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Richard D Riley - One of the best experts on this subject based on the ideXlab platform.
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Individual Participant data validation of the PICNICC prediction model for febrile neutropenia.
Archives of disease in childhood, 2019Co-Authors: Bob Phillips, Jessica E. Morgan, Gabrielle M Haeusler, Richard D RileyAbstract:BACKGROUND: Risk-stratified approaches to managing cancer therapies and their consequent complications rely on accurate predictions to work effectively. The risk-stratified management of fever with neutropenia is one such very common area of management in paediatric practice. Such rules are frequently produced and promoted without adequate confirmation of their accuracy. METHODS: An Individual Participant data meta-analytic validation of the 'Predicting Infectious ComplicatioNs In Children with Cancer' (PICNICC) prediction model for microbiologically documented infection in paediatric fever with neutropenia was undertaken. Pooled estimates were produced using random-effects meta-analysis of the area under the curve-receiver operating characteristic curve (AUC-ROC), calibration slope and ratios of expected versus observed cases (E/O). RESULTS: The PICNICC model was poorly predictive of microbiologically documented infection (MDI) in these validation cohorts. The pooled AUC-ROC was 0.59, 95% CI 0.41 to 0.78, tau2=0, compared with derivation value of 0.72, 95% CI 0.71 to 0.76. There was poor discrimination (pooled slope estimate 0.03, 95% CI -0.19 to 0.26) and calibration in the large (pooled E/O ratio 1.48, 95% CI 0.87 to 2.1). Three different simple recalibration approaches failed to improve performance meaningfully. CONCLUSION: This meta-analysis shows the PICNICC model should not be used at admission to predict MDI. Further work should focus on validating alternative prediction models. Validation across multiple cohorts from diverse locations is essential before widespread clinical adoption of such rules to avoid overtreating or undertreating children with fever with neutropenia.
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deriving percentage study weights in multi parameter meta analysis models with application to meta regression network meta analysis and one stage Individual Participant data models
Statistical Methods in Medical Research, 2018Co-Authors: Richard D Riley, Joie Ensor, Dan Jackson, D BurkeAbstract:Many meta-analysis models contain multiple parameters, for example due to multiple outcomes, multiple treatments or multiple regression coefficients. In particular, meta-regression models may contain multiple study-level covariates, and one-stage Individual Participant data meta-analysis models may contain multiple patient-level covariates and interactions. Here, we propose how to derive percentage study weights for such situations, in order to reveal the (otherwise hidden) contribution of each study toward the parameter estimates of interest. We assume that studies are independent, and utilise a decomposition of Fisher’s information matrix to decompose the total variance matrix of parameter estimates into study-specific contributions, from which percentage weights are derived. This approach generalises how percentage weights are calculated in a traditional, single parameter meta-analysis model. Application is made to one- and two-stage Individual Participant data meta-analyses, meta-regression and networ...
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Individual Participant data meta-analysis of continuous outcomes: A comparison of approaches for specifying and estimating one-stage models.
Statistics in medicine, 2018Co-Authors: Amardeep Legha, Richard D Riley, Joie Ensor, Kym I.e. Snell, Tim P. Morris, D BurkeAbstract:One-stage Individual Participant data meta-analysis models should account for within-trial clustering, but it is currently debated how to do this. For continuous outcomes modeled using a linear regression framework, two competing approaches are a stratified intercept or a random intercept. The stratified approach involves estimating a separate intercept term for each trial, whereas the random intercept approach assumes that trial intercepts are drawn from a normal distribution. Here, through an extensive simulation study for continuous outcomes, we evaluate the impact of using the stratified and random intercept approaches on statistical properties of the summary treatment effect estimate. Further aims are to compare (i) competing estimation options for the one-stage models, including maximum likelihood and restricted maximum likelihood, and (ii) competing options for deriving confidence intervals (CI) for the summary treatment effect, including the standard normal-based 95% CI, and more conservative approaches of Kenward-Roger and Satterthwaite, which inflate CIs to account for uncertainty in variance estimates. The findings reveal that, for an Individual Participant data meta-analysis of randomized trials with a 1:1 treatment:control allocation ratio and heterogeneity in the treatment effect, (i) bias and coverage of the summary treatment effect estimate are very similar when using stratified or random intercept models with restricted maximum likelihood, and thus either approach could be taken in practice, (ii) CIs are generally best derived using either a Kenward-Roger or Satterthwaite correction, although occasionally overly conservative, and (iii) if maximum likelihood is required, a random intercept performs better than a stratified intercept model. An illustrative example is provided.
