scholarly journals Measurement Error in Meta‐Analysis ( MEMA ) ‐ a Bayesian framework for continuous outcome data subject to non‐differential measurement error

2021 ◽  
Author(s):  
Harlan Campbell ◽  
Valentijn M. T. de Jong ◽  
Lauren Maxwell ◽  
Thomas Jaenisch ◽  
Thomas P. A. Debray ◽  
...  
Keyword(s):  
Measurement Error ◽  
Meta Analysis ◽  
Bayesian Framework ◽  
Outcome Data ◽  
Differential Measurement ◽  
Continuous Outcome ◽  
Differential Measurement Error ◽  
Data Subject

scholarly journals Mendelian randomisation for mediation analysis: current methods and challenges for implementation

2021 ◽  
Author(s):  
Alice R. Carter ◽  
Eleanor Sanderson ◽  
Gemma Hammerton ◽  
Rebecca C. Richmond ◽  
George Davey Smith ◽  
...  
Keyword(s):  
Measurement Error ◽  
Causal Inference ◽  
Mediation Analysis ◽  
Instrumental Variable ◽  
Real Data ◽  
Mendelian Randomisation ◽  
Differential Measurement ◽  
Individual Level ◽  
Differential Measurement Error ◽  
Summary Data

AbstractMediation analysis seeks to explain the pathway(s) through which an exposure affects an outcome. Traditional, non-instrumental variable methods for mediation analysis experience a number of methodological difficulties, including bias due to confounding between an exposure, mediator and outcome and measurement error. Mendelian randomisation (MR) can be used to improve causal inference for mediation analysis. We describe two approaches that can be used for estimating mediation analysis with MR: multivariable MR (MVMR) and two-step MR. We outline the approaches and provide code to demonstrate how they can be used in mediation analysis. We review issues that can affect analyses, including confounding, measurement error, weak instrument bias, interactions between exposures and mediators and analysis of multiple mediators. Description of the methods is supplemented by simulated and real data examples. Although MR relies on large sample sizes and strong assumptions, such as having strong instruments and no horizontally pleiotropic pathways, our simulations demonstrate that these methods are unaffected by confounders of the exposure or mediator and the outcome and non-differential measurement error of the exposure or mediator. Both MVMR and two-step MR can be implemented in both individual-level MR and summary data MR. MR mediation methods require different assumptions to be made, compared with non-instrumental variable mediation methods. Where these assumptions are more plausible, MR can be used to improve causal inference in mediation analysis.

scholarly journals Mendelian randomisation for mediation analysis: current methods and challenges for implementation

2019 ◽  
Author(s):  
Alice R Carter ◽  
Eleanor Sanderson ◽  
Gemma Hammerton ◽  
Rebecca C Richmond ◽  
George Davey Smith ◽  
...  
Keyword(s):  
Measurement Error ◽  
Causal Inference ◽  
Mediation Analysis ◽  
Real Data ◽  
Mendelian Randomisation ◽  
Reverse Causality ◽  
Differential Measurement ◽  
Individual Level ◽  
Differential Measurement Error ◽  
Summary Data

AbstractMediation analysis seeks to explain the pathway(s) through which an exposure affects an outcome. Mediation analysis experiences a number of methodological difficulties, including bias due to confounding and measurement error. Mendelian randomisation (MR) can be used to improve causal inference for mediation analysis. We describe two approaches that can be used for estimating mediation analysis with MR: multivariable Mendelian randomisation (MVMR) and two-step Mendelian randomisation. We outline the approaches and provide code to demonstrate how they can be used in mediation analysis. We review issues that can affect analyses, including confounding, measurement error, weak instrument bias, and analysis of multiple mediators. Description of the methods is supplemented by simulated and real data examples. Although Mendelian randomisation relies on large sample sizes and strong assumptions, such as having strong instruments and no horizontally pleiotropic pathways, our examples demonstrate that it is unlikely to be affected by confounders of the exposure or mediator and the outcome, reverse causality and non-differential measurement error of the exposure or mediator. Both MVMR and two-step MR can be implemented in both individual-level MR and summary data MR, and can improve causal inference in mediation analysis.

scholarly journals A Bayesian framework to account for uncertainty due to missing binary outcome data in pairwise meta‐analysis

2015 ◽  
Vol 34 (12) ◽  
pp. 2062-2080 ◽  
Author(s):  
N. L. Turner ◽  
S. Dias ◽  
A. E. Ades ◽  
N. J. Welton
Keyword(s):  
Meta Analysis ◽  
Bayesian Framework ◽  
Outcome Data ◽  
Binary Outcome

scholarly journals One‐stage random effects meta‐analysis using linear mixed models for aggregate continuous outcome data

2019 ◽  
Vol 10 (3) ◽  
pp. 360-375 ◽  
Author(s):  
Katerina Papadimitropoulou ◽  
Theo Stijnen ◽  
Olaf M. Dekkers ◽  
Saskia Cessie
Keyword(s):  
Random Effects ◽  
Mixed Models ◽  
Meta Analysis ◽  
Linear Mixed Models ◽  
Outcome Data ◽  
Continuous Outcome ◽  
One Stage

scholarly journals Allowing for uncertainty due to missing continuous outcome data in pairwise and network meta‐analysis

2015 ◽  
Vol 34 (5) ◽  
pp. 721-741 ◽  
Author(s):  
Dimitris Mavridis ◽  
Ian R. White ◽  
Julian P. T. Higgins ◽  
Andrea Cipriani ◽  
Georgia Salanti
Keyword(s):  
Meta Analysis ◽  
Outcome Data ◽  
Continuous Outcome
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