A general binomial regression model to estimate standardized risk differences from binary response data

Statistics in Medicine

Volumn 32, Issue 5, 2013, Pages 808-821

  Kovalchik, Stephanie A ^a   Varadhan, Ravi ^b   Fetterman, Barbara ^c   Poitras, Nancy E ^c   Wacholder, Sholom ^a   Katki, Hormuzd A ^a  

^a NATIONAL CANCER INSTITUTE   (United States)

^b JOHNS HOPKINS UNIVERSITY SCHOOL OF MEDICINE   (United States)

^c KAISER PERMANENTE   (United States)

Author keywords

Absolute risk; Binomial regression; Constrained maximization; Logistic regression; Risk difference

Indexed keywords

ALGORITHM; ARTICLE; CANCER RISK; DATA ANALYSIS; DISEASE ASSOCIATION; FEASIBILITY STUDY; HEALTH PROGRAM; HUMAN; LINEAR SYSTEM; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; MAXIMUM LIKELIHOOD METHOD; NONLINEAR SYSTEM; OUTCOME ASSESSMENT; PAPANICOLAOU TEST; PAPILLOMA VIRUS; PRACTICE GUIDELINE; PRECANCER; PROBABILITY; PROCESS DEVELOPMENT; PROCESS MODEL; RATING SCALE; REGRESSION ANALYSIS; RISK ASSESSMENT; RISK FACTOR; SCREENING TEST; STANDARDIZATION; UTERINE CERVIX CANCER; VIRUS IDENTIFICATION;

ADULT; ALGORITHMS; BIOSTATISTICS; CASE-CONTROL STUDIES; CERVICAL INTRAEPITHELIAL NEOPLASIA; FEMALE; HUMAN PAPILLOMAVIRUS DNA TESTS; HUMANS; LIKELIHOOD FUNCTIONS; LINEAR MODELS; MIDDLE AGED; MODELS, STATISTICAL; RISK FACTORS; SOFTWARE; UTERINE CERVICAL NEOPLASMS; VAGINAL SMEARS;

EID: 84873446436     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.5553     Document Type: Article

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