Statistics and Econometrics Methodology

Lead Research Organisation: University of Bristol

Abstract

(around 200 words – max. 1,500 characters including spaces) We are trying to develop statistical methods to help medical researchers in their search for causes of disease. A lot of medical research involves gathering data from people and using statistical models to tell us which factors cause later health. For example, we might ask a group of people about their diet as children, as teenagers and as adults, and use this to try to tell us whether poor diet causes cancer in later life.
There are several problems with these studies that make it hard to draw conclusions. One is that people agreeing to be in a study are often different from people who don’t agree. Another is that people tend to drop out of a study over time – and again the people who drop out are often different from the people who stay. A third problem is that people change as they go through life – and we might want to know whether the cause of a disease happens at birth, or during childhood, or whether there are chances to prevent the disease even in adults.
Statistical models may not give the right answers if any of these problems occur – and this could mean that the wrong health advice is given, or the wrong treatments developed. We aim to develop methods that can overcome these real-life problems, and help medical researchers to be more confident in their conclusions about causes of disease.

Technical Summary

(around 400 words – max. 3,500 characters including spaces) Aim: The aim of this programme is to develop methods for causal inference that are robust to missing data and can investigate change over time, in order to draw unbiased conclusions about realistic problems, using complex observational data.
Importance: Causal inference methods - in particular instrumental variable (IV) and Mendelian randomization (MR) methods - are now straightforward to implement, can be used with summary data, and are widely used by epidemiologists and medical researchers. However, the majority of real-world clinical research settings are more complex than the standard methods allow: data are missing; samples are selected; exposures and outcomes evolve jointly over time; and data from a wide variety of sources need to be integrated. Methods for causal inference, including IV, may be biased by missing data, including individuals missing due to sample selection. Standard IV methods are not able to address complex (and possibly time-varying) relationships between exposure, covariates and outcome. Multiple studies may provide information about the same causal effect, or about different paths in a network of causal effects, and we need to develop better methods to integrate evidence from different study types in order to draw causal inferences.
Objectives:
1. Develop methods to minimise bias due to missing data
2. Develop methods to model complex exposures and outcomes
3. Develop IV methods to examine causal influences of multiple exposures
4. Integrate evidence to improve causal models
Research plans: Part 1 of this programme will develop methods to use study information, and information external to the study, to infer the missing data structure, to inform all types of causal analyses. We will then focus on methods to maximise the robustness of IV methods to different types of missing data. We will pay particular attention to two cases: two-sample IV (using individual or summary data), and the investigation of disease prognosis. Part 2 will extend current methods for modelling trajectories and variability of exposures and outcomes. We will then focus on overcoming some of the current limitations of IV methods, by using structural equation modelling (SEM) and multivariable IV to examine impacts of time-varying exposures. Finally, we will maximise the use of all research data by extending methods to combine and use external information to inform causal models and sensitivity analyses.

Publications

10 25 50
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Albarqouni LN (2017) Indirect evidence of reporting biases was found in a survey of medical research studies. in Journal of clinical epidemiology

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Allen R (2015) More reliable inference for the dissimilarity index of segregation. in The econometrics journal

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Bell JA (2018) Associations of Body Mass and Fat Indexes With Cardiometabolic Traits. in Journal of the American College of Cardiology

 
Description Biostatistics Fellowship
Amount £352,864 (GBP)
Funding ID MR/L011824/1 
Organisation Medical Research Council (MRC) 
Sector Academic/University
Country United Kingdom
Start 03/2014 
End 02/2018
 
Description Career Development Award
Amount £783,730 (GBP)
Funding ID MR/M020894/1 
Organisation Medical Research Council (MRC) 
Sector Academic/University
Country United Kingdom
Start 09/2015 
End 08/2020
 
Description ESRC Future Research Leaders Fellowship
Amount £279,636 (GBP)
Funding ID ES/N000757/1 
Organisation Economic and Social Research Council 
Sector Public
Country United Kingdom
Start 01/2016 
End 12/2018
 
Description Epigenetics: Environment, Embodiment and Equality (E4)
Amount £834,324 (GBP)
Funding ID ES/N000498/1 
Organisation Economic and Social Research Council 
Sector Public
Country United Kingdom
Start 10/2015 
End 09/2018
 
