Description Usage Arguments Details Value Author(s) References Examples
Approximate simultaneous confidence intervals for manytoone comparisons of proportions. The add4, add2, Newcombes Hybrid Score interval for the difference of proportions can be calculated using either quantiles of the multivariate normal distributrion (Dunnett) standard normal quantiles (Bonferroni or unadjusted.)
1 2 3 4 5 6 7 8 
x 
vector giving the number of success in the groups 
n 
vector giving the number of trials, i.e. the sample size of each group 
names 
(character)vector specifying the names of groups given in x and n, ignored if formula and data.frame are used 
formula 
a formula specifying a response and treatment variable like: response~treatment; the response must consist of 0,1 (failure and success) 
data 
data.frame containing the response and treatment variable specified in formula 
base 
a numeric value specifying which group to be treated as control group 
conf.level 
confidence level 
alternative 
character string, one of "two.sided", "less", "greater" 
method 
character string specifying the method of CI construction to used, one of: "Add4": adding4method (Agresti and Caffo, 2000), conservative, recommended for small sample sizes, "Add2": adding2method (Brown and Li, 2005),less conservative, recommended for onesided limits, "NHS": Newcombes Hybrid Score method (Newcombe, 1998), "Wald": Wald method, not recommended, only for large sample sizes and not too extreme proportions. 
adj 
character string, specifying the adjustment for multiplicity, one of: "Dunnett": Recommended, using quantiles of the multivariate normal distribution adjusting for multiplicity and correlation between comparisons depending on sample size and estimated proportion (Piegorsch, 1991), "Bonf": Simple Bonferroniadjustment, conseravtive for large number of comparisons, "Unadj": Unadjusted interval, i.e. each with local confidence level = conf.level 
... 
arguments to be passed to the methods 
All methods only asymptotically hold the nominal confidence level. Thus they can not be recommended if sample size is combined with extreme proportions of success (close to 0 or 1). Among the available methods Add4 is most appropriate for small sample sizes, if conservative performance is acceptable.
A list containing:
conf.int 
a matrix containg estimates, lower and upper confidence limits 
and further values specified in the function call, apply str() to the output for details
Frank Schaarschmidt
Schaarschmidt, F., Biesheuvel, E., Hothorn, L.A. (2009) Asymptotic simultaneous confidence intervals for manytoone comparisons of binary proportions in randomized clinical trials, Journal of Biopharmaceutical Statistics 19(2):292310.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33  # 1)Simultaneous CI for Dunnett contrasts for
# the example in Table 1 of Bretz F and Hothorn LA (2002):
# Detecting doseresponse using contrasts: asymptotic
# power and sample size determination for binomial data.
# Statistics in Medicine 21, 33253335.
binMto(x=c(9,19,21,21,24),
n=c(20,43,42,42,41),
names = c("Placebo", 0.125, 0.5, 0.75, 1) )
#########################################################
# 2) BerthJones, J., Todd, G., Hutchinson, P.E.,
# ThestrupPedersen, K., Vanhoutte, F.P. (2000):
# Treatment of Psoriasis with oral liarozole:
# a doseranging study.
# British Journal of Dermatology 143 (6), 11701176.
# Three doses of a compound (liarozole) were compared
# to a group treated with placebo. The primary variable
# was defined as the proportion of patients with an at
# least marked improvement of psoriasis symptoms.
# A total of 139 patients were assigned to the 4 treatment
# groups, sample sizes were 34,35,36,34, for the Placebo,
# 50mg, 75mg, and 150mg treatments, respectively.
# The number of patients with marked improvement of
# symptoms was 2,6,4,13 in the 4 treatment groups.
# twosided Add4 95percent confidence intervals:
binMto(x=c(2,6,4,13),
n=c(34,35,36,34),
names = c("Placebo","50mg","75mg","150mg") )

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