A spatial bivariate probit model for correlated binary data with application to adverse birth outcomes

Statistical Methods in Medical Research

Volumn 23, Issue 2, 2014, Pages 119-133

  Neelon, Brian ^a,b   Anthopolos, Rebecca ^b   Miranda, Marie Lynn ^c  

^a DUKE UNIVERSITY MEDICAL CENTER   (United States)

^b DUKE UNIVERSITY   (United States)

^c UNIVERSITY OF MICHIGAN   (United States)

Author keywords

Bayesian analysis; birth outcomes; bivariate conditionally autoregressive prior; bivariate probit model; multivariate spatial analysis

Indexed keywords

ADOLESCENT; ADULT; BAYES THEOREM; CONFERENCE PAPER; CONTROLLED STUDY; FEMALE; HUMAN; LOW BIRTH WEIGHT; MALE; MONTE CARLO METHOD; PREGNANCY OUTCOME; PREMATURE LABOR; PROBABILITY; SPATIAL ANALYSIS; UNITED STATES; YOUNG ADULT; ALGORITHM; BIOSTATISTICS; BIRTH CERTIFICATE; INFANT; INFANT MORTALITY; MULTIVARIATE ANALYSIS; NEWBORN; PREGNANCY; PREMATURE BIRTH; PROGNOSIS; STATISTICAL MODEL;

ADOLESCENT; ADULT; ALGORITHMS; BAYES THEOREM; BIOSTATISTICS; BIRTH CERTIFICATES; FEMALE; HUMANS; INFANT; INFANT MORTALITY; INFANT, LOW BIRTH WEIGHT; INFANT, NEWBORN; MALE; MARKOV CHAINS; MODELS, STATISTICAL; MONTE CARLO METHOD; MULTIVARIATE ANALYSIS; NORTH CAROLINA; PREGNANCY; PREMATURE BIRTH; PROGNOSIS; YOUNG ADULT;

EID: 84899107021     PISSN: 09622802     EISSN: 14770334     Source Type: Journal    
DOI: 10.1177/0962280212447149     Document Type: Conference Paper

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