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.)
binMto(x, ...) ## 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:
|
adj |
character string, specifying the adjustment for multiplicity, one of:
|
... |
arguments to be passed to the methods binMto.formula and binMto.default |
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. Requires the package mvtnorm.
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
For the calculation of quantiles for many-to-one comparisons:
Piegorsch, W.W. (1991): Multiple comparisons for analyzing dichotomous response. Biometrics 47 (1), 45-52.
For the used interval methods (two-sample comparisons), see:
Agresti, A. and Caffo, B. (2000): Simple and effective confidence intervals for proportions and differences of proportions result from adding two successes and two failures. American Statistician 54 (4), 280-288. Brown, L. and Li, X. (2005): Confidence intervals for two sample binomial distribution. Journal of Statistical Planning and Inference 130, 359-375. Newcombe, R.G. (1998): Interval estimation for the difference between independent proportions: comparison of eleven methods. Statistics in Medicine 17, 873-890.
# 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) ) plot(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") ) plot(binMto(x=c(2,6,4,13), n=c(34,35,36,34), names = c("Placebo","50mg","75mg","150mg") )) # One-sided Add-4 95-percent confidence intervals # (for higher values in treatments than in control): binMto(x=c(2,6,4,13), n=c(34,35,36,34), names = c("Placebo","50mg","75mg","150mg"), alternative="greater" ) # Comparison with a 2x4 table Chisquare-Test: x<-c(2,6,4,13) n<-c(34,35,36,34) tab<-rbind(x, n-x) chisq.test(tab, correct=TRUE)