Regularized regression versus the high-dimensional propensity score for confounding adjustment in secondary database analyses

American Journal of Epidemiology

Volumn 182, Issue 7, 2015, Pages 651-659

  Franklin, Jessica M ^a   Eddings, Wesley ^a   Glynn, Robert J ^a   Schneeweiss, Sebastian ^a  

^a BRIGHAM AND WOMEN S HOSPITAL   (United States)

Author keywords

bias; confounding factors; epidemiologic methods; lasso; propensity score; simulation; variable selection

Indexed keywords

ANTICONVULSIVE AGENT; NONSTEROID ANTIINFLAMMATORY AGENT;

ALGORITHM; DATABASE; EPIDEMIOLOGY; NUMERICAL METHOD; NUMERICAL MODEL; REGRESSION ANALYSIS; SIMULATION; THEORETICAL STUDY;

ALGORITHM; ARTICLE; ATTRIBUTABLE RISK; COHORT ANALYSIS; CONTROLLED STUDY; EMPIRICAL RESEARCH; HUMAN; MAXIMUM LIKELIHOOD METHOD; OUTCOME ASSESSMENT; PROPENSITY SCORE; RISK FACTOR; SECONDARY ANALYSIS; SIMULATION; COMPARATIVE STUDY; EPIDEMIOLOGY; REGRESSION ANALYSIS;

ALGORITHMS; CONFOUNDING FACTORS (EPIDEMIOLOGY); PROPENSITY SCORE; REGRESSION ANALYSIS;

EID: 84943555084     PISSN: 00029262     EISSN: 14766256     Source Type: Journal    
DOI: 10.1093/aje/kwv108     Document Type: Article

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