Description INTERpreting epigenetic signatures in Studies of Early Life Adversity (InterStELA)
Amount £249,391 (GBP)
Funding ID ES/N0003821/1 
Organisation Economic and Social Research Council 
Sector Public
Country United Kingdom
Start 09/2015 
End 08/2017
 
Description MRC Methodology bolt-on to the Efficient Study Design Call
Amount £119,671 (GBP)
Funding ID MR/N01006X/1 
Organisation Medical Research Council (MRC) 
Sector Academic/University
Country United Kingdom
Start 08/2015 
End 01/2017
 
Description NIHR-HTA Board. Efficient Study Design call
Amount £401,502 (GBP)
Organisation National Institute for Health Research 
Department NIHR Clinical Research Network (CRN)
Sector Public
Country United Kingdom
Start 03/2017 
End 02/2019
 
Description NIHR-HTA Board. Efficient Study Design call.
Amount £266,679 (GBP)
Funding ID 14/49/94 
Organisation National Institute for Health Research 
Department NIHR Biomedical Research Centre
Sector Academic/University
Country United Kingdom
Start 09/2015 
End 02/2017
 
Description Network of individuals interested in statistical methods for examining consistency of findings in meta-analysis 
Organisation University of Cambridge
Department MRC Biostatistics Unit
Country United Kingdom 
Sector Public 
PI Contribution Ad hoc discussions, telephone calls, written correspondence
Collaborator Contribution Equal efforts
Impact Veroniki AA, Jackson D, Viechtbauer W, Bender R, Bowden J, Knapp G, Kuss O, Higgins JPT, Langan D, Salanti G. Methods to estimate heterogeneity variance and its uncertainty in meta-analysis. Research Synthesis Methods 2015; 7: 55-79; doi: 10.1002/jrsm.1164. Jackson D, Law M, Barrett JK, Turner R, Higgins JPT, Salanti G, White IR. Extending DerSimonian and Laird's methodology to perform network meta-analyses with random inconsistency effects. Statistics in Medicine 2015; 35: 819-39; doi: 10.1002/sim.6752. Rhodes KM, Turner RM, White IR, Jackson D, Spiegelhalter DJ, Higgins JPT. Implementing informative priors for heterogeneity in meta-analysis using meta-regression and pseudo data. Statistics in Medicine 2016; 35: 5495-5511; doi: 10.1002/sim.7090. Langan D, Higgins JPT, Simmonds M. Comparative performance of heterogeneity variance estimators in meta-analysis: a review of simulation studies. Research Synthesis Methods. Published online 6 April 2016; doi: 10.1002/jrsm.1198. Mawdsley D, Higgins JPT, Sutton AJ, Abrams KR. Accounting for heterogeneity in meta-analysis using a multiplicative model: an empirical study. Research Synthesis Methods. Published online 3 Jun 2016; doi: 10.1002/jrsm.1216. Borenstein M, Higgins JPT, Rothstein HR, Hedges LV. Basics of meta-analysis: I2 is not an absolute measure of heterogeneity. Research Synthesis Methods. Published online 6 Jan 2017; doi: 10.1002/jrsm.1230. Rice K, Higgins JPT, Lumley T. A re-evaluation of fixed-effect(s) meta-analysis. Journal of the Royal Statistical Society Series A (in press).
Start Year 2008
 
Description Network of individuals interested in statistical methods for examining consistency of findings in meta-analysis 
Organisation University of Ioannina
Country Greece 
Sector Academic/University 
PI Contribution Ad hoc discussions, telephone calls, written correspondence
Collaborator Contribution Equal efforts
Impact Veroniki AA, Jackson D, Viechtbauer W, Bender R, Bowden J, Knapp G, Kuss O, Higgins JPT, Langan D, Salanti G. Methods to estimate heterogeneity variance and its uncertainty in meta-analysis. Research Synthesis Methods 2015; 7: 55-79; doi: 10.1002/jrsm.1164. Jackson D, Law M, Barrett JK, Turner R, Higgins JPT, Salanti G, White IR. Extending DerSimonian and Laird's methodology to perform network meta-analyses with random inconsistency effects. Statistics in Medicine 2015; 35: 819-39; doi: 10.1002/sim.6752. Rhodes KM, Turner RM, White IR, Jackson D, Spiegelhalter DJ, Higgins JPT. Implementing informative priors for heterogeneity in meta-analysis using meta-regression and pseudo data. Statistics in Medicine 2016; 35: 5495-5511; doi: 10.1002/sim.7090. Langan D, Higgins JPT, Simmonds M. Comparative performance of heterogeneity variance estimators in meta-analysis: a review of simulation studies. Research Synthesis Methods. Published online 6 April 2016; doi: 10.1002/jrsm.1198. Mawdsley D, Higgins JPT, Sutton AJ, Abrams KR. Accounting for heterogeneity in meta-analysis using a multiplicative model: an empirical study. Research Synthesis Methods. Published online 3 Jun 2016; doi: 10.1002/jrsm.1216. Borenstein M, Higgins JPT, Rothstein HR, Hedges LV. Basics of meta-analysis: I2 is not an absolute measure of heterogeneity. Research Synthesis Methods. Published online 6 Jan 2017; doi: 10.1002/jrsm.1230. Rice K, Higgins JPT, Lumley T. A re-evaluation of fixed-effect(s) meta-analysis. Journal of the Royal Statistical Society Series A (in press).
Start Year 2008
 
