Multiple imputation for handling systematically missing confounders in meta-analysis of individual participant data

Statistics in Medicine

Volumn 32, Issue 28, 2013, Pages 4890-4905

  Resche Rigon, Matthieu ^a,b,c,d   White, Ian R ^a   Bartlett, JonathanW ^e   Peters, Sanne A E ^f   Thompson, Simon G ^a  

^a UNIVERSITY OF CAMBRIDGE   (United Kingdom)

^b SAINT LOUIS HOSPITAL   (France)

^c UNIVERSITÉ PARIS DESCARTES   (France)

^d INSERM   (France)

^e LONDON SCHOOL OF HYGIENE AND TROPICAL MEDICINE   (United Kingdom)

^f UNIVERSITY MEDICAL CENTER UTRECHT   (Netherlands)

Author keywords

IPD meta analysis; Missing data; Multilevel model; Multiple imputation: chained equations

Indexed keywords

CHOLESTEROL; CREATININE; HEMOGLOBIN;

ARTERIAL WALL THICKNESS; ARTICLE; BODY MASS; CARDIOVASCULAR DISEASE; COHORT ANALYSIS; CREATININE BLOOD LEVEL; DATA ANALYSIS; DIABETES MELLITUS; DYSLIPIDEMIA; HUMAN; HYPERTENSION; META ANALYSIS (TOPIC); METHODOLOGY; MONTE CARLO METHOD; MULTIPLE IMPUTATION METHOD; OBSERVATIONAL STUDY; PARTICIPANT OBSERVATION; POPULATION RESEARCH; RANDOM SAMPLE; RISK FACTOR; SMOKING; STATISTICAL ANALYSIS; STRUCTURAL EQUATION MODELING; SYSTOLIC BLOOD PRESSURE;

IPD META-ANALYSIS; MISSING DATA; MULTILEVEL MODEL; MULTIPLE IMPUTATION: CHAINED EQUATIONS;

CARDIOVASCULAR DISEASES; CAROTID INTIMA-MEDIA THICKNESS; COMPUTER SIMULATION; CONFOUNDING FACTORS (EPIDEMIOLOGY); EPIDEMIOLOGIC METHODS; HUMANS; META-ANALYSIS AS TOPIC; MODELS, STATISTICAL; MONTE CARLO METHOD;

EID: 84887138473     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.5894     Document Type: Article

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