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Individual Participant Data (IPD) Meta-analyses of Randomised Controlled Trials: Guidance on Their Use
PLoS medicine, 2015Co-Authors: Jayne F. Tierney, Richard D Riley, Mike Clarke, Claire L Vale, Catrin Tudur Smith, Lesley A. Stewart, Maroeska M. RoversAbstract:Jayne Tierney and colleagues offer guidance on how to spot a well-designed and well-conducted Individual Participant data meta-analysis.
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Multivariate meta-analysis using Individual Participant data
Research synthesis methods, 2014Co-Authors: Richard D Riley, Dan Jackson, François Gueyffier, Malcolm J Price, M. Wardle, Ji-guang Wang, Jan A. Staessen, Ian R. WhiteAbstract:When combining results across related studies, a multivariate meta-analysis allows the joint synthesis of correlated effect estimates from multiple outcomes. Joint synthesis can improve efficiency over separate univariate syntheses, may reduce selective outcome reporting biases, and enables joint inferences across the outcomes. A common issue is that within-study correlations needed to fit the multivariate model are unknown from published reports. However, provision of Individual Participant data (IPD) allows them to be calculated directly. Here, we illustrate how to use IPD to estimate within-study correlations, using a joint linear regression for multiple continuous outcomes and bootstrapping methods for binary, survival and mixed outcomes. In a meta-analysis of 10 hypertension trials, we then show how these methods enable multivariate meta-analysis to address novel clinical questions about continuous, survival and binary outcomes; treatment–covariate interactions; adjusted risk/prognostic factor effects; longitudinal data; prognostic and multiparameter models; and multiple treatment comparisons. Both frequentist and Bayesian approaches are applied, with example software code provided to derive within-study correlations and to fit the models. © 2014 The Authors. Research Synthesis Methods published by John Wiley & Sons, Ltd.
Simon G. Thompson - One of the best experts on this subject based on the ideXlab platform.
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Inflammatory markers and extent and progression of early atherosclerosis: Pooled analysis of Individual Participant data from 20 prospective studies of the PROG-IMT collaboration
International Journal of Stroke, 2014Co-Authors: Peter Willeit, Simon G. Thompson, Stefan Agewall, Göran Bergström, Horst Bickel, Alberico L. Catapano, Kuo-liong Chien, E. R. De Groot, Jean Philippe Empana, T. EtgenAbstract:Inflammatory markers and extent and progression of early atherosclerosis : Pooled analysis of Individual Participant data from 20 prospective studies of the PROG-IMT collaboration
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multiple imputation for handling systematically missing confounders in meta analysis of Individual Participant data
Statistics in Medicine, 2013Co-Authors: Matthieu Rescherigon, Sanne A E Peters, Ian R. White, Jonathan W Bartlett, Simon G. ThompsonAbstract:A variable is ‘systematically missing’ if it is missing for all Individuals within particular studies in an Individual Participant data meta-analysis. When a systematically missing variable is a potential confounder in observational epidemiology, standard methods either fail to adjust the exposure–disease association for the potential confounder or exclude studies where it is missing. We propose a new approach to adjust for systematically missing confounders based on multiple imputation by chained equations. Systematically missing data are imputed via multilevel regression models that allow for heterogeneity between studies. A simulation study compares various choices of imputation model. An illustration is given using data from eight studies estimating the association between carotid intima media thickness and subsequent risk of cardiovascular events. Results are compared with standard methods and also with an extension of a published method that exploits the relationship between fully adjusted and partially adjusted estimated effects through a multivariate random effects meta-analysis model. We conclude that multiple imputation provides a practicable approach that can handle arbitrary patterns of systematic missingness. Bias is reduced by including sufficient between-study random effects in the imputation model. Copyright © 2013 John Wiley & Sons, Ltd.