Description Network of individuals interested in statistical methods for examining consistency of findings in meta-analysis 
Organisation University of Leicester
Country United Kingdom 
Sector Academic/University 
PI Contribution Ad hoc discussions, telephone calls, written correspondence
Collaborator Contribution Equal efforts
Impact Veroniki AA, Jackson D, Viechtbauer W, Bender R, Bowden J, Knapp G, Kuss O, Higgins JPT, Langan D, Salanti G. Methods to estimate heterogeneity variance and its uncertainty in meta-analysis. Research Synthesis Methods 2015; 7: 55-79; doi: 10.1002/jrsm.1164. Jackson D, Law M, Barrett JK, Turner R, Higgins JPT, Salanti G, White IR. Extending DerSimonian and Laird's methodology to perform network meta-analyses with random inconsistency effects. Statistics in Medicine 2015; 35: 819-39; doi: 10.1002/sim.6752. Rhodes KM, Turner RM, White IR, Jackson D, Spiegelhalter DJ, Higgins JPT. Implementing informative priors for heterogeneity in meta-analysis using meta-regression and pseudo data. Statistics in Medicine 2016; 35: 5495-5511; doi: 10.1002/sim.7090. Langan D, Higgins JPT, Simmonds M. Comparative performance of heterogeneity variance estimators in meta-analysis: a review of simulation studies. Research Synthesis Methods. Published online 6 April 2016; doi: 10.1002/jrsm.1198. Mawdsley D, Higgins JPT, Sutton AJ, Abrams KR. Accounting for heterogeneity in meta-analysis using a multiplicative model: an empirical study. Research Synthesis Methods. Published online 3 Jun 2016; doi: 10.1002/jrsm.1216. Borenstein M, Higgins JPT, Rothstein HR, Hedges LV. Basics of meta-analysis: I2 is not an absolute measure of heterogeneity. Research Synthesis Methods. Published online 6 Jan 2017; doi: 10.1002/jrsm.1230. Rice K, Higgins JPT, Lumley T. A re-evaluation of fixed-effect(s) meta-analysis. Journal of the Royal Statistical Society Series A (in press).
Start Year 2008
 
Description Network of individuals interested in statistical methods for examining consistency of findings in meta-analysis 
Organisation University of Washington
Department School of Public Health
Country United States 
Sector Academic/University 
PI Contribution Ad hoc discussions, telephone calls, written correspondence
Collaborator Contribution Equal efforts
Impact Veroniki AA, Jackson D, Viechtbauer W, Bender R, Bowden J, Knapp G, Kuss O, Higgins JPT, Langan D, Salanti G. Methods to estimate heterogeneity variance and its uncertainty in meta-analysis. Research Synthesis Methods 2015; 7: 55-79; doi: 10.1002/jrsm.1164. Jackson D, Law M, Barrett JK, Turner R, Higgins JPT, Salanti G, White IR. Extending DerSimonian and Laird's methodology to perform network meta-analyses with random inconsistency effects. Statistics in Medicine 2015; 35: 819-39; doi: 10.1002/sim.6752. Rhodes KM, Turner RM, White IR, Jackson D, Spiegelhalter DJ, Higgins JPT. Implementing informative priors for heterogeneity in meta-analysis using meta-regression and pseudo data. Statistics in Medicine 2016; 35: 5495-5511; doi: 10.1002/sim.7090. Langan D, Higgins JPT, Simmonds M. Comparative performance of heterogeneity variance estimators in meta-analysis: a review of simulation studies. Research Synthesis Methods. Published online 6 April 2016; doi: 10.1002/jrsm.1198. Mawdsley D, Higgins JPT, Sutton AJ, Abrams KR. Accounting for heterogeneity in meta-analysis using a multiplicative model: an empirical study. Research Synthesis Methods. Published online 3 Jun 2016; doi: 10.1002/jrsm.1216. Borenstein M, Higgins JPT, Rothstein HR, Hedges LV. Basics of meta-analysis: I2 is not an absolute measure of heterogeneity. Research Synthesis Methods. Published online 6 Jan 2017; doi: 10.1002/jrsm.1230. Rice K, Higgins JPT, Lumley T. A re-evaluation of fixed-effect(s) meta-analysis. Journal of the Royal Statistical Society Series A (in press).
Start Year 2008
 
