Dan Jackson

Statistics and Probability · 2009–2018

A researcher in the Science corpus: 10 papers; trusts 40 researchers and is trusted by 39 researchers here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 104 citations into it; #1,357 of 12,932 by trust here, #926 by citations
• Economics and Econometrics — 21 citations into it; #8,869 of 23,481 by trust here, #9,863 by citations
• Surgery — 17 citations into it
• Artificial Intelligence — 10 citations into it
• Public Health, Environmental and Occupational Health — 10 citations into it
• Food Science — 7 citations into it

Whom Dan Jackson trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Julian P. T. Higgins
2. R. Riley
3. S. Thompson
4. A. Sutton
5. L. Hedges
6. W. Viechtbauer
7. Ian R. White
8. R. Harbord
9. R. Dersimonian
10. Guido Knapp

Who trusts Dan Jackson overall

1. Nathan Walter
2. Ian R. White
3. Antonio Gasparrini
4. Mike W.-L. Cheung
5. Georgia Salanti
6. U. Krahn
7. E. Tanner‐Smith
8. Julian P. T. Higgins
9. D. Aune
10. Ben Armstrong

Papers from Dan Jackson's perspective

Ordered by how strongly the researchers Dan Jackson trusts endorse each paper. Same corpus, a different order for every researcher.

1. Random-effects model for meta-analysis of clinical trials: an update. (Contemporary Clinical Trials)
2. How vague is vague? A simulation study of the impact of the use of vague prior distributions in MCMC using WinBUGS
3. Controlling the risk of spurious findings from meta‐regression (Statistics in Medicine)
4. WinBUGS - A Bayesian modelling framework: Concepts, structure, and extensibility (Statistics and computing)
5. What to add to nothing? Use and avoidance of continuity corrections in meta-analysis of sparse data. (Statistics in Medicine)
6. Advanced methods in meta‐analysis: multivariate approach and meta‐regression (Statistics in Medicine)
7. Improved tests for a random effects meta‐regression with a single covariate (Statistics in Medicine)
8. General methods for monitoring convergence of iterative simulations
9. Predicting the extent of heterogeneity in meta-analysis, using empirical data from the Cochrane Database of Systematic Reviews (International Journal of Epidemiology)
10. Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper)

Their papers

• How does the DerSimonian and Laird procedure for random effects meta-analysis compare with its more efficient but harder to compute counterparts? (2010) — 223 citations
• Consistency and inconsistency in network meta-analysis: model estimation using multivariate meta-regression (2012) — 1,033 citations
• Consistency and inconsistency in network meta-analysis: concepts and models for multi-arm studies (2012) — 1,812 citations
• Multivariate meta-analysis: Potential and promise (2011) — 517 citations
• Rejoinder to commentaries on ‘Multivariate meta‐analysis: Potential and promise’ (2011) — 166 citations
• Extending DerSimonian and Laird's methodology to perform multivariate random effects meta‐analyses (2009) — 585 citations
• Power analysis for random-effects meta-analysis (2017) — 549 citations
• A comparison of heterogeneity variance estimators in simulated random‐effects meta‐analyses (2018) — 871 citations
• Methods to estimate the between‐study variance and its uncertainty in meta‐analysis† (2015) — 1,384 citations
• Predictive distributions for between-study heterogeneity and simple methods for their application in Bayesian meta-analysis (2014) — 336 citations