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Multiple imputation for handling systematically missing confounders in meta‐analysis of Individual Participant data
Statistics in medicine, 2013Co-Authors: Matthieu Resche-rigon, Sanne A E Peters, Ian R. White, Jonathan W Bartlett, Simon G. ThompsonAbstract:A variable is ‘systematically missing’ if it is missing for all Individuals within particular studies in an Individual Participant data meta-analysis. When a systematically missing variable is a potential confounder in observational epidemiology, standard methods either fail to adjust the exposure–disease association for the potential confounder or exclude studies where it is missing. We propose a new approach to adjust for systematically missing confounders based on multiple imputation by chained equations. Systematically missing data are imputed via multilevel regression models that allow for heterogeneity between studies. A simulation study compares various choices of imputation model. An illustration is given using data from eight studies estimating the association between carotid intima media thickness and subsequent risk of cardiovascular events. Results are compared with standard methods and also with an extension of a published method that exploits the relationship between fully adjusted and partially adjusted estimated effects through a multivariate random effects meta-analysis model. We conclude that multiple imputation provides a practicable approach that can handle arbitrary patterns of systematic missingness. Bias is reduced by including sufficient between-study random effects in the imputation model. Copyright © 2013 John Wiley & Sons, Ltd.
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Meta-analysis of Individual Participant data from observational studies
2013Co-Authors: Simon G. ThompsonAbstract:Meta-analyses of multiple studies, for which Individual Participant data (IPD) are available, are becoming more common. The aim of this session is to update Participants on statistical methods that can be used for such analyses, and the pitfalls to be avoided. The focus will be on observational studies rather than randomised trials. Available software will be discussed. The session will be organised as four 30-minute presentations, each allowing 10 minutes for discussion and questions. Specifically, the presentations will cover the following topics:
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statistical methods for the time to event analysis of Individual Participant data from multiple epidemiological studies
International Journal of Epidemiology, 2010Co-Authors: Simon G. Thompson, Stephen Kaptoge, Angela M Wood, Philip Perry, I H White, J DaneshAbstract:Background Meta-analysis of Individual Participant time-to-event data from multiple prospective epidemiological studies enables detailed investigation of exposure–risk relationships, but involves a number of analytical challenges. Methods This article describes statistical approaches adopted in the Emerging Risk Factors Collaboration, in which primary data from more than 1 million Participants in more than 100 prospective studies have been collated to enable detailed analyses of various risk markers in relation to incident cardiovascular disease outcomes. Results Analyses have been principally based on Cox proportional hazards regression models stratified by sex, undertaken in each study separately. Estimates of exposure–risk relationships, initially unadjusted and then adjusted for several confounders, have been combined over studies using meta-analysis. Methods for assessing the shape of exposure–risk associations and the proportional hazards assumption have been developed. Estimates of interactions have also been combined using meta-analysis, keeping separate within- and between-study information. Regression dilution bias caused by measurement error and within-person variation in exposures and confounders has been addressed through the analysis of repeat measurements to estimate corrected regression coefficients. These methods are exemplified by analysis of plasma fibrinogen and risk of coronary heart disease, and Stata code is made available. Conclusion Increasing numbers of meta-analyses of Individual Participant data from observational data are being conducted to enhance the statistical power and detail of epidemiological studies. The statistical methods developed here can be used to address the needs of such analyses.
Solja T. Nyberg - One of the best experts on this subject based on the ideXlab platform.
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Long working hours and change in body weight: analysis of Individual-Participant data from 19 cohort studies.
International journal of obesity (2005), 2019Co-Authors: Marianna Virtanen, Markus Jokela, Lars Alfredsson, G. David Batty, Tea Lallukka, Linda L. Magnusson Hanson, Jaana Pentti, Solja T. Nyberg, Annalisa Casini, Els ClaysAbstract:Objective: To examine the relation between long working hours and change in body mass index (BMI). Methods: We performed random effects meta-analyses using Individual-Participant data from 19 cohor ...
