Maximum likelihood analysis of logistic regression models with incomplete covariate data and auxiliary information

Biometrics

Volumn 57, Issue 1, 2001, Pages 34-42

  Horton, Nicholas J ^a,b   Laird, Nan M ^c  

^a BOSTON UNIVERSITY SCHOOL OF PUBLIC HEALTH   (United States)

^b BOSTON UNIVERSITY SCHOOL OF MEDICINE   (United States)

^c HARVARD SCHOOL OF PUBLIC HEALTH   (United States)

Author keywords

Conditional independence assumption; EM algorithm; Joint maximization; Logistic regression model; Missing covariates, Surrogate information; Two stage designs

Indexed keywords

REGRESSION ANALYSIS;

AUXILIARY DATA; AUXILIARY INFORMATION; CONDITIONAL INDEPENDENCE ASSUMPTION; COVARIATES; EM ALGORITHMS; JOINT MAXIMIZATION; LOGISTIC REGRESSION MODELING; MISSING COVARIATE, SURROGATE INFORMATION; PERFORMANCE; TWO STAGE DESIGNS;

MAXIMUM LIKELIHOOD ESTIMATION;

ALGORITHM; ARTICLE; CHILD; CHILD HEALTH CARE; DATA ANALYSIS; HEALTH CARE UTILIZATION; HUMAN; MAXIMUM LIKELIHOOD METHOD; MENTAL DISEASE; MENTAL HEALTH CARE; OUTCOMES RESEARCH; REGRESSION ANALYSIS; STATISTICAL ANALYSIS; STATISTICAL MODEL; STATISTICAL PARAMETERS;

EID: 0035102876     PISSN: 0006341X     EISSN: None     Source Type: Journal    
DOI: 10.1111/j.0006-341X.2001.00034.x     Document Type: Article

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