What is he best known for?
The fields of study he is best known for:
- Internal medicine
- Statistics
- Cancer
His primary areas of investigation include Meta-analysis, Internal medicine, Econometrics, Publication bias and Statistics.
His research in Meta-analysis intersects with topics in Clinical trial, Research design, Systematic review, Evidence-based medicine and Bayesian probability.
His Internal medicine research is multidisciplinary, incorporating elements of Gastroenterology and Surgery.
Keith R. Abrams combines subjects such as Statistical hypothesis testing, Prior probability, Study heterogeneity and Frequentist inference with his study of Econometrics.
His Publication bias study combines topics in areas such as Primary tumor and Data mining.
His study in Statistics is interdisciplinary in nature, drawing from both Meta-regression and Random effects model.
His most cited work include:
- The risk of colorectal cancer in ulcerative colitis: a meta-analysis (2110 citations)
- Comparison of Two Methods to Detect Publication Bias in Meta-analysis (1235 citations)
- Empirical assessment of effect of publication bias on meta-analyses. (1040 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Meta-analysis, Internal medicine, Econometrics, Bayesian probability and Statistics.
A large part of his Meta-analysis studies is devoted to Publication bias.
His study looks at the intersection of Internal medicine and topics like Surgery with Confidence interval.
His research integrates issues of Multivariate statistics and Random effects model in his study of Econometrics.
His Bayesian probability research is under the purview of Artificial intelligence.
His Randomized controlled trial study frequently involves adjacent topics like Clinical trial.
He most often published in these fields:
- Meta-analysis (24.03%)
- Internal medicine (18.76%)
- Econometrics (14.42%)
What were the highlights of his more recent work (between 2015-2021)?
- Meta-analysis (24.03%)
- Econometrics (14.42%)
- Bayesian probability (12.59%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Meta-analysis, Econometrics, Bayesian probability, Internal medicine and Statistics.
His Meta-analysis research incorporates elements of Psychological intervention, Observational study, Prospective cohort study and Public health.
Keith R. Abrams has researched Econometrics in several fields, including Parametric statistics, Outcome, Monte Carlo method, Survival analysis and Random effects model.
The various areas that he examines in his Bayesian probability study include Univariate, Bivariate analysis, Machine learning and Surrogate endpoint.
His Internal medicine study incorporates themes from Surgery and Oncology.
His biological study spans a wide range of topics, including Placebo and Hazard ratio.
Between 2015 and 2021, his most popular works were:
- Ethnicity and clinical outcomes in COVID-19: A systematic review and meta-analysis. (83 citations)
- Methods for Population-Adjusted Indirect Comparisons in Health Technology Appraisal (70 citations)
- NICE DSU Technical Support Document 18: Methods for population-adjusted indirect comparisons in submissions to NICE (59 citations)
In his most recent research, the most cited papers focused on:
- Internal medicine
- Statistics
- Cancer
Keith R. Abrams mainly investigates Econometrics, Internal medicine, Meta-analysis, Clinical trial and Survival analysis.
His studies deal with areas such as Proportional hazards model, Statistics, Parametric statistics, Outcome and Comparative effectiveness research as well as Econometrics.
His Internal medicine study typically links adjacent topics like Surgery.
His Meta-analysis research includes themes of Public health and Risk of infection.
His work carried out in the field of Clinical trial brings together such families of science as Pharmacy, Duchenne muscular dystrophy and Regulatory science.
The study incorporates disciplines such as Randomized controlled trial, Censoring and Overall survival in addition to Survival analysis.
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