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Long working hours and depressive symptoms: systematic review and meta-analysis of published studies and unpublished Individual Participant data
Scandinavian journal of work environment & health, 2018Co-Authors: Marianna Virtanen, Markus Jokela, Ida E. H. Madsen, Lars Alfredsson, G. David Batty, Jakob B. Bjorner, Tea Lallukka, Linda L. Magnusson Hanson, Solja T. Nyberg, Marianne BorritzAbstract:Objectives This systematic review and meta-analysis combined published study-level data and unpublished Individual-Participant data with the aim of quantifying the relation between long working hours and the onset of depressive symptoms. Methods We searched PubMed and Embase for published prospective cohort studies and included available cohorts with unpublished Individual-Participant data. We used a random-effects meta-analysis to calculate summary estimates across studies. Results We identified ten published cohort studies and included unpublished Individual-Participant data from 18 studies. In the majority of cohorts, long working hours was defined as working ≥55 hours per week. In multivariable-adjusted meta-analyses of 189 729 Participants from 35 countries [96 275 men, 93 454 women, follow-up ranging from 1-5 years, 21 747 new-onset cases), there was an overall association of 1.14 (95% confidence interval (CI) 1.03-1.25] between long working hours and the onset of depressive symptoms, with significant evidence of heterogeneity (I 2=45.1%, P=0.004). A moderate association between working hours and depressive symptoms was found in Asian countries (1.50, 95% CI 1.13-2.01), a weaker association in Europe (1.11, 95% CI 1.00-1.22), and no association in North America (0.97, 95% CI 0.70-1.34) or Australia (0.95, 95% CI 0.70-1.29). Differences by other characteristics were small. Conclusions This observational evidence suggests a moderate association between long working hours and onset of depressive symptoms in Asia and a small association in Europe.
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job strain and the risk of stroke an Individual Participant data meta analysis
Stroke, 2015Co-Authors: Eleonor I. Fransson, Katriina Heikkilä, Lars Alfredsson, Jakob B. Bjorner, Marianne Borritz, Hermann Burr, Solja T. Nyberg, Nico Dragano, G Geuskens, Marcel GoldbergAbstract:BACKGROUND AND PURPOSE: Psychosocial stress at work has been proposed to be a risk factor for cardiovascular disease. However, its role as a risk factor for stroke is uncertain. METHODS: We conducted an Individual-Participant-data meta-analysis of 196 380 males and females from 14 European cohort studies to investigate the association between job strain, a measure of work-related stress, and incident stroke. RESULTS: In 1.8 million person-years at risk (mean follow-up 9.2 years), 2023 first-time stroke events were recorded. The age- and sex-adjusted hazard ratio for job strain relative to no job strain was 1.24 (95% confidence interval, 1.05;1.47) for ischemic stroke, 1.01 (95% confidence interval, 0.75;1.36) for hemorrhagic stroke, and 1.09 (95% confidence interval, 0.94;1.26) for overall stroke. The association with ischemic stroke was robust to further adjustment for socioeconomic status. CONCLUSION: Job strain may be associated with an increased risk of ischemic stroke, but further research is needed to determine whether interventions targeting job strain would reduce stroke risk beyond existing preventive strategies.
