Comparison of random forest and parametric imputation models for imputing missing data using MICE: A CALIBER study

American Journal of Epidemiology

Volumn 179, Issue 6, 2014, Pages 764-774

  Shah, Anoop D ^a   Bartlett, Jonathan W ^a   Carpenter, James ^a   Nicholas, Owen ^a   Hemingway, Harry ^a  

^a UNIVERSITY COLLEGE LONDON   (United Kingdom)

Author keywords

angina stable; imputation; missing data; missingness at random; regression trees; simulation; survival

Indexed keywords

ALGORITHM; DATA ACQUISITION; EPIDEMIOLOGY; ESTIMATION METHOD; FOREST ECOSYSTEM; HEALTH RISK; MULTIVARIATE ANALYSIS; NONLINEARITY; NUMERICAL MODEL; REGRESSION ANALYSIS; SIMULATION;

ALGORITHM; ARTICLE; DATA BASE; EPIDEMIOLOGICAL DATA; FEMALE; HUMAN; INTERMETHOD COMPARISON; MAJOR CLINICAL STUDY; MALE; MULTIVARIATE IMPUTATION BY CHAINED EQUATION; NONLINEAR SYSTEM; PARAMETRIC TEST; RANDOM FOREST; RANDOM SAMPLE; SIMULATION; STABLE ANGINA PECTORIS; SURVIVAL;

ANGINA;

ANGINA, STABLE; IMPUTATION; MISSING DATA; MISSINGNESS AT RANDOM; REGRESSION TREES; SIMULATION; SURVIVAL;

AGE FACTORS; ANGINA, STABLE; ARTIFICIAL INTELLIGENCE; BIAS (EPIDEMIOLOGY); COMPUTER SIMULATION; CONFIDENCE INTERVALS; EPIDEMIOLOGIC METHODS; HEALTH BEHAVIOR; HEALTH STATUS; HUMANS; PROPORTIONAL HAZARDS MODELS; RANDOM ALLOCATION; SEX FACTORS;

EID: 84895894249     PISSN: 00029262     EISSN: 14766256     Source Type: Journal    
DOI: 10.1093/aje/kwt312     Document Type: Article

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