binMto {binMto} | R Documentation |
Approximate simultaneous confidence intervals for many-to-one comparisons of proportions. The add-4, add-2, 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.)
## Default S3 method:
binMto(x, n, names = NULL,
base = 1, conf.level = 0.95, alternative = "two.sided",
method = "Add4", adj = "Dunnett", ...)
## S3 method for class 'formula'
binMto(formula, data, base=1, conf.level=0.95,
alternative="two.sided", method="Add4", adj="Dunnett", ...)
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": adding-4-method (Agresti and Caffo, 2000), conservative, recommended for small sample sizes, "Add2": adding-2-method (Brown and Li, 2005),less conservative, recommended for one-sided 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 Bonferroni-adjustment, 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 Add-4 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 many-to-one comparisons of binary proportions in randomized clinical trials, Journal of Biopharmaceutical Statistics 19(2):292-310.
# 1)Simultaneous CI for Dunnett contrasts for
# the example in Table 1 of Bretz F and Hothorn LA (2002):
# Detecting dose-response using contrasts: asymptotic
# power and sample size determination for binomial data.
# Statistics in Medicine 21, 3325-3335.
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) Berth-Jones, J., Todd, G., Hutchinson, P.E.,
# Thestrup-Pedersen, K., Vanhoutte, F.P. (2000):
# Treatment of Psoriasis with oral liarozole:
# a dose-ranging study.
# British Journal of Dermatology 143 (6), 1170-1176.
# 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.
# two-sided Add-4 95-percent confidence intervals:
binMto(x=c(2,6,4,13),
n=c(34,35,36,34),
names = c("Placebo","50mg","75mg","150mg") )