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Long working hours and alcohol use: systematic review and meta-analysis of published studies and unpublished Individual Participant data
BMJ (Clinical research ed.), 2015Co-Authors: Marianna Virtanen, Markus Jokela, Ida E. H. Madsen, Lars Alfredsson, G. David Batty, Jakob B. Bjorner, Tea Lallukka, Solja T. Nyberg, Kirsi Ahola, Marianne BorritzAbstract:Objective: To quantify the association between long working hours and alcohol use. Design: Systematic review and meta-analysis of published studies and unpublished Individual Participant data. Data sources: A systematic search of PubMed and Embase databases in April 2014 for published studies, supplemented with manual searches. Unpublished Individual Participant data were obtained from 27 additional studies. Review methods: The search strategy was designed to retrieve cross sectional and prospective studies of the association between long working hours and alcohol use. Summary estimates were obtained with random effects meta-analysis. Sources of heterogeneity were examined with meta-regression. Results: Cross sectional analysis was based on 61 studies representing 333 693 Participants from 14 countries. Prospective analysis was based on 20 studies representing 100 602 Participants from nine countries. The pooled maximum adjusted odds ratio for the association between long working hours and alcohol use was 1.11 (95% confidence interval 1.05 to 1.18) in the cross sectional analysis of published and unpublished data. Odds ratio of new onset risky alcohol use was 1.12 (1.04 to 1.20) in the analysis of prospective published and unpublished data. In the 18 studies with Individual Participant data it was possible to assess the European Union Working Time Directive, which recommends an upper limit of 48 hours a week. Odds ratios of new onset risky alcohol use for those working 49-54 hours and ≥55 hours a week were 1.13 (1.02 to 1.26; adjusted difference in incidence 0.8 percentage points) and 1.12 (1.01 to 1.25; adjusted difference in incidence 0.7 percentage points), respectively, compared with working standard 35-40 hours (incidence of new onset risky alcohol use 6.2%). There was no difference in these associations between men and women or by age or socioeconomic groups, geographical regions, sample type (population based v occupational cohort), prevalence of risky alcohol use in the cohort, or sample attrition rate. Conclusions: Individuals whose working hours exceed standard recommendations are more likely to increase their alcohol use to levels that pose a health risk.
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Job strain and COPD exacerbations: an Individual-Participant meta-analysis
The European respiratory journal, 2014Co-Authors: Katriina Heikkilä, Ida E. H. Madsen, Lars Alfredsson, Jakob B. Bjorner, Marianne Borritz, Hermann Burr, Solja T. Nyberg, Eleonor I. Fransson, Kirsi Ahola, Anders KnutssonAbstract:To the Editor:Chronic obstructive pulmonary disease (COPD) is a major cause of mortality and disability worldwide (1). The clinical course of COPD is characterised by exacerbations, which can be minor and manageable at home or in primary care, or severe, leading to hospitalisation or even death. Known causes of exacerbations include tobacco smoke, air pollution, dusts and fumes, and respiratory infections (1, 2). One less well understood risk factor is stress, which could plausibly lead to COPD exacerbations as it can trigger inflammation (3, 4) and is associated with increased smoking (5), which are both implicated in COPD pathology (2). Work is an important source of stress in the age groups in which COPD is typically diagnosed (1, 6). However, we are not aware of previous investigations of work-related stress and the risk of COPD exacerbations.In this study, we examined the associations between job strain (the most widely studied conceptualisation of work-related stress) and severe COPD exacerbations using Individual-level data from 10 prospective cohort studies from the Individual Participant Data Meta-analysis in Working Populations (IPD-Work) Consortium (7). Job strain is defined as a combination of high demands (excessive amounts of work) and low control (having little influence on what tasks to.
Marianne Borritz - One of the best experts on this subject based on the ideXlab platform.
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Long working hours and depressive symptoms: systematic review and meta-analysis of published studies and unpublished Individual Participant data
Scandinavian journal of work environment & health, 2018Co-Authors: Marianna Virtanen, Markus Jokela, Ida E. H. Madsen, Lars Alfredsson, G. David Batty, Jakob B. Bjorner, Tea Lallukka, Linda L. Magnusson Hanson, Solja T. Nyberg, Marianne BorritzAbstract:Objectives This systematic review and meta-analysis combined published study-level data and unpublished Individual-Participant data with the aim of quantifying the relation between long working hours and the onset of depressive symptoms. Methods We searched PubMed and Embase for published prospective cohort studies and included available cohorts with unpublished Individual-Participant data. We used a random-effects meta-analysis to calculate summary estimates across studies. Results We identified ten published cohort studies and included unpublished Individual-Participant data from 18 studies. In the majority of cohorts, long working hours was defined as working ≥55 hours per week. In multivariable-adjusted meta-analyses of 189 729 Participants from 35 countries [96 275 men, 93 454 women, follow-up ranging from 1-5 years, 21 747 new-onset cases), there was an overall association of 1.14 (95% confidence interval (CI) 1.03-1.25] between long working hours and the onset of depressive symptoms, with significant evidence of heterogeneity (I 2=45.1%, P=0.004). A moderate association between working hours and depressive symptoms was found in Asian countries (1.50, 95% CI 1.13-2.01), a weaker association in Europe (1.11, 95% CI 1.00-1.22), and no association in North America (0.97, 95% CI 0.70-1.34) or Australia (0.95, 95% CI 0.70-1.29). Differences by other characteristics were small. Conclusions This observational evidence suggests a moderate association between long working hours and onset of depressive symptoms in Asia and a small association in Europe.
