Meta-analysis and systematic reviews

Automation of citation screening in pre-clinical systematic reviews

The paper develops machine learning classifiers to automate citation screening in pre-clinical systematic reviews, achieving high sensitivity and specificity across different domains.

Standardized Effect Sizes and Image-Based Meta-Analytical Approaches for fMRI Data.

The paper discusses methods to standardize effect sizes in fMRI data and presents approaches for meta-analyzing results across studies.

Hypothesis-testing Improves the Predicted Reliability of Neuroscience Research

The paper reviews neuroscience publications to assess hypothesis-testing practices and shows that combining multiple test results increases predicted research reliability.

Machine learning algorithms for systematic reviews: reducing workload in a preclinical review of animal studies and reducing human screening error

This paper applies machine learning to citation screening in systematic reviews of animal studies, improving sensitivity and identifying human errors.

Effect size and statistical power in the rodent fear conditioning literature - a systematic review

This study reviews effect sizes and statistical power in rodent fear conditioning research, finding low power and inconsistent reporting of these metrics.

Estimating the statistical performance of different approaches to meta-analysis of data from animal studies in identifying the impact of aspects of study design

This paper compares statistical methods for meta-analysis of animal studies, finding that normalized mean difference (NMD) has higher power than standardized mean difference (SMD), and meta-regression outperforms stratified meta-analysis.

Assessing robustness against potential publication bias in coordinate based fMRI meta-analyses using the Fail-Safe N

The paper develops a tool to assess robustness against publication bias in fMRI meta-analyses by simulating noise studies and testing parameter effects.

Meta-analysis of the variance ratio

The paper presents a meta-analytic approach to combine variance ratios from different studies to produce more robust estimates, addressing the issue of wide confidence intervals in small studies.