Use of multiple covariates in assessing treatment‐effect modifiers: A methodological review of individual participant data meta‐analyses
Published 2023 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Use of multiple covariates in assessing treatment‐effect modifiers: A methodological review of individual participant data meta‐analyses
Authors
Keywords
-
Journal
Research Synthesis Methods
Volume -, Issue -, Pages -
Publisher
Wiley
Online
2023-09-29
DOI
10.1002/jrsm.1674
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Nonlinear effects and effect modification at the participant-level in IPD meta-analysis part 2: methodological guidance is available
- (2023) Nadine Marlin et al. JOURNAL OF CLINICAL EPIDEMIOLOGY
- Nonlinear effects and effect modification at the participant-level in IPD meta-analysis part 1: analysis methods are often substandard
- (2023) Nadine Marlin et al. JOURNAL OF CLINICAL EPIDEMIOLOGY
- Estimating interactions and subgroup‐specific treatment effects in meta‐analysis without aggregation bias: A within‐trial framework
- (2022) Peter J. Godolphin et al. Research Synthesis Methods
- Individual participant data meta‐analysis to examine interactions between treatment effect and participant‐level covariates: Statistical recommendations for conduct and planning
- (2020) Richard D. Riley et al. STATISTICS IN MEDICINE
- Comparing methods for estimating patient‐specific treatment effects in individual patient data meta‐analysis
- (2020) Michael Seo et al. STATISTICS IN MEDICINE
- Meta-analysis of Gaussian individual patient data: Two-stage or not two-stage?
- (2018) Tim P. Morris et al. STATISTICS IN MEDICINE
- Meta-analytical methods to identify who benefits most from treatments: daft, deluded, or deft approach?
- (2017) David J Fisher et al. BMJ-British Medical Journal
- Exploring changes over time and characteristics associated with data retrieval across individual participant data meta-analyses: systematic review
- (2017) Sarah J Nevitt et al. BMJ-British Medical Journal
- Meta-analytical methods to identify who benefits most from treatments: daft, deluded, or deft approach?
- (2017) David J Fisher et al. BMJ-British Medical Journal
- Exploring changes over time and characteristics associated with data retrieval across individual participant data meta-analyses: systematic review
- (2017) Sarah J Nevitt et al. BMJ-British Medical Journal
- Should multiple imputation be the method of choice for handling missing data in randomized trials?
- (2016) Thomas R Sullivan et al. STATISTICAL METHODS IN MEDICAL RESEARCH
- 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) Hairui Hua et al. STATISTICS IN MEDICINE
- Meta-analysis using individual participant data: one-stage and two-stage approaches, and why they may differ
- (2016) Danielle L. Burke et al. STATISTICS IN MEDICINE
- Preferred Reporting Items for a Systematic Review and Meta-analysis of Individual Participant Data
- (2015) Lesley A. Stewart et al. JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION
- Individual Participant Data (IPD) Meta-analyses of Randomised Controlled Trials: Guidance on Their Use
- (2015) Jayne F. Tierney et al. PLOS MEDICINE
- Statistical Analysis of Individual Participant Data Meta-Analyses: A Comparison of Methods and Recommendations for Practice
- (2012) Gavin B. Stewart et al. PLoS One
- A critical review of methods for the assessment of patient-level interactions in individual participant data meta-analysis of randomized trials, and guidance for practitioners
- (2011) D.J. Fisher et al. JOURNAL OF CLINICAL EPIDEMIOLOGY
Create your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create NowBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started