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Job insecurity and risk of diabetes: a meta-analysis of Individual Participant data
CMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne, 2016Co-Authors: Jane E. Ferrie, Marianna Virtanen, Markus Jokela, Ida E. H. Madsen, Katriina Heikkilä, Lars Alfredsson, G. David Batty, Jakob B. Bjorner, Marianne Borritz, Hermann BurrAbstract:BACKGROUND: Job insecurity has been associated with certain health outcomes. We examined the role of job insecurity as a risk factor for incident diabetes.METHODS: We used Individual Participant da ...
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job strain and the risk of stroke an Individual Participant data meta analysis
Stroke, 2015Co-Authors: Eleonor I. Fransson, Katriina Heikkilä, Lars Alfredsson, Jakob B. Bjorner, Marianne Borritz, Hermann Burr, Solja T. Nyberg, Nico Dragano, G Geuskens, Marcel GoldbergAbstract:BACKGROUND AND PURPOSE: Psychosocial stress at work has been proposed to be a risk factor for cardiovascular disease. However, its role as a risk factor for stroke is uncertain. METHODS: We conducted an Individual-Participant-data meta-analysis of 196 380 males and females from 14 European cohort studies to investigate the association between job strain, a measure of work-related stress, and incident stroke. RESULTS: In 1.8 million person-years at risk (mean follow-up 9.2 years), 2023 first-time stroke events were recorded. The age- and sex-adjusted hazard ratio for job strain relative to no job strain was 1.24 (95% confidence interval, 1.05;1.47) for ischemic stroke, 1.01 (95% confidence interval, 0.75;1.36) for hemorrhagic stroke, and 1.09 (95% confidence interval, 0.94;1.26) for overall stroke. The association with ischemic stroke was robust to further adjustment for socioeconomic status. CONCLUSION: Job strain may be associated with an increased risk of ischemic stroke, but further research is needed to determine whether interventions targeting job strain would reduce stroke risk beyond existing preventive strategies.
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Long working hours and alcohol use: systematic review and meta-analysis of published studies and unpublished Individual Participant data
BMJ (Clinical research ed.), 2015Co-Authors: Marianna Virtanen, Markus Jokela, Ida E. H. Madsen, Lars Alfredsson, G. David Batty, Jakob B. Bjorner, Tea Lallukka, Solja T. Nyberg, Kirsi Ahola, Marianne BorritzAbstract:Objective: To quantify the association between long working hours and alcohol use. Design: Systematic review and meta-analysis of published studies and unpublished Individual Participant data. Data sources: A systematic search of PubMed and Embase databases in April 2014 for published studies, supplemented with manual searches. Unpublished Individual Participant data were obtained from 27 additional studies. Review methods: The search strategy was designed to retrieve cross sectional and prospective studies of the association between long working hours and alcohol use. Summary estimates were obtained with random effects meta-analysis. Sources of heterogeneity were examined with meta-regression. Results: Cross sectional analysis was based on 61 studies representing 333 693 Participants from 14 countries. Prospective analysis was based on 20 studies representing 100 602 Participants from nine countries. The pooled maximum adjusted odds ratio for the association between long working hours and alcohol use was 1.11 (95% confidence interval 1.05 to 1.18) in the cross sectional analysis of published and unpublished data. Odds ratio of new onset risky alcohol use was 1.12 (1.04 to 1.20) in the analysis of prospective published and unpublished data. In the 18 studies with Individual Participant data it was possible to assess the European Union Working Time Directive, which recommends an upper limit of 48 hours a week. Odds ratios of new onset risky alcohol use for those working 49-54 hours and ≥55 hours a week were 1.13 (1.02 to 1.26; adjusted difference in incidence 0.8 percentage points) and 1.12 (1.01 to 1.25; adjusted difference in incidence 0.7 percentage points), respectively, compared with working standard 35-40 hours (incidence of new onset risky alcohol use 6.2%). There was no difference in these associations between men and women or by age or socioeconomic groups, geographical regions, sample type (population based v occupational cohort), prevalence of risky alcohol use in the cohort, or sample attrition rate. Conclusions: Individuals whose working hours exceed standard recommendations are more likely to increase their alcohol use to levels that pose a health risk.
