Publication Date
In 2025 | 0 |
Since 2024 | 0 |
Since 2021 (last 5 years) | 2 |
Since 2016 (last 10 years) | 2 |
Since 2006 (last 20 years) | 2 |
Descriptor
Computation | 2 |
Meta Analysis | 2 |
Randomized Controlled Trials | 2 |
Sample Size | 2 |
Bayesian Statistics | 1 |
Data | 1 |
Matrices | 1 |
Monte Carlo Methods | 1 |
Participant Characteristics | 1 |
Prediction | 1 |
Statistical Analysis | 1 |
More ▼ |
Source
Research Synthesis Methods | 2 |
Author
Collins, Gary S. | 1 |
Ensor, Joie | 1 |
Hattle, Miriam | 1 |
Qi, Hongchao | 1 |
Riley, Richard D. | 1 |
Rizopoulos, Dimitris | 1 |
Rosmalen, Joost | 1 |
Whittle, Rebecca | 1 |
Publication Type
Journal Articles | 2 |
Information Analyses | 1 |
Reports - Descriptive | 1 |
Education Level
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Riley, Richard D.; Collins, Gary S.; Hattle, Miriam; Whittle, Rebecca; Ensor, Joie – Research Synthesis Methods, 2023
Before embarking on an individual participant data meta-analysis (IPDMA) project, researchers should consider the power of their planned IPDMA conditional on the studies promising their IPD and their characteristics. Such power estimates help inform whether the IPDMA project is worth the time and funding investment, before IPD are collected. Here,…
Descriptors: Computation, Meta Analysis, Participant Characteristics, Data
Qi, Hongchao; Rizopoulos, Dimitris; Rosmalen, Joost – Research Synthesis Methods, 2023
The meta-analytic-predictive (MAP) approach is a Bayesian method to incorporate historical controls in new trials that aims to increase the statistical power and reduce the required sample size. Here we investigate how to calculate the sample size of the new trial when historical data is available, and the MAP approach is used in the analysis. In…
Descriptors: Sample Size, Computation, Meta Analysis, Bayesian Statistics