What is he best known for?
The fields of study he is best known for:
- Statistics
- Internal medicine
- Cancer
Sander Greenland spends much of his time researching Statistics, Confounding, Econometrics, Confidence interval and Odds ratio.
Statistics is a component of his Covariate, Logistic regression, Estimator, Regression and Regression analysis studies.
His Confounding study also includes fields such as
- Causation which connect with Causality,
- Selection that intertwine with fields like Feature selection.
His studies in Econometrics integrate themes in fields like Inference and Bayesian probability, Bayes' theorem.
The Confidence interval study combines topics in areas such as Sample size determination, Meta-analysis, Mean squared error, Point estimation and Monte Carlo method.
Surgery is closely connected to Case-control study in his research, which is encompassed under the umbrella topic of Odds ratio.
His most cited work include:
- Causal diagrams for epidemiologic research. (2342 citations)
- Modeling and variable selection in epidemiologic analysis. (1765 citations)
- THE IMPACT OF CONFOUNDER SELECTION CRITERIA ON EFFECT ESTIMATION (1758 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Statistics, Econometrics, Confounding, Confidence interval and Internal medicine.
His study in Estimator, Odds ratio, Logistic regression, Bayesian probability and Covariate is carried out as part of his Statistics studies.
His Econometrics research incorporates themes from Regression analysis and Observational study.
A large part of his Confounding studies is devoted to Confounding Factors.
His research in Confidence interval is mostly focused on Relative risk.
His Internal medicine research is multidisciplinary, relying on both Gastroenterology and Oncology.
He most often published in these fields:
- Statistics (32.94%)
- Econometrics (23.27%)
- Confounding (16.37%)
What were the highlights of his more recent work (between 2013-2021)?
- Statistics (32.94%)
- Econometrics (23.27%)
- Confidence interval (12.43%)
In recent papers he was focusing on the following fields of study:
Sander Greenland mainly investigates Statistics, Econometrics, Confidence interval, Statistical hypothesis testing and Confounding.
His multidisciplinary approach integrates Statistics and Sensitivity in his work.
His Econometrics research is multidisciplinary, incorporating elements of Uncertainty analysis, Prior probability, Bayesian probability and Identification.
Sander Greenland interconnects Null hypothesis, Alternative hypothesis, Dosing and Statistical power in the investigation of issues within Confidence interval.
His study in Statistical hypothesis testing is interdisciplinary in nature, drawing from both Statistical inference, Inference, Interpretation and Scientific communication.
Sander Greenland has researched Confounding in several fields, including Treatment outcome, Outcome, Regression, Causal inference and Measure.
Between 2013 and 2021, his most popular works were:
- Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations (876 citations)
- Scientists rise up against statistical significance (863 citations)
- Increasing value and reducing waste in research design, conduct, and analysis (815 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Internal medicine
- Cancer
His main research concerns Statistics, Inference, Confounding, Econometrics and Covariate.
His Inference research focuses on subjects like p-value, which are linked to Interpretation, Statistical power and Confidence interval.
His Confounding research focuses on Odds ratio and how it connects with Protocol and Matching.
His studies in Econometrics integrate themes in fields like Logistic regression, Prior probability, Bayesian probability, Representation and Graphical model.
The Prior probability study which covers Regression analysis that intersects with Sample size determination.
His research investigates the connection between Covariate and topics such as Artificial intelligence that intersect with problems in Causal inference and Outcome.
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