Bayesian approach to average power calculations for binary regression models with misclassified outcomes

Statistics in Medicine

Volumn 28, Issue 5, 2009, Pages 848-863

  Cheng, Dunlei ^a   Stamey, James D ^b   Branscum, Adam J ^c  

^a BAYLOR HEALTH CARE SYSTEM   (United States)

^b BAYLOR UNIVERSITY   (United States)

^c UNIVERSITY OF KENTUCKY   (United States)

Author keywords

Logistic regression; Misclassification; Simulation

Indexed keywords

ARTICLE; BAYES THEOREM; BINOMIAL DISTRIBUTION; CONTROLLED STUDY; DIAGNOSTIC ACCURACY; DIAGNOSTIC TEST; FEMALE; HUMAN; LOGISTIC REGRESSION ANALYSIS; MAJOR CLINICAL STUDY; MULTIVARIATE ANALYSIS OF COVARIANCE; PROBABILITY; REGRESSION ANALYSIS; RISK ASSESSMENT; SAMPLE SIZE; SIMULATION; VERRUCA VULGARIS; WART VIRUS; ALGORITHM; ANOVULATION; COMPUTER PROGRAM; COMPUTER SIMULATION; DIAGNOSTIC ERROR; DIAGNOSTIC PROCEDURE; EPIDEMIOLOGY; INTERNET; SMOKING; STATISTICAL MODEL; STATISTICS; VIRUS INFECTION;

ALGORITHMS; ANOVULATION; BAYES THEOREM; BIAS (EPIDEMIOLOGY); COMPUTER SIMULATION; DIAGNOSTIC ERRORS; DIAGNOSTIC TECHNIQUES AND PROCEDURES; FEMALE; HUMANS; INTERNET; LOGISTIC MODELS; PAPILLOMAVIRUS INFECTIONS; RISK ASSESSMENT; SAMPLE SIZE; SMOKING; SOFTWARE;

EID: 64549126940     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.3505     Document Type: Article

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