Package BayesNI. February 19, 2015
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1 Package BayesNI February 19, 2015 Type Package Title BayesNI: Bayesian Testing Procedure for Noninferiority with Binary Endpoints Version 0.1 Date Author Sujit K Ghosh, Muhtarjan Osman Maintainer Muhtarjan Osman <mjosman@gmail.com> Description A Bayesian testing procedure for noninferiority trials with binary endpoints. The prior is constructed based on Bernstein polynomials with options for both informative and non-informative prior. The critical value of the test statistic (Bayes factor) is determined by minimizing total weighted error (TWE) criteria License GPL-2 LazyLoad yes Repository CRAN Date/Publication :13:20 NeedsCompilation no R topics documented: BayesNI-package bayesni Index 5 1
2 2 BayesNI-package BayesNI-package Semiparametric Bayesian Noninfereiority Testing Description Details This package implements a Bayesian testing procedure for noninfeiority trials with binary endpoints. The prior is constructed based on Bernstein polynomials with options for both informative and non-informative prior. The critical value of the test statistic Bayes factor is determined by minimizing total weighted error criteria. Package: BayesNI Type: Package Version: 1.0 Date: License: GPL-2 LazyLoad: yes Author(s) Muhtarjan Osman and Sujit K. Ghosh Maintainer: Muhtarjan Osman <mjosman@gmail.com> References Osman M. and Ghosh S. K. (2011) Semiparametric Bayesian Testing Procedure for Non-inferiority Trials with Binary Endpoints. Journal of Biopharmaceutical Statistics, 21, See Also Examples bayesni(x1=97,x2=98,n1=107,n2=106, dm= OR,rho=0.5, m=20,zeta=0.025, TWE=1)
3 bayesni 3 bayesni Semiparametric Bayesian testing procedure for non-inferiority trials with binary endpoints Description This function calculates the Bayes factor and the cut-off value for noninfeiority trials with binary endpoints. The prior is constructed based on Bernstein polynomials with options for both informative and non-informative prior. The critical value of the test statistic (Bayes factor) is determined by minimizing total weighted error (TWE) criteria. Usage bayesni(x1, x2, n1, n2, dm = OR, rho, m = 10, noninform.prior = TRUE, w1, w2, TWE = 1, zeta = 0.5, plot.prior = FALSE) Arguments x1 The number of success events in the group 1 x2 The number of success events in the group 2 n1 The total number of subjects in the group 1 n2 The total number of subjects in the group 2 dm The dissimilarity measure of two binomial parameters: "RD" risk difference ; "RR" relative risk ; "OR" odds ratio (default value) rho m Noninferiority boundary The order of Bernstein polynomials (default value m=10) noninform.prior "TRUE" (default) for using noninformative prior to determines weights in the mixture prior; "FALSE" user-specified weights in the mixture prior based on prior information w1 w2 TWE zeta plot.prior If noninform.prior=false, a user-specified vector of weights for the prior of theta_1. The length of this vector should be m. If noninform.prior=false, a user-specified vector of weights for the prior of theta_2. The length of this vector should be m. 1 (default value): total weighted error conditioned on the hypotheses; 2 : total weighted error conditioned on the decisions The weight the total weighted criteria future functionality, under development.
4 4 bayesni Details Value This function gives the Bayes factor and the cut-off value for noninfeiority trials with binary endpoints. The prior is constructed based on Bernstein polynomials with options for both informative and non-informative prior. The weights w1 and w2 are not necessary when using non-informative prior. For informative prior, users can refer to the correspondering section in Osman and Ghosh (2012) on how to construct weights based on historical data. The Bayes factor computed in this function is define in favor of alternative hypothesis. A larger Bayes factor indicates stronger evidence against the null hypothesis. The critical value of the test statistic (Bayes factor) is determined by minimizing total weighted error (TWE) criteria. Users can refer to Theorem 1 in Osman and Ghosh (2012) for specification of the weight zeta. A common approach is to set zeta=alpha=0.05. logbf L0 w1 w2 the observed (log) Bayes factor the cut-off critical value of (log) Bayes factor for the rejection region the weights used in the prior of theta_1 the weights used in the prior of theta_2 Note Author(s) Muhtarjan Osman and Sujit K. Ghosh References Osman M. and Ghosh S. K. (2011) Semiparametric Bayesian Testing Procedure for Non-inferiority Trials with Binary Endpoints. Journal of Biopharmaceutical Statistics, 21, See Also Examples bayesni(x1=97,x2=98,n1=107,n2=106, dm= OR,rho=0.5, m=20,zeta=0.025, TWE=1)
5 Index Topic Bayesian Topic noninferiority, Bayes factor BayesNI-package, 2 Topic noninferiority BayesNI (BayesNI-package), 2 BayesNI-package, 2 5
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