Publication Date
In 2025 | 0 |
Since 2024 | 0 |
Since 2021 (last 5 years) | 1 |
Since 2016 (last 10 years) | 9 |
Since 2006 (last 20 years) | 9 |
Descriptor
Error of Measurement | 9 |
Medical Research | 9 |
Meta Analysis | 8 |
Outcomes of Treatment | 8 |
Comparative Analysis | 7 |
Simulation | 5 |
Correlation | 4 |
Drug Therapy | 3 |
Patients | 3 |
Randomized Controlled Trials | 3 |
Bayesian Statistics | 2 |
More ▼ |
Source
Research Synthesis Methods | 9 |
Author
Ades, A. E. | 1 |
Carlin, Bradley P. | 1 |
Cates, Christopher J. | 1 |
Chan, Kelvin K. W. | 1 |
Cheng, Sierra | 1 |
Chu, Haitao | 1 |
Clark, Allan | 1 |
Debray, Thomas P. A. | 1 |
Dias, Sofia | 1 |
Dogo, Samson Henry | 1 |
Hong, Hwanhee | 1 |
More ▼ |
Publication Type
Journal Articles | 9 |
Reports - Research | 6 |
Reports - Evaluative | 2 |
Reports - Descriptive | 1 |
Education Level
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Proctor, Tanja; Zimmermann, Samuel; Seide, Svenja; Kieser, Meinhard – Research Synthesis Methods, 2022
During drug development, a biomarker is sometimes identified as separating a patient population into those with more and those with less benefit from evaluated treatments. Consequently, later studies might be targeted, while earlier ones are performed in mixed patient populations. This poses a challenge in evidence synthesis, especially if only…
Descriptors: Comparative Analysis, Meta Analysis, Patients, Medical Research
Equivalence of Entropy Balancing and the Method of Moments for Matching-Adjusted Indirect Comparison
Phillippo, David M.; Dias, Sofia; Ades, A. E.; Welton, Nicky J. – Research Synthesis Methods, 2020
Indirect comparisons are used to obtain estimates of relative effectiveness between two treatments that have not been compared in the same randomized controlled trial, but have instead been compared against a common comparator in separate trials. Standard indirect comparisons use only aggregate data, under the assumption that there are no…
Descriptors: Comparative Analysis, Outcomes of Treatment, Patients, Randomized Controlled Trials
van Zundert, Camiel H. J.; Miocevic, Milica – Research Synthesis Methods, 2020
Synthesizing findings about the indirect (mediated) effect plays an important role in determining the mechanism through which variables affect one another. This simulation study compared six methods for synthesizing indirect effects: correlation-based MASEM, parameter-based MASEM, marginal likelihood synthesis, an adjustment to marginal likelihood…
Descriptors: Correlation, Comparative Analysis, Meta Analysis, Bayesian Statistics
Rücker, Gerta; Cates, Christopher J.; Schwarzer, Guido – Research Synthesis Methods, 2017
Systematic reviewers conducting pairwise meta-analyses sometimes encounter multi-arm studies. To include these studies, and to avoid a unit-of-analysis error, often two or more arms are combined or the control arm is split. In this tutorial, we present 5 different approaches that can be used. Particularly, we present a novel approach (method 4)…
Descriptors: Meta Analysis, Medical Research, Outcomes of Treatment, Error of Measurement
Kontopantelis, Evangelos – Research Synthesis Methods, 2018
Background: Individual patient data (IPD) meta-analysis allows for the exploration of heterogeneity and can identify subgroups that most benefit from an intervention (or exposure), much more successfully than meta-analysis of aggregate data. One-stage or two-stage IPD meta-analysis is possible, with the former using mixed-effects regression models…
Descriptors: Patients, Medical Research, Meta Analysis, Intervention
Saluja, Ronak; Cheng, Sierra; delos Santos, Keemo Althea; Chan, Kelvin K. W. – Research Synthesis Methods, 2019
Objective: Various statistical methods have been developed to estimate hazard ratios (HRs) from published Kaplan-Meier (KM) curves for the purpose of performing meta-analyses. The objective of this study was to determine the reliability, accuracy, and precision of four commonly used methods by Guyot, Williamson, Parmar, and Hoyle and Henley.…
Descriptors: Meta Analysis, Reliability, Accuracy, Randomized Controlled Trials
Debray, Thomas P. A.; Moons, Karel G. M.; Riley, Richard D. – Research Synthesis Methods, 2018
Small-study effects are a common threat in systematic reviews and may indicate publication bias. Their existence is often verified by visual inspection of the funnel plot. Formal tests to assess the presence of funnel plot asymmetry typically estimate the association between the reported effect size and their standard error, the total sample size,…
Descriptors: Meta Analysis, Comparative Analysis, Publications, Bias
Dogo, Samson Henry; Clark, Allan; Kulinskaya, Elena – Research Synthesis Methods, 2017
Temporal changes in magnitude of effect sizes reported in many areas of research are a threat to the credibility of the results and conclusions of meta-analysis. Numerous sequential methods for meta-analysis have been proposed to detect changes and monitor trends in effect sizes so that meta-analysis can be updated when necessary and interpreted…
Descriptors: Effect Size, Meta Analysis, Visualization, Error of Measurement
Hong, Hwanhee; Chu, Haitao; Zhang, Jing; Carlin, Bradley P. – Research Synthesis Methods, 2016
Bayesian statistical approaches to mixed treatment comparisons (MTCs) are becoming more popular because of their flexibility and interpretability. Many randomized clinical trials report multiple outcomes with possible inherent correlations. Moreover, MTC data are typically sparse (although richer than standard meta-analysis, comparing only two…
Descriptors: Bayesian Statistics, Meta Analysis, Outcomes of Treatment, Comparative Analysis