health Geogr 2021; 20:13. [PMID: 33736677 PMCID: PMC7977178 DOI: 10.1186/s12942-021-00267-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Accepted: 02/26/2021] [Indexed: 11/10/2022] Open
Abstract
Background Cancer epidemiology studies require sufficient power to assess spatial relationships between exposures and cancer incidence accurately. However, methods for power calculations of spatial statistics are complicated and underdeveloped, and therefore underutilized by investigators. The spatial relative risk function, a cluster detection technique that detects spatial clusters of point-level data for two groups (e.g., cancer cases and controls, two exposure groups), is a commonly used spatial statistic but does not have a readily available power calculation for study design. Results We developed sparrpowR as an open-source R package to estimate the statistical power of the spatial relative risk function. sparrpowR generates simulated data applying user-defined parameters (e.g., sample size, locations) to detect spatial clusters with high statistical power. We present applications of sparrpowR that perform a power calculation for a study designed to detect a spatial cluster of incident cancer in relation to a point source of numerous environmental emissions. The conducted power calculations demonstrate the functionality and utility of sparrpowR to calculate the local power for spatial cluster detection. Conclusions sparrpowR improves the current capacity of investigators to calculate the statistical power of spatial clusters, which assists in designing more efficient studies. This newly developed R package addresses a critically underdeveloped gap in cancer epidemiology by estimating statistical power for a common spatial cluster detection technique.
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Affiliation(s)
- Ian D Buller
- Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD, 20850, USA. .,Cancer Prevention Fellowship Program, Division of Cancer Prevention, National Cancer Institute, Rockville, MD, 20850, USA.
| | - Derek W Brown
- Cancer Prevention Fellowship Program, Division of Cancer Prevention, National Cancer Institute, Rockville, MD, 20850, USA.,Integrative Tumor Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, 20850, USA
| | - Timothy A Myers
- Laboratory of Genetic Susceptibility, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, 20850, USA
| | - Rena R Jones
- Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD, 20850, USA
| | - Mitchell J Machiela
- Integrative Tumor Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, 20850, USA
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12
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Wu IXY, Xiao F, Wang H, Chen Y, Zhang Z, Lin Y, Tam W. Trials number, funding support, and intervention type associated with IPDMA data retrieval: a cross-sectional study. J Clin Epidemiol 2020; 130:59-68. [PMID: 33098991 DOI: 10.1016/j.jclinepi.2020.10.011] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2020] [Revised: 09/17/2020] [Accepted: 10/15/2020] [Indexed: 01/08/2023]
Abstract
OBJECTIVES This study aimed to investigate the predictors for high data retrieval and the reporting of individual participant data meta-analyses (IPDMAs). STUDY DESIGN AND SETTING We searched EMBASE, MEDLINE, and the Cochrane Library for articles pertaining to IPDMA from 2011 to 2019. Only IPDMA assessing treatment effects, including randomized controlled trials (RCTs), were included. Adherence to the PRISMA-IPD guideline was checked. RESULTS A total of 210 IPDMA covering 18 diseases were sampled; 80 (38.1%) and 123 (58.6%) of the IPDMA retrieved IPD from all and ≥80% RCTs, respectively. Non-Cochrane reviews, IPDMA on nonpharmacological interventions, analyses of smaller numbers of RCTs, and having funding supports were predictors of complete IPD retrieval. Owners of RCTs had an increased probability of obtaining IPD. Only 4.3% described the eligibility criteria covering all the PICO components, 11.0% reported the methods for assessing risk of bias across studies, 11.4% mentioned the IPD integrity, and 9.0% presented detailed results of syntheses. CONCLUSION IPD retrieval and reporting was not satisfactory among the published IPDMA. Encouraging RCT owners to conduct or join in the IPDMA is a potential strategy to maximize the IPD retrieval. IPDMA are suggested to adhere to the PRISMA-IPD guideline during reporting.