Description Network of individuals interested in statistical methods for examining consistency of findings in meta-analysis 
Organisation University of York
Department Department of Biology
Country United Kingdom 
Sector Academic/University 
PI Contribution Ad hoc discussions, telephone calls, written correspondence
Collaborator Contribution Equal efforts
Impact Veroniki AA, Jackson D, Viechtbauer W, Bender R, Bowden J, Knapp G, Kuss O, Higgins JPT, Langan D, Salanti G. Methods to estimate heterogeneity variance and its uncertainty in meta-analysis. Research Synthesis Methods 2015; 7: 55-79; doi: 10.1002/jrsm.1164. Jackson D, Law M, Barrett JK, Turner R, Higgins JPT, Salanti G, White IR. Extending DerSimonian and Laird's methodology to perform network meta-analyses with random inconsistency effects. Statistics in Medicine 2015; 35: 819-39; doi: 10.1002/sim.6752. Rhodes KM, Turner RM, White IR, Jackson D, Spiegelhalter DJ, Higgins JPT. Implementing informative priors for heterogeneity in meta-analysis using meta-regression and pseudo data. Statistics in Medicine 2016; 35: 5495-5511; doi: 10.1002/sim.7090. Langan D, Higgins JPT, Simmonds M. Comparative performance of heterogeneity variance estimators in meta-analysis: a review of simulation studies. Research Synthesis Methods. Published online 6 April 2016; doi: 10.1002/jrsm.1198. Mawdsley D, Higgins JPT, Sutton AJ, Abrams KR. Accounting for heterogeneity in meta-analysis using a multiplicative model: an empirical study. Research Synthesis Methods. Published online 3 Jun 2016; doi: 10.1002/jrsm.1216. Borenstein M, Higgins JPT, Rothstein HR, Hedges LV. Basics of meta-analysis: I2 is not an absolute measure of heterogeneity. Research Synthesis Methods. Published online 6 Jan 2017; doi: 10.1002/jrsm.1230. Rice K, Higgins JPT, Lumley T. A re-evaluation of fixed-effect(s) meta-analysis. Journal of the Royal Statistical Society Series A (in press).
Start Year 2008
 
Description Risk of bias tools for systematic reviews 
Organisation Harvard University
Country United States 
Sector Academic/University 
PI Contribution Leadership of the collaborative project from Bristol
Collaborator Contribution Contributions to writing, teleconferences, face-to-face meetings
Impact Sterne JAC, Hernán MA, Reeves BC, Savovic J, Berkman ND, Viswanathan M, Henry D, Altman DG, Ansari MT, Boutron I, Carpenter JR, Chan AW, Churchill R, Deeks JJ, Hróbjartsson A, Kirkham J, Jüni P, Loke YK, Pigott TD, Ramsay CR, Regidor D, Rothstein HR, Sandhu L, Santaguida PL, Schünemann HJ, Shea B, Shrier I, Tugwell P, Turner L, Valentine JC, Waddington H, Waters E, Wells GA, Whiting PF, Higgins JPT. ROBINS-I: a tool for assessing risk of bias in non-randomized studies of interventions. BMJ 2016; 355; i4919; doi: 10.1136/bmj.i4919. Whiting P, Savovic J, Higgins JPT, Caldwell DM, Reeves BC, Shea B, Davies P, Kleijnen J, Churchill R, the ROBIS group. ROBIS: a new tool to assess the risk of bias in systematic reviews. Journal of Clinical Epidemiology 2016; 69: 225-34; doi: 10.1016/j.jclinepi.2015.06.005.
Start Year 2011
 