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Job strain and COPD exacerbations: an Individual-Participant meta-analysis
The European respiratory journal, 2014Co-Authors: Katriina Heikkilä, Ida E. H. Madsen, Lars Alfredsson, Jakob B. Bjorner, Marianne Borritz, Hermann Burr, Solja T. Nyberg, Eleonor I. Fransson, Kirsi Ahola, Anders KnutssonAbstract:To the Editor:Chronic obstructive pulmonary disease (COPD) is a major cause of mortality and disability worldwide (1). The clinical course of COPD is characterised by exacerbations, which can be minor and manageable at home or in primary care, or severe, leading to hospitalisation or even death. Known causes of exacerbations include tobacco smoke, air pollution, dusts and fumes, and respiratory infections (1, 2). One less well understood risk factor is stress, which could plausibly lead to COPD exacerbations as it can trigger inflammation (3, 4) and is associated with increased smoking (5), which are both implicated in COPD pathology (2). Work is an important source of stress in the age groups in which COPD is typically diagnosed (1, 6). However, we are not aware of previous investigations of work-related stress and the risk of COPD exacerbations.In this study, we examined the associations between job strain (the most widely studied conceptualisation of work-related stress) and severe COPD exacerbations using Individual-level data from 10 prospective cohort studies from the Individual Participant Data Meta-analysis in Working Populations (IPD-Work) Consortium (7). Job strain is defined as a combination of high demands (excessive amounts of work) and low control (having little influence on what tasks to.
Glenn I. Roisman - One of the best experts on this subject based on the ideXlab platform.
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An Individual Participant data (IPD) meta-analysis on the attachment network to multiple caregivers
2020Co-Authors: Or Dagan, Glenn I. Roisman, Marinus H. Van Ijzendoorn, Carlo Schuengel, Mirjam Oosterman, Pasco Fearon, Marije Verhage, Kristin Bernard, Robbie Duschinsky, Marian Bakermans-kranenburgAbstract:Since the seminal 1992 paper by van IJzendoorn, Sagi, and Lambermon, putting forward the “The multiple caretaker paradox”, relatively little attention has been given to the potential joint effects of the role early attachment network to mother and father play in development. Recently, Dagan and Sagi-Schwartz (2018) have published a paper that attempts to revive this unsettled issue, calling for research on the subject and offering a framework for posing attachment network hypotheses. This Collaboration for Attachment Research Synthesis project attempts to use an Individual Participant Data meta-analyses to test the hypotheses put forward in Dagan and Sagi-Schwartz (2018). Specifically, we test (a) whether the number of secure attachments (0,1, or 2) matter in predicting a range of developmental outcomes, and (b) whether the quality of attachment relationship with one parent contributes more than the other to these outcomes.
