Sensitivity analysis for unmeasured confounding in principal stratification settings with binary variables

Statistics in Medicine

Volumn 31, Issue 10, 2012, Pages 949-962

  Schwartz, Scott ^a   Li, Fan ^b   Reiter, Jerome P ^b  

^a TEXAS A AND M UNIVERSITY   (United States)

^b Duke University   (United States)

Author keywords

Causal; Confounding; Intermediate; Observational; Principal stratification; Sensitivity analysis

Indexed keywords

ARTICLE; BODY MASS; CARDIOVASCULAR DISEASE; CAUSAL MODELING; CONFOUNDING VARIABLE; HUMAN; OBSERVATIONAL STUDY; PHYSICAL ACTIVITY; SENSITIVITY ANALYSIS; STATISTICAL MODEL; STRATIFICATION; STRATIFIED SAMPLE; STUDY DESIGN; TREATMENT OUTCOME; TREATMENT RESPONSE;

COHORT STUDIES; HUMANS; MODELS, STATISTICAL; POPULATION DYNAMICS; QUESTIONNAIRES; RANDOMIZED CONTROLLED TRIALS AS TOPIC; TREATMENT OUTCOME;

EID: 84859710550     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.4472     Document Type: Article

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