Missing covariates in longitudinal data with informative dropouts: Bias analysis and inference

Biometrics

Volumn 61, Issue 3, 2005, Pages 837-846

  Roy, Jason ^a   Lin, Xihong ^b  

^a UNIVERSITY OF ROCHESTER   (United States)

^b UNIVERSITY OF MICHIGAN SCHOOL OF PUBLIC HEALTH   (United States)

Author keywords

Asymptotic bias; EM algorithm; Missing data; Random effects; Sensitivity analysis; Transition model

Indexed keywords

INFERENCE ENGINES; RANDOM PROCESSES; STATISTICAL METHODS; VIRUSES;

ASYMPTOTIC BIAS; COVARIATES; DROP-OUT; EM ALGORITHMS; GENERALIZED LINEAR MIXED MODELS; LONGITUDINAL DATA; MISSING DATA; RANDOM EFFECTS; TIME VARYING; TRANSITION MODEL;

SENSITIVITY ANALYSIS;

STATISTICAL ANALYSIS;

ALGORITHM; ARTICLE; COVARIANCE; EPIDEMIOLOGY; OUTCOMES RESEARCH; STATISTICAL ANALYSIS; STATISTICAL MODEL; STATISTICAL PARAMETERS; CD4 LYMPHOCYTE COUNT; COMPUTER SIMULATION; FEMALE; HOSPITALIZATION; HUMAN; HUMAN IMMUNODEFICIENCY VIRUS INFECTION; LONGITUDINAL STUDY; PATIENT;

HUMAN IMMUNODEFICIENCY VIRUS;

PROTEINASE INHIBITOR;

ALGORITHMS; BIAS (EPIDEMIOLOGY); CD4 LYMPHOCYTE COUNT; COMPUTER SIMULATION; FEMALE; HIV INFECTIONS; HIV PROTEASE INHIBITORS; HOSPITALIZATION; HUMANS; LINEAR MODELS; LONGITUDINAL STUDIES; PATIENT DROPOUTS;

EID: 27744463539     PISSN: 0006341X     EISSN: None     Source Type: Journal    
DOI: 10.1111/j.1541-0420.2005.00340.x     Document Type: Article

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