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The Collaboration on Attachment Transmission Synthesis (CATS): A Move to the Level of Individual-Participant-Data Meta-Analysis:
Current directions in psychological science, 2020Co-Authors: M.l. Verhage, Glenn I. Roisman, Marinus H. Van Ijzendoorn, R. M. Pasco Fearon, Carlo Schuengel, Marian J. Bakermans-kranenburg, Sheri Madigan, Robbie Duschinsky, Mirjam OostermanAbstract:Generations of researchers have tested and used attachment theory to understand children’s development. To bring coherence to the expansive set of findings from small-sample studies, the field early on adopted meta-analysis. Nevertheless, gaps in understanding intergenerational transmission of Individual differences in attachment continue to exist. We discuss how attachment research has been addressing these challenges by collaborating in formulating questions and pooling data and resources for Individual-Participant-data meta-analyses. The collaborative model means that sharing hard-won and valuable data goes hand in hand with directly and intensively interacting with a large community of researchers in the initiation phase of research, deliberating on and critically reviewing new hypotheses, and providing access to a large, carefully curated pool of data for testing these hypotheses. Challenges in pooling data are also discussed.
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Meta‐analysis and Individual Participant Data Synthesis in Child Development: Introduction to the Special Section
Child Development, 2018Co-Authors: Glenn I. Roisman, Marinus H Van IjzendoornAbstract:This paper serves as an Introduction by the co-editors to a Special Section of Child Development entitled "Meta-analysis and Individual Participant Data Synthesis in Child Development." First, the co-editors emphasize that the work contained in the Special Section was selected to highlight the value of meta-analysis not only for synthesizing study-level published and unpublished data but also as regards its ability to support programmatic, replicable, and cumulative developmental science. Second, the co-editors identify some of the cross-cutting themes of the papers featured in the Special Section, including the value of meta-analysis for summarizing and interrogating the full range of developmental science and in potentially transforming conventional wisdom in given domains along with the importance of recent innovations for improving standard meta-analytic practice-particularly in the context of developmental questions. Emphasized especially are contributions to the Special Section that extend classic meta-analysis to Individual Participant data synthesis. [ABSTRACT FROM AUTHOR]
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Meta-analysis and Individual Participant Data Synthesis in Child Development: Introduction to the Special Section.
Child development, 2018Co-Authors: Glenn I. Roisman, Marinus H. Van IjzendoornAbstract:This paper serves as an Introduction by the co-editors to a Special Section of Child Development entitled "Meta-analysis and Individual Participant Data Synthesis in Child Development." First, the co-editors emphasize that the work contained in the Special Section was selected to highlight the value of meta-analysis not only for synthesizing study-level published and unpublished data but also as regards its ability to support programmatic, replicable, and cumulative developmental science. Second, the co-editors identify some of the cross-cutting themes of the papers featured in the Special Section, including the value of meta-analysis for summarizing and interrogating the full range of developmental science and in potentially transforming conventional wisdom in given domains along with the importance of recent innovations for improving standard meta-analytic practice-particularly in the context of developmental questions. Emphasized especially are contributions to the Special Section that extend classic meta-analysis to Individual Participant data synthesis.
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Examining Ecological Constraints on the Intergenerational Transmission of Attachment Via Individual Participant Data Meta-analysis.
Child development, 2018Co-Authors: M.l. Verhage, Glenn I. Roisman, Marinus H. Van Ijzendoorn, R. M. Pasco Fearon, Carlo Schuengel, Marian J. Bakermans-kranenburg, Sheri Madigan, Mirjam Oosterman, Kazuko Y. Behrens, Maria S. WongAbstract:Parents' attachment representations and child-parent attachment have been shown to be associated, but these associations vary across populations (Verhage et al., 2016). The current study examined whether ecological factors may explain variability in the strength of intergenerational transmission of attachment, using Individual Participant data (IPD) meta-analysis. Analyses on 4,396 parent-child dyads (58 studies, child age 11-96 months) revealed a combined effect size of r = .29. IPD meta-analyses revealed that effect sizes for the transmission of autonomous-secure representations to secure attachments were weaker under risk conditions and weaker in adolescent parent-child dyads, whereas transmission was stronger for older children. Findings support the ecological constraints hypothesis on attachment transmission. Implications for attachment theory and the use of IPD meta-analysis are discussed.