Description Risk of bias tools for systematic reviews 
Organisation McGill University
Country Canada 
Sector Academic/University 
PI Contribution Leadership of the collaborative project from Bristol
Collaborator Contribution Contributions to writing, teleconferences, face-to-face meetings
Impact Sterne JAC, Hernán MA, Reeves BC, Savovic J, Berkman ND, Viswanathan M, Henry D, Altman DG, Ansari MT, Boutron I, Carpenter JR, Chan AW, Churchill R, Deeks JJ, Hróbjartsson A, Kirkham J, Jüni P, Loke YK, Pigott TD, Ramsay CR, Regidor D, Rothstein HR, Sandhu L, Santaguida PL, Schünemann HJ, Shea B, Shrier I, Tugwell P, Turner L, Valentine JC, Waddington H, Waters E, Wells GA, Whiting PF, Higgins JPT. ROBINS-I: a tool for assessing risk of bias in non-randomized studies of interventions. BMJ 2016; 355; i4919; doi: 10.1136/bmj.i4919. Whiting P, Savovic J, Higgins JPT, Caldwell DM, Reeves BC, Shea B, Davies P, Kleijnen J, Churchill R, the ROBIS group. ROBIS: a new tool to assess the risk of bias in systematic reviews. Journal of Clinical Epidemiology 2016; 69: 225-34; doi: 10.1016/j.jclinepi.2015.06.005.
Start Year 2011
 
Description Risk of bias tools for systematic reviews 
Organisation Medical Research Council (MRC)
Country United Kingdom 
Sector Academic/University 
PI Contribution Leadership of the collaborative project from Bristol
Collaborator Contribution Contributions to writing, teleconferences, face-to-face meetings
Impact Sterne JAC, Hernán MA, Reeves BC, Savovic J, Berkman ND, Viswanathan M, Henry D, Altman DG, Ansari MT, Boutron I, Carpenter JR, Chan AW, Churchill R, Deeks JJ, Hróbjartsson A, Kirkham J, Jüni P, Loke YK, Pigott TD, Ramsay CR, Regidor D, Rothstein HR, Sandhu L, Santaguida PL, Schünemann HJ, Shea B, Shrier I, Tugwell P, Turner L, Valentine JC, Waddington H, Waters E, Wells GA, Whiting PF, Higgins JPT. ROBINS-I: a tool for assessing risk of bias in non-randomized studies of interventions. BMJ 2016; 355; i4919; doi: 10.1136/bmj.i4919. Whiting P, Savovic J, Higgins JPT, Caldwell DM, Reeves BC, Shea B, Davies P, Kleijnen J, Churchill R, the ROBIS group. ROBIS: a new tool to assess the risk of bias in systematic reviews. Journal of Clinical Epidemiology 2016; 69: 225-34; doi: 10.1016/j.jclinepi.2015.06.005.
Start Year 2011
 
Description Risk of bias tools for systematic reviews 
Organisation RTI International
Country United States 
Sector Charity/Non Profit 
PI Contribution Leadership of the collaborative project from Bristol
Collaborator Contribution Contributions to writing, teleconferences, face-to-face meetings
Impact Sterne JAC, Hernán MA, Reeves BC, Savovic J, Berkman ND, Viswanathan M, Henry D, Altman DG, Ansari MT, Boutron I, Carpenter JR, Chan AW, Churchill R, Deeks JJ, Hróbjartsson A, Kirkham J, Jüni P, Loke YK, Pigott TD, Ramsay CR, Regidor D, Rothstein HR, Sandhu L, Santaguida PL, Schünemann HJ, Shea B, Shrier I, Tugwell P, Turner L, Valentine JC, Waddington H, Waters E, Wells GA, Whiting PF, Higgins JPT. ROBINS-I: a tool for assessing risk of bias in non-randomized studies of interventions. BMJ 2016; 355; i4919; doi: 10.1136/bmj.i4919. Whiting P, Savovic J, Higgins JPT, Caldwell DM, Reeves BC, Shea B, Davies P, Kleijnen J, Churchill R, the ROBIS group. ROBIS: a new tool to assess the risk of bias in systematic reviews. Journal of Clinical Epidemiology 2016; 69: 225-34; doi: 10.1016/j.jclinepi.2015.06.005.
Start Year 2011
 
