Ian R. White mainly focuses on Missing data, Statistics, Econometrics, Randomized controlled trial and Meta-analysis. His Missing data research incorporates elements of Covariate, Software, Data mining and MEDLINE. His biological study spans a wide range of topics, including Estimator, Inverse probability weighting and Multivariate statistics.
His studies in Randomized controlled trial integrate themes in fields like Clinical trial and Public health. Ian R. White has included themes like Psychological intervention and Epidemiology in his Clinical trial study. His Imputation study combines topics in areas such as Regression analysis and Regression.
His primary areas of investigation include Statistics, Dermatology, Missing data, Meta-analysis and Econometrics. His Statistics research incorporates themes from Randomized controlled trial and Random effects model. His work deals with themes such as Psychological intervention and Physical therapy, which intersect with Randomized controlled trial.
His Dermatology research includes elements of Contact dermatitis, Patch testing, Contact allergy, Patch test and Allergic contact dermatitis. His biological study focuses on Imputation. The various areas that he examines in his Meta-analysis study include Multivariate statistics and Bayesian probability.
Ian R. White mainly investigates Meta-analysis, Statistics, Missing data, Contact allergy and Econometrics. His study in Meta-analysis is interdisciplinary in nature, drawing from both Contrast, Randomized controlled trial, Anxiety, Multivariate statistics and Bayesian probability. His Randomized controlled trial research is multidisciplinary, incorporating perspectives in Odds ratio and Family medicine.
Statistics is closely attributed to Random effects model in his research. In general Missing data study, his work on Imputation often relates to the realm of Binary data, thereby connecting several areas of interest. His work focuses on many connections between Contact allergy and other disciplines, such as Allergic contact dermatitis, that overlap with his field of interest in Dermatology.
His primary scientific interests are in Missing data, Statistics, Meta-analysis, Contact allergy and Imputation. Ian R. White combines subjects such as Mixture model, Artificial intelligence and Data mining with his study of Missing data. His research integrates issues of Econometrics, Hazard and Random effects model in his study of Statistics.
His Meta-analysis research is within the category of Internal medicine. His research in Contact allergy intersects with topics in Allergic contact dermatitis, Toxicology and Hair dyes. Ian R. White combines subjects such as Inference, Heteroscedasticity and Confounding with his study of Imputation.
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Multiple imputation using chained equations: Issues and guidance for practice
Ian R. White;Patrick Royston;Angela M. Wood.
Statistics in Medicine (2011)
Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls.
Jonathan A C Sterne;Ian R White;John B Carlin;Michael Spratt.
Health inequalities among British civil servants: the Whitehall II study
M.G. Marmot;S. Stansfeld;C. Patel;F. North.
The Lancet (1991)
Diabetes mellitus, fasting blood glucose concentration, and risk of vascular disease: a collaborative meta-analysis of 102 prospective studies
N Sarwar;P Gao;Seshasai Srk..
The Lancet (2010)
RoB 2: a revised tool for assessing risk of bias in randomised trials.
Jonathan A.C. Sterne;Jelena Savović;Jelena Savović;Matthew J. Page;Roy G. Elbers.
Lipoprotein(a) concentration and the risk of coronary heart disease, stroke, and nonvascular mortality.
S Erqou;S Kaptoge;PL Perry;E Di Angelantonio.
Blood Pressure and Incidence of Twelve Cardiovascular Diseases: Lifetime Risks, Healthy Life-Years Lost, and Age-Specific Associations in 1·25 Million People
Eleni Rapsomaniki;Adam Timmis;Julie George;Mar Pujades-Rodriguez.
The Lancet (2014)
Multiple Imputation by Chained Equations (MICE): Implementation in Stata
Patrick Royston;Ian R. White.
Journal of Statistical Software (2011)
C-Reactive Protein, Fibrinogen, and Cardiovascular Disease Prediction
Stephen Kaptoge;Emanuele Di Angelantonio;Lisa Pennells;Angela M. Wood.
The New England Journal of Medicine (2012)
Review of inverse probability weighting for dealing with missing data
Shaun R Seaman;Ian R White.
Statistical Methods in Medical Research (2013)
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