SimComp (version 1.4.5)

SimComp-package: Simultaneous Comparisons for Multiple Endpoints

Description

This package provides simultaneous tests and confidence intervals for one-way experimental designs with one or many normally distributed, primary response variables (endpoints). Means of several groups or dose levels can be compared

• by arbitrary contrasts, like the Dunnett or the Tukey test,
• for balanced or unbalanced sample sizes,
• for a single endpoint or for many endpoints simultaneously,
• for homogeneous or heterogeneous variances/ covariance matrices of the groups, and
• in terms of differences or ratios.

Exact or approximate multivariate t-distributions, respectively, are used for quantiles or p-values. For example, the well-known conventional all-pair comparison of Tukey (1953) can be performed by specifying only a single endpoint and homogeneous (co-)variances. On the other hand, it's also possible to do the same, but for many endpoints simultaneously, with heterogeneous covariance matrices and in terms of ratios. For multiple comparisons of means of heteroscedastic data, see Hasler and Hothorn (2008). The test procedure for multiple endpoints is described by Hasler and Hothorn (2011).

Details

ll{ Package: SimComp Type: Package Version: 1.4.5 Date: 2010-12-16 License: GPL LazyLoad: yes } Index:

• SimCiDiff: Simultaneous Confidence Intervals for Differences of Means of Multiple Endpoints
• SimCiRat: Simultaneous Confidence Intervals for Ratios of Means of Multiple Endpoints
• SimTestDiff: Simultaneous Tests for Differences of Means of Multiple Endpoints
• SimTestRat: Simultaneous Tests for Ratios of Means of Multiple Endpoints
• coagulation: Data from a clinical study of three sets of extracorporeal circulation in heart-lung machines
• ermvnorm: Multivariate Normal Random Numbers with Exact Parameters

References

Hasler, M. and Hothorn, L.A. (2011): A Dunnett-type procedure for multiple endpoints. The International Journal of Biostatistics. Hasler, M. and Hothorn, L.A. (2008): Multiple contrast tests in the presence of heteroscedasticity. Biometrical Journal 50, 793-800. Dilba, G. et al. (2006): Simultaneous confidence sets and confidence intervals for multiple ratios. Journal of Statistical Planning and Inference 136, 2640-2658.

See Also

mratios

Examples

# Example 1:
# A Dunnett-test for the groups B and H against the standard S, on
# the (single) endpoint Thromb.count, assuming unequal variances for
# the groups. This is the well-known Dunnett-test but in the
# presence of heteroscedasticity.

data(coagulation)

comp1 <- SimTestDiff(data=coagulation, grp="Group", resp="Thromb.count", type="Dunnett",
  base=3, alternative="greater", covar.equal=FALSE)
comp1

# Example 2:
# A Dunnett-test for the groups B and H against the standard S,
# simultaneously on all endpoints, assuming unequal covariance
# matrices for the groups.

data(coagulation)

comp2 <- SimTestDiff(data=coagulation, grp="Group", resp=c("Thromb.count","ADP","TRAP"), type="Dunnett",
  base=3, alternative="greater", covar.equal=FALSE)
summary(comp2)