Package RI2by2. October 21, 2016
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1 Package RI2by2 October 21, 2016 Type Package Title Randomization Inference for Treatment Effects on a Binary Outcome Version 1.3 Author Joseph Rigdon, Wen Wei Loh, Michael G. Hudgens Maintainer Wen Wei Loh <wwloh@ .unc.edu> Imports compiler, gtools, Rcpp LinkingTo Rcpp Computes attributable effects based confidence, permutation test confidence, or asymptotic confidence for the the average treatment effect on a binary outcome. License GPL (>= 3) Suggests testthat NeedsCompilation yes Repository CRAN Date/Publication :42:34 R topics documented: AE.CI Perm.CI Perm.CI.RLH Robins.CI Index 7 1
2 2 AE.CI AE.CI Attributable effects based confidence for a treatment effect on a binary outcome Computes the attributable effects based confidence for the average treatment effect on a binary outcome in an experiment where m of n individuals are randomized to treatment by design. AE.CI(, ) Details The attributable effects based confidence from inverting n + 2 hypothesis tests. lower upper lower bound of confidence upper bound of confidence Joseph Rigdon <jrigdon@stanford.edu> Rigdon, J.R. and Hudgens, M.G. (2015). Randomization inference for treatment effects on a binary outcome. Statistics in Medicine, 34(6), ex = matrix(c(8,2,3,7),2,2,byrow=true) AE.CI(ex,0.05)
3 Perm.CI 3 Perm.CI Permutation test confidence for a treatment effect on a binary outcome Computes permutation-based confidence s for the average treatment effect on a binary outcome in an experiment where m of n individuals are randomized to treatment by design. Perm.CI(,, nperm) nperm number of randomizations to perform for each hypothesis test Details The permutation confidence results from inverting O(n 4 ) hypothesis tests where n is the total number of observations in the observed 2 by 2 table. For each hypothesis test, if ( n m) is less than or equal to nperm, ( ( n m) randomizations are performed, but if n m) is greater than nperm, a random sample with replacement of nperm randomizations are performed. lower upper lower bound of confidence upper bound of confidence Joseph Rigdon <jrigdon@stanford.edu> Rigdon, J.R. and Hudgens, M.G. (2015). Randomization inference for treatment effects on a binary outcome. Statistics in Medicine, 34(6), ex = matrix(c(8,2,3,7),2,2,byrow=true) Perm.CI(ex,0.05,100)
4 4 Perm.CI.RLH Perm.CI.RLH Permutation test confidence for a treatment effect on a binary outcome Computes permutation-based confidence s for the average treatment effect on a binary outcome in an experiment where m of n individuals are randomized to treatment by design. This function is based on the modified approach (RLH) in Rigdon, Loh and Hudgens (forthcoming). The Chiba (2015) and Blaker (2000) s are also returned. There is an additional option of specifying the maximum number of hypothesis tests to be carried out. Perm.CI.RLH(,, verbose=false, total_tests=na) verbose If TRUE, returns an additional frame listing all the values of (n 11, n 10, n 01, n 00 ) tested, and the corresponding p-values; default = FALSE. total_tests maximum number of hypotheses to be tested in total, with a minimum of two for each possible value of (n 10 n 01 )/n; default = NA. By default, all hypotheses are evaluated until the minimum and maximum values of (n 10 n 01 )/n with p-values (or /2 for the Chiba s) are found. A list with the following items: Chiba RLH Blaker p_values Chiba confidence RLH confidence Blaker confidence if verbose=true, a frame with all the p-values from the hypothesis tests; default=false Wen Wei Loh <wwloh@ .unc.edu>
5 Robins.CI 5 Rigdon, J.R. and Hudgens, M.G. (2015). Randomization inference for treatment effects on a binary outcome. Statistics in Medicine, 34(6), Chiba, Y. (2015). Exact tests for the weak causal null hypothesis on a binary outcome in randomized trials. Journal of Biometrics & Biostatistics, 6(244). Chiba, Y. (2016). A note on exact confidence for causal effects on a binary outcome in randomized trials. Statistics in Medicine, 35(10), Blaker, H. (2000). Confidence curves and improved exact confidence s for discrete distributions. Canadian Journal of Statistics, 28(4), Rigdon, J.R., Loh W.W. and Hudgens, M.G. (forthcoming). Response to comment on "Randomization inference for treatment effects on a binary outcome." ex = matrix(c(11,1,7,21),2,2,byrow=true) Perm.CI.RLH(ex,0.05) ex = matrix(c(7,5,1,27),2,2,byrow=true) Perm.CI.RLH(ex,0.05) Perm.CI.RLH(ex,0.05, verbose=true) ex = matrix(c(33,15,11,37),2,2,byrow=true) Perm.CI.RLH(ex,0.05, total_tests=1000) Perm.CI.RLH(ex,0.05) ex = matrix(c(40,60,15,85),2,2,byrow=true) Perm.CI.RLH(ex,0.05, total_tests=1000) ## Not run: Perm.CI.RLH(ex,0.05) Robins.CI Asymptotic confidence for a treatment effect on a binary outcome Computes the Robins (1988) confidence for the average treatment effect on a binary outcome in an experiment where m of n individuals are randomized to treatment by design. Robins.CI(, )
6 6 Robins.CI Details The Robins (1988) confidence is similar in form to the well known Wald confidence for a difference in proportions, but is guaranteed to have smaller width. lower upper lower bound of confidence upper bound of confidence Joseph Rigdon <jrigdon@stanford.edu> Robins, J.M. (1988). Confidence s for causal parameters. Statistics in Medicine, 7(7), #Example 1 from Robins (1988) ex = matrix(c(40,60,15,85),2,2,byrow=true) Robins.CI(ex,0.05)
7 Index Topic attributable effects AE.CI, 2 Topic permutation test Perm.CI, 3 Topic randomization inference AE.CI, 2 Perm.CI, 3 Robins.CI, 5 AE.CI, 2 Perm.CI, 3 Perm.CI.RLH, 4 Robins.CI, 5 7
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