## File: samplesize.bin.Rd

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hmisc 4.2-0-1
 12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455565758596061626364 \name{samplesize.bin} \alias{samplesize.bin} \title{ Sample Size for 2-sample Binomial } \description{ Computes sample size(s) for 2-sample binomial problem given vector or scalar probabilities in the two groups. } \usage{ samplesize.bin(alpha, beta, pit, pic, rho=0.5) } \arguments{ \item{alpha}{ scalar ONE-SIDED test size, or two-sided size/2 } \item{beta}{ scalar or vector of powers } \item{pit}{ hypothesized treatment probability of success } \item{pic}{ hypothesized control probability of success } \item{rho}{ proportion of the sample devoted to treated group (\eqn{0 <\code{rho} < 1}) } } \value{ TOTAL sample size(s) } \section{AUTHOR}{ Rick Chappell\cr Dept. of Statistics and Human Oncology\cr University of Wisconsin at Madison\cr \email{chappell@stat.wisc.edu} } \examples{ alpha <- .05 beta <- c(.70,.80,.90,.95) # N1 is a matrix of total sample sizes whose # rows vary by hypothesized treatment success probability and # columns vary by power # See Meinert's book for formulae. N1 <- samplesize.bin(alpha, beta, pit=.55, pic=.5) N1 <- rbind(N1, samplesize.bin(alpha, beta, pit=.60, pic=.5)) N1 <- rbind(N1, samplesize.bin(alpha, beta, pit=.65, pic=.5)) N1 <- rbind(N1, samplesize.bin(alpha, beta, pit=.70, pic=.5)) attr(N1,"dimnames") <- NULL #Accounting for 5\% noncompliance in the treated group inflation <- (1/.95)**2 print(round(N1*inflation+.5,0)) } \keyword{htest} \keyword{category} \concept{study design} \concept{power}