scholarly journals Meta-analysis using individual participant data: one-stage and two-stage approaches, and why they may differ

2016 ◽  
Vol 36 (5) ◽  
pp. 855-875 ◽  
Author(s):  
Danielle L. Burke ◽  
Joie Ensor ◽  
Richard D. Riley
Keyword(s):  
Meta Analysis ◽  
Individual Participant Data ◽  
Two Stage ◽  
One Stage ◽  
Individual Participant

scholarly journals Multilevel Meta-Analysis of Individual Participant Data of Single-Case Experimental Designs: One-Stage versus Two-Stage Methods

2020 ◽  
pp. 1-20
Author(s):  
Lies Declercq ◽  
Laleh Jamshidi ◽  
Belén Fernández Castilla ◽  
Mariola Moeyaert ◽  
S. Natasha Beretvas ◽  
...  
Keyword(s):  
Single Case ◽  
Meta Analysis ◽  
Experimental Designs ◽  
Individual Participant Data ◽  
Stage Versus ◽  
Two Stage ◽  
One Stage ◽  
Individual Participant

scholarly journals Individual Participant Data Meta-Analysis for a Binary Outcome: One-Stage or Two-Stage?

PLoS ONE ◽  
2013 ◽  
Vol 8 (4) ◽  
pp. e60650 ◽  
Author(s):  
Thomas P. A. Debray ◽  
Karel G. M. Moons ◽  
Ghada Mohammed Abdallah Abo-Zaid ◽  
Hendrik Koffijberg ◽  
Richard David Riley
Keyword(s):  
Meta Analysis ◽  
Individual Participant Data ◽  
Binary Outcome ◽  
Two Stage ◽  
One Stage ◽  
Individual Participant

scholarly journals 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

2017 ◽  
Vol 27 (10) ◽  
pp. 2885-2905 ◽  
Author(s):  
Richard D Riley ◽  
Joie Ensor ◽  
Dan Jackson ◽  
Danielle L Burke
Keyword(s):  
Meta Analysis ◽  
Information Matrix ◽  
Indirect Evidence ◽  
Parameter Estimates ◽  
Individual Participant Data ◽  
One Stage ◽  
Individual Participant ◽  
Meta Regression ◽  
Multiple Treatments ◽  
Analysis Models

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 network (multivariate) meta-analysis of multiple treatments. These reveal percentage study weights toward clinically important estimates, such as summary treatment effects and treatment-covariate interactions, and are especially useful when some studies are potential outliers or at high risk of bias. We also derive percentage study weights toward methodologically interesting measures, such as the magnitude of ecological bias (difference between within-study and across-study associations) and the amount of inconsistency (difference between direct and indirect evidence in a network meta-analysis).

scholarly journals One‐stage individual participant data meta‐analysis models for continuous and binary outcomes: Comparison of treatment coding options and estimation methods

2020 ◽  
Vol 39 (19) ◽  
pp. 2536-2555
Author(s):  
Richard D. Riley ◽  
Amardeep Legha ◽  
Dan Jackson ◽  
Tim P. Morris ◽  
Joie Ensor ◽  
...  
Keyword(s):  
Meta Analysis ◽  
Estimation Methods ◽  
Binary Outcomes ◽  
Individual Participant Data ◽  
One Stage ◽  
Individual Participant ◽  
Analysis Models

scholarly journals One-stage individual participant data meta-analysis models: estimation of treatment-covariate interactions must avoid ecological bias by separating out within-trial and across-trial information

2016 ◽  
Vol 36 (5) ◽  
pp. 772-789 ◽  
Author(s):  
Hairui Hua ◽  
Danielle L. Burke ◽  
Michael J. Crowther ◽  
Joie Ensor ◽  
Catrin Tudur Smith ◽  
...  
Keyword(s):  
Meta Analysis ◽  
Individual Participant Data ◽  
Ecological Bias ◽  
One Stage ◽  
Individual Participant ◽  
Analysis Models

The reciprocal relationship between alliance and early treatment symptoms: A two-stage individual participant data meta-analysis.

2020 ◽  
Vol 88 (9) ◽  
pp. 829-843 ◽  
Author(s):  
Christoph Flückiger ◽  
Julian Rubel ◽  
A. C. Del Re ◽  
Adam O. Horvath ◽  
Bruce E. Wampold ◽  
...  
Keyword(s):  
Early Treatment ◽  
Meta Analysis ◽  
Reciprocal Relationship ◽  
Individual Participant Data ◽  
Two Stage ◽  
Individual Participant

Estimating interactions in individual participant data meta-analysis. A comparison of methods in practice.

2021 ◽  
Author(s):  
Ruth Walker ◽  
Lesley Stewart ◽  
Mark Simmonds
Keyword(s):  
Random Effects ◽  
Meta Analysis ◽  
Individual Preference ◽  
Detailed Examination ◽  
International Studies ◽  
Individual Participant Data ◽  
Stage Models ◽  
One Stage ◽  
Interaction Terms ◽  
Individual Participant

Abstract Medical interventions may be more effective in some types of individuals than others and identifying characteristics that modify the effectiveness of an intervention is a cornerstone of precision or stratified medicine. The opportunity for detailed examination of treatment-covariate interactions can be an important driver for undertaking an individual participant data (IPD) meta-analysis, rather than a meta-analysis using aggregate data. A number of recent modelling approaches are available. We apply these methods to the Perinatal Antiplatelet Review of International Studies (PARIS) Collaboration IPD dataset and compare estimates between them. We discuss the practical implications of applying these methods, which may be of interest to aid meta-analysists in the use of these, often complex models. Models compared included the two-stage meta-analysis of interaction terms and one-stage models which fit multiple random effects and separate within and between trial information. Models were fitted for nine covariates and five binary outcomes and results compared. Interaction terms produced by the methods were generally consistent. We show that where data are sparse and there is low heterogeneity in the covariate distributions across trials, the meta-analysis of interactions may produce unstable estimates and have issues with convergence. In this IPD dataset, varying assumptions by using multiple random effects in one-stage models or using only within trial information made little difference to the estimates of treatment-covariate interaction. Method choice will depend on datasets characteristics and individual preference.

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