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Affiliation(s)
- Irene X Y Wu
- Xiangya School of Public Health, Central South University, Changsha, China; Hunan Provincial Key Laboratory of Clinical Epidemiology, Changsha, China
| | - Fang Xiao
- Xiangya School of Public Health, Central South University, Changsha, China
| | - Huan Wang
- Xiangya School of Public Health, Central South University, Changsha, China
| | - Yancong Chen
- Xiangya School of Public Health, Central South University, Changsha, China
| | - Zixuan Zhang
- Xiangya School of Public Health, Central South University, Changsha, China
| | - Yali Lin
- Xiangya School of Public Health, Central South University, Changsha, China
| | - Wilson Tam
- Alice Lee Centre for Nursing Studies, National University of Singapore, Singapore.
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13
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Riley RD, Debray TPA, Fisher D, Hattle M, Marlin N, Hoogland J, Gueyffier F, Staessen JA, Wang J, Moons KGM, Reitsma JB, Ensor J. Individual participant data meta-analysis to examine interactions between treatment effect and participant-level covariates: Statistical recommendations for conduct and planning. Stat Med 2020; 39:2115-2137. [PMID: 32350891 PMCID: PMC7401032 DOI: 10.1002/sim.8516] [Citation(s) in RCA: 81] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Revised: 02/07/2020] [Accepted: 02/08/2020] [Indexed: 01/06/2023]
Abstract
Precision medicine research often searches for treatment‐covariate interactions, which refers to when a treatment effect (eg, measured as a mean difference, odds ratio, hazard ratio) changes across values of a participant‐level covariate (eg, age, gender, biomarker). Single trials do not usually have sufficient power to detect genuine treatment‐covariate interactions, which motivate the sharing of individual participant data (IPD) from multiple trials for meta‐analysis. Here, we provide statistical recommendations for conducting and planning an IPD meta‐analysis of randomized trials to examine treatment‐covariate interactions. For conduct, two‐stage and one‐stage statistical models are described, and we recommend: (i) interactions should be estimated directly, and not by calculating differences in meta‐analysis results for subgroups; (ii) interaction estimates should be based solely on within‐study information; (iii) continuous covariates and outcomes should be analyzed on their continuous scale; (iv) nonlinear relationships should be examined for continuous covariates, using a multivariate meta‐analysis of the trend (eg, using restricted cubic spline functions); and (v) translation of interactions into clinical practice is nontrivial, requiring individualized treatment effect prediction. For planning, we describe first why the decision to initiate an IPD meta‐analysis project should not be based on between‐study heterogeneity in the overall treatment effect; and second, how to calculate the power of a potential IPD meta‐analysis project in advance of IPD collection, conditional on characteristics (eg, number of participants, standard deviation of covariates) of the trials (potentially) promising their IPD. Real IPD meta‐analysis projects are used for illustration throughout.
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Affiliation(s)
- Richard D Riley
- Centre for Prognosis Research, School of Primary, Community and Social Care, Keele University, Staffordshire, UK
| | - Thomas P A Debray
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
| | - David Fisher
- MRC Clinical Trials Unit, Institute of Clinical Trials & Methodology, Faculty of Population Health Sciences, University College London, London, UK
| | - Miriam Hattle
- Centre for Prognosis Research, School of Primary, Community and Social Care, Keele University, Staffordshire, UK
| | - Nadine Marlin
- Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Jeroen Hoogland
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
| | | | - Jan A Staessen
- Department of Cardiovascular Sciences, Research Unit Hypertension and Cardiovascular Epidemiology, Studies Coordinating Centre, KU Leuven, Leuven, Belgium
| | - Jiguang Wang
- Centre for Epidemiological Studies and Clinical Trials, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Karel G M Moons
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Johannes B Reitsma
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Joie Ensor
- Centre for Prognosis Research, School of Primary, Community and Social Care, Keele University, Staffordshire, UK
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