Description Risk of bias tools for systematic reviews 
Organisation The Cochrane Collaboration
Country Global 
Sector Charity/Non Profit 
PI Contribution Leadership of the collaborative project from Bristol
Collaborator Contribution Contributions to writing, teleconferences, face-to-face meetings
Impact Sterne JAC, Hernán MA, Reeves BC, Savovic J, Berkman ND, Viswanathan M, Henry D, Altman DG, Ansari MT, Boutron I, Carpenter JR, Chan AW, Churchill R, Deeks JJ, Hróbjartsson A, Kirkham J, Jüni P, Loke YK, Pigott TD, Ramsay CR, Regidor D, Rothstein HR, Sandhu L, Santaguida PL, Schünemann HJ, Shea B, Shrier I, Tugwell P, Turner L, Valentine JC, Waddington H, Waters E, Wells GA, Whiting PF, Higgins JPT. ROBINS-I: a tool for assessing risk of bias in non-randomized studies of interventions. BMJ 2016; 355; i4919; doi: 10.1136/bmj.i4919. Whiting P, Savovic J, Higgins JPT, Caldwell DM, Reeves BC, Shea B, Davies P, Kleijnen J, Churchill R, the ROBIS group. ROBIS: a new tool to assess the risk of bias in systematic reviews. Journal of Clinical Epidemiology 2016; 69: 225-34; doi: 10.1016/j.jclinepi.2015.06.005.
Start Year 2011
 
Description Risk of bias tools for systematic reviews 
Organisation University of Ottawa
Country Canada 
Sector Academic/University 
PI Contribution Leadership of the collaborative project from Bristol
Collaborator Contribution Contributions to writing, teleconferences, face-to-face meetings
Impact Sterne JAC, Hernán MA, Reeves BC, Savovic J, Berkman ND, Viswanathan M, Henry D, Altman DG, Ansari MT, Boutron I, Carpenter JR, Chan AW, Churchill R, Deeks JJ, Hróbjartsson A, Kirkham J, Jüni P, Loke YK, Pigott TD, Ramsay CR, Regidor D, Rothstein HR, Sandhu L, Santaguida PL, Schünemann HJ, Shea B, Shrier I, Tugwell P, Turner L, Valentine JC, Waddington H, Waters E, Wells GA, Whiting PF, Higgins JPT. ROBINS-I: a tool for assessing risk of bias in non-randomized studies of interventions. BMJ 2016; 355; i4919; doi: 10.1136/bmj.i4919. Whiting P, Savovic J, Higgins JPT, Caldwell DM, Reeves BC, Shea B, Davies P, Kleijnen J, Churchill R, the ROBIS group. ROBIS: a new tool to assess the risk of bias in systematic reviews. Journal of Clinical Epidemiology 2016; 69: 225-34; doi: 10.1016/j.jclinepi.2015.06.005.
Start Year 2011
 
Description Risk of bias tools for systematic reviews 
Organisation University of Oxford
Country United Kingdom 
Sector Academic/University 
PI Contribution Leadership of the collaborative project from Bristol
Collaborator Contribution Contributions to writing, teleconferences, face-to-face meetings
Impact Sterne JAC, Hernán MA, Reeves BC, Savovic J, Berkman ND, Viswanathan M, Henry D, Altman DG, Ansari MT, Boutron I, Carpenter JR, Chan AW, Churchill R, Deeks JJ, Hróbjartsson A, Kirkham J, Jüni P, Loke YK, Pigott TD, Ramsay CR, Regidor D, Rothstein HR, Sandhu L, Santaguida PL, Schünemann HJ, Shea B, Shrier I, Tugwell P, Turner L, Valentine JC, Waddington H, Waters E, Wells GA, Whiting PF, Higgins JPT. ROBINS-I: a tool for assessing risk of bias in non-randomized studies of interventions. BMJ 2016; 355; i4919; doi: 10.1136/bmj.i4919. Whiting P, Savovic J, Higgins JPT, Caldwell DM, Reeves BC, Shea B, Davies P, Kleijnen J, Churchill R, the ROBIS group. ROBIS: a new tool to assess the risk of bias in systematic reviews. Journal of Clinical Epidemiology 2016; 69: 225-34; doi: 10.1016/j.jclinepi.2015.06.005.
Start Year 2011
 
