Description Usage Arguments Details Value Source References See Also Examples
Computation of simultaneous confidence intervals for commonly used parametric multiple testing procedures (singlestep and stepdown Dunnett procedures).
1  parci(stat, n, est, stderror, covprob, proc)

stat 
Vector of test statistics. 
n 
Common sample size in each treatment group. 
est 
Vector of point estimates. 
stderror 
Vector of standard errors associated with the point estimates. 
covprob 
Simultaneous coverage probability (default is 0.975). 
proc 
Vector of character strings containing the procedure
name. This vector should include any of the following:

This function computes lower onesided simultaneous confidence limits for the singlestep Dunnett procedure (Dunnett, 1955) and stepdown Dunnett procedure (Naik, 1975; Marcus, Peritz and Gabriel, 1976) in onesided hypothesis testing problems with a balanced oneway layout and equally weighted null hypotheses.
The simultaneous confidence intervals are computed using the methods developed in Bofinger (1987) and Stefansson, Kim and Hsu (1988). For more information on the algorithms used in the function, see Dmitrienko et al. (2009, Section 2.7).
A data frame result
with columns for the test statistics, point estimates,
standard errors, adjusted pvalues, and lower simultaneous confidence limits
for the specified procedure.
http://multxpert.com/wiki/MultXpert_package
Bofinger, E. (1987). Stepdown procedures for comparison with a control.
Australian Journal of Statistics. 29, 348–364.
Dmitrienko, A., Bretz, F., Westfall, P.H., Troendle, J., Wiens, B.L.,
Tamhane, A.C., Hsu, J.C. (2009). Multiple testing methodology.
Multiple Testing Problems in Pharmaceutical Statistics.
Dmitrienko, A., Tamhane, A.C., Bretz, F. (editors). Chapman and
Hall/CRC Press, New York.
Dunnett, C.W. (1955). A multiple comparison procedure for
comparing several treatments with a control. Journal of the American
Statistical Association. 50, 1096–1121.
Marcus, R. Peritz, E., Gabriel, K.R. (1976). On closed testing
procedures with special reference to ordered analysis of variance.
Biometrika. 63, 655–660.
Naik, U.D. (1975). Some selection rules for comparing p processes
with a standard. Communications in Statistics. Series A.
4, 519–535.
Stefansson, G., Kim, W.C., Hsu, J.C. (1988). On confidence sets in multiple comparisons. Statistical Decision Theory and Related Topics IV. Gupta, S.S., Berger, J.O. (editors). Academic Press, New York, 89–104.
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  # Consider a clinical trial conducted to evaluate the effect of three
# doses of a treatment compared to a placebo with respect to a normally
# distributed endpoint
# Three null hypotheses of no effect are tested in the trial:
# Null hypothesis H1: No difference between Dose 1 and Placebo
# Null hypothesis H2: No difference between Dose 2 and Placebo
# Null hypothesis H3: No difference between Dose 3 and Placebo
# Treatment effect estimates (mean doseplacebo differences)
est<c(2.3,2.5,1.9)
# Pooled standard deviation
sd<9.5
# Study design is balanced with 180 patients per treatment arm
n<180
# Standard errors
stderror<rep(sd*sqrt(2/n),3)
# Tstatistics associated with the three doseplacebo tests
stat<est/stderror
# Compute lower onesided simultaneous confidence limits
# for the singlestep Dunnett procedure
parci(stat,n,est,stderror,covprob=0.975,proc="Singlestep Dunnett")
# Compute lower onesided simultaneous confidence limits
# for the singlestep and stepdown Dunnett procedures
parci(stat,n,est,stderror,covprob=0.975,proc=c("Singlestep Dunnett", "Stepdown Dunnett"))

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