Uncovering symptom progression history from disease registry data with application to young cystic fibrosis patients

Biometrics

Volumn 66, Issue 2, 2010, Pages 594-602

  Yan, Jun ^a   Cheng, Yu ^b   Fine, Jason P ^c   Lai, Huichuan J ^d  

^a UNIVERSITY OF CONNECTICUT   (United States)

^b UNIVERSITY OF PITTSBURGH SCHOOL OF MEDICINE   (United States)

^c The University of North Carolina at Chapel Hill   (United States)

^d UNIVERSITY OF WISCONSIN SCHOOL OF MEDICINE AND PUBLIC HEALTH   (United States)

Author keywords

Generalized linear model; Lung infection; Newborn screening and diagnosis; Prevalence; Pseudomonas aeruginosa; Varying coefficient

Indexed keywords

BACTERIA; HAZARDS; PATIENT MONITORING;

CYSTIC FIBROSIS; DATA ANALYSIS TECHNIQUES; GENERALIZED LINEAR MODEL; LUNG INFECTION; NATURAL HISTORY; NEWBORN SCREENING; NEWBORN SCREENING AND DIAGNOSE; PREVALENCE; PSEUDOMONAS AERUGINOSA; VARYING COEFFICIENTS;

DIAGNOSIS;

BACTERIUM; COHORT ANALYSIS; COVARIANCE ANALYSIS; DISEASE PREVALENCE; EPIDEMIOLOGY; HAZARD ASSESSMENT; INFECTIOUS DISEASE; RISK FACTOR; SYMPTOM;

PSEUDOMONAS; PSEUDOMONAS AERUGINOSA;

CYSTIC FIBROSIS; DISEASE COURSE; FETAL DISEASES; HUMAN; INFANT, NEWBORN, DISEASES; MICROBIOLOGY; NEWBORN; PRENATAL DIAGNOSIS; PSEUDOMONAS AERUGINOSA; PSEUDOMONAS INFECTIONS; REGISTER; STATISTICAL ANALYSIS; STATISTICAL MODEL; TIME;

CYSTIC FIBROSIS; DATA INTERPRETATION, STATISTICAL; DISEASE PROGRESSION; FETAL DISEASES; HUMANS; INFANT, NEWBORN; INFANT, NEWBORN, DISEASES; MODELS, STATISTICAL; PRENATAL DIAGNOSIS; PSEUDOMONAS AERUGINOSA; PSEUDOMONAS INFECTIONS; REGISTRIES; TIME FACTORS;

EID: 77953003142     PISSN: 0006341X     EISSN: 15410420     Source Type: Journal    
DOI: 10.1111/j.1541-0420.2009.01288.x     Document Type: Article

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