Description Risk of bias tools for systematic reviews 
Organisation University of Paris - Descartes
Country France 
Sector Academic/University 
PI Contribution Leadership of the collaborative project from Bristol
Collaborator Contribution Contributions to writing, teleconferences, face-to-face meetings
Impact Sterne JAC, Hernán MA, Reeves BC, Savovic J, Berkman ND, Viswanathan M, Henry D, Altman DG, Ansari MT, Boutron I, Carpenter JR, Chan AW, Churchill R, Deeks JJ, Hróbjartsson A, Kirkham J, Jüni P, Loke YK, Pigott TD, Ramsay CR, Regidor D, Rothstein HR, Sandhu L, Santaguida PL, Schünemann HJ, Shea B, Shrier I, Tugwell P, Turner L, Valentine JC, Waddington H, Waters E, Wells GA, Whiting PF, Higgins JPT. ROBINS-I: a tool for assessing risk of bias in non-randomized studies of interventions. BMJ 2016; 355; i4919; doi: 10.1136/bmj.i4919. Whiting P, Savovic J, Higgins JPT, Caldwell DM, Reeves BC, Shea B, Davies P, Kleijnen J, Churchill R, the ROBIS group. ROBIS: a new tool to assess the risk of bias in systematic reviews. Journal of Clinical Epidemiology 2016; 69: 225-34; doi: 10.1016/j.jclinepi.2015.06.005.
Start Year 2011
 
Description Risk of bias tools for systematic reviews 
Organisation University of Southern Denmark
Country Denmark 
Sector Academic/University 
PI Contribution Leadership of the collaborative project from Bristol
Collaborator Contribution Contributions to writing, teleconferences, face-to-face meetings
Impact Sterne JAC, Hernán MA, Reeves BC, Savovic J, Berkman ND, Viswanathan M, Henry D, Altman DG, Ansari MT, Boutron I, Carpenter JR, Chan AW, Churchill R, Deeks JJ, Hróbjartsson A, Kirkham J, Jüni P, Loke YK, Pigott TD, Ramsay CR, Regidor D, Rothstein HR, Sandhu L, Santaguida PL, Schünemann HJ, Shea B, Shrier I, Tugwell P, Turner L, Valentine JC, Waddington H, Waters E, Wells GA, Whiting PF, Higgins JPT. ROBINS-I: a tool for assessing risk of bias in non-randomized studies of interventions. BMJ 2016; 355; i4919; doi: 10.1136/bmj.i4919. Whiting P, Savovic J, Higgins JPT, Caldwell DM, Reeves BC, Shea B, Davies P, Kleijnen J, Churchill R, the ROBIS group. ROBIS: a new tool to assess the risk of bias in systematic reviews. Journal of Clinical Epidemiology 2016; 69: 225-34; doi: 10.1016/j.jclinepi.2015.06.005.
Start Year 2011
 
Description Risk of bias tools for systematic reviews 
Organisation University of Toronto
Country Canada 
Sector Academic/University 
PI Contribution Leadership of the collaborative project from Bristol
Collaborator Contribution Contributions to writing, teleconferences, face-to-face meetings
Impact Sterne JAC, Hernán MA, Reeves BC, Savovic J, Berkman ND, Viswanathan M, Henry D, Altman DG, Ansari MT, Boutron I, Carpenter JR, Chan AW, Churchill R, Deeks JJ, Hróbjartsson A, Kirkham J, Jüni P, Loke YK, Pigott TD, Ramsay CR, Regidor D, Rothstein HR, Sandhu L, Santaguida PL, Schünemann HJ, Shea B, Shrier I, Tugwell P, Turner L, Valentine JC, Waddington H, Waters E, Wells GA, Whiting PF, Higgins JPT. ROBINS-I: a tool for assessing risk of bias in non-randomized studies of interventions. BMJ 2016; 355; i4919; doi: 10.1136/bmj.i4919. Whiting P, Savovic J, Higgins JPT, Caldwell DM, Reeves BC, Shea B, Davies P, Kleijnen J, Churchill R, the ROBIS group. ROBIS: a new tool to assess the risk of bias in systematic reviews. Journal of Clinical Epidemiology 2016; 69: 225-34; doi: 10.1016/j.jclinepi.2015.06.005.
Start Year 2011