C. Bias

Statistics and Probability · 2011

A researcher in the Science corpus: 1 paper; trusts 1 researcher and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom C. Bias trusts overall

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

  1. Julian P. T. Higgins

Who trusts C. Bias overall

  1. I. Robertson
  2. Tom Hendriks
  3. A. Carr
  4. H. Waddington
  5. Cary L. Cooper
  6. Marijke Schotanus-Dijkstra
  7. Hugues Lortie-Forgues
  8. Katie Cullen
  9. Julian P. T. Higgins
  10. H. White

Papers from C. Bias's perspective

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

  1. How vague is vague? A simulation study of the impact of the use of vague prior distributions in MCMC using WinBUGS
  2. Systematic Review of the Empirical Evidence of Study Publication Bias and Outcome Reporting Bias (PLoS ONE)
  3. Quantifying heterogeneity in a meta‐analysis (Statistics in Medicine)
  4. Observational studies analyzed like randomized experiments: an application to postmenopausal hormone therapy and coronary heart disease (Epidemiology)
  5. Systematic Review and Meta-analysis Supplements for Primary and Secondary Prevention: Mortality in Randomized Trials of Antioxidant
  6. Using Big Data to Emulate a Target Trial When a Randomized Trial Is Not Available. (American Journal of Epidemiology)
  7. The Handbook of Research Synthesis and Meta-Analysis
  8. Controlling the risk of spurious findings from meta‐regression (Statistics in Medicine)
  9. Predicting the extent of heterogeneity in meta-analysis, using empirical data from the Cochrane Database of Systematic Reviews (International Journal of Epidemiology)
  10. C-reactive protein and other markers of inflammation in the prediction of cardiovascular disease in women. (New England Journal of Medicine)

Their papers