Imputation-based strategies for clinical trial longitudinal data with nonignorable missing values

Statistics in Medicine

Volumn 27, Issue 15, 2008, Pages 2826-2849

  Yang, Xiaowei ^a,b   Li, Jinhui ^b,c   Shoptaw, Steven ^c  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b BayesSoft Inc   (United States)

^c UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Intermittent missing values; Markov transition model; Multiple partial imputation; Nonignorable dropout; Pattern mixture model; Selection model

Indexed keywords

CARBON MONOXIDE; METHADONE; NICOTINE PATCH;

ALGORITHM; ANALYSIS OF VARIANCE; ARTICLE; BEHAVIOR THERAPY; CLINICAL TRIAL; CONTROLLED CLINICAL TRIAL; CONTROLLED STUDY; HUMAN; LONGITUDINAL STUDY; MAJOR CLINICAL STUDY; MATHEMATICAL COMPUTING; MEDICAL RESEARCH; NORMAL DISTRIBUTION; PARAMETRIC TEST; RANDOMIZED CONTROLLED TRIAL; SIMULATION; SMOKING CESSATION; STATISTICAL MODEL; STATISTICAL SIGNIFICANCE; TOBACCO DEPENDENCE; TREATMENT DURATION;

BIAS (EPIDEMIOLOGY); BIOMEDICAL RESEARCH; CLINICAL TRIALS AS TOPIC; DATA INTERPRETATION, STATISTICAL; HUMANS; LONGITUDINAL STUDIES; MARKOV CHAINS; PATIENT DROPOUTS; SMOKING CESSATION;

EID: 47249109100     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.3111     Document Type: Article

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