SimComp-package: Simultaneous Comparisons for Multiple Endpoints

In SimComp: Simultaneous Comparisons for Multiple Endpoints

Description

Simultaneous tests and confidence intervals are provided for one-way experimental designs with one or many normally distributed, primary response variables (endpoints). Differences (Hasler and Hothorn, 2011 <doi:10.2202/1557-4679.1258>) or ratios (Hasler and Hothorn, 2012 <doi:10.1080/19466315.2011.633868>) of means can be considered. Various contrasts can be chosen, unbalanced sample sizes are allowed as well as heterogeneous variances (Hasler and Hothorn, 2008 <doi:10.1002/bimj.200710466>) or covariance matrices (Hasler, 2014 <doi:10.1515/ijb-2012-0015>).

Details

The DESCRIPTION file: This package was not yet installed at build time.

Index: This package was not yet installed at build time.

Author(s)

Mario Hasler, Christof Kluss

Maintainer: Mario Hasler <[e-mail]>

Thanks to: Frank Schaarschmidt, Gemechis Djira Dilba, Kornelius Rohmeyer

References

Hasler, M. and Hothorn, L.A. (2018): Multi-arm trials with multiple primary endpoints and missing values. Statistics in Medicine 37, 710–721, <doi:10.1002/sim.7542>.

Hasler, M. (2014): Multiple contrast tests for multiple endpoints in the presence of heteroscedasticity. The International Journal of Biostatistics 10, 17–28, <doi:10.1515/ijb-2012-0015>.

Hasler, M. and Hothorn, L.A. (2012): A multivariate Williams-type trend procedure. Statistics in Biopharmaceutical Research 4, 57–65, <doi:10.1080/19466315.2011.633868>.

Hasler, M. and Hothorn, L.A. (2011): A Dunnett-type procedure for multiple endpoints. The International Journal of Biostatistics 7, Article 3, <doi:10.2202/1557-4679.1258>.

Hasler, M. and Hothorn, L.A. (2008): Multiple contrast tests in the presence of heteroscedasticity. Biometrical Journal 50, 793–800, <doi:10.1002/bimj.200710466>.

Dilba, G. et al. (2006): Simultaneous confidence sets and confidence intervals for multiple ratios. Journal of Statistical Planning and Inference 136, 2640–2658, <doi:10.1016/j.jspi.2004.11.009>.

See Also

multcomp, mratios

Examples

# Example 1:
# A comparison of the groups B and H against the standard S, for endpoint
# Thromb.count, assuming unequal variances for the groups. This is an
# extension of the well-known Dunnett-test to the case 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 comparison of the groups B and H against the standard S, simultaneously
# for all endpoints, assuming unequal covariance matrices for the groups. This is
# an extension of the well-known Dunnett-test to the case of heteroscedasticity
# and multiple endpoints.

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)

Example output

 
Test for differences of means of multiple endpoints 
Assumption: Heterogeneous covariance matrices for the groups 
Alternative hypotheses: True differences greater than the margins 
 
      comparison     endpoint margin estimate statistic degr.fr p.value.raw
B - S      B - S Thromb.count      0   0.1217    1.3327   17.95      0.1001
H - S      H - S Thromb.count      0   0.0435    0.4244   17.67      0.3383
      p.value.adj
B - S      0.1778
H - S      0.5224
 
 
Contrast matrix: 

	 Multiple Comparisons of Means: Dunnett Contrasts

      B H  S
B - S 1 0 -1
H - S 0 1 -1
 
Estimated covariance matrices of the data: 
$B
             Thromb.count    ADP    TRAP
Thromb.count       0.0626 0.0565 -0.0102
ADP                0.0565 0.0638  0.0054
TRAP              -0.0102 0.0054  0.0963

$H
             Thromb.count    ADP   TRAP
Thromb.count       0.0943 0.0637 0.0663
ADP                0.0637 0.0518 0.0446
TRAP               0.0663 0.0446 0.1157

$S
             Thromb.count    ADP   TRAP
Thromb.count       0.0318 0.0132 0.0598
ADP                0.0132 0.0079 0.0269
TRAP               0.0598 0.0269 0.1376

 
Estimated correlation matrices of the data: 
$B
             Thromb.count    ADP    TRAP
Thromb.count       1.0000 0.8937 -0.1314
ADP                0.8937 1.0000  0.0687
TRAP              -0.1314 0.0687  1.0000

$H
             Thromb.count    ADP   TRAP
Thromb.count       1.0000 0.9121 0.6348
ADP                0.9121 1.0000 0.5770
TRAP               0.6348 0.5770 1.0000

$S
             Thromb.count    ADP   TRAP
Thromb.count       1.0000 0.8338 0.9033
ADP                0.8338 1.0000 0.8161
TRAP               0.9033 0.8161 1.0000

 
Estimated correlation matrix of the comparisons: 
             Thromb.count    ADP   TRAP Thromb.count    ADP   TRAP
Thromb.count       1.0000 0.8494 0.3122       0.2833 0.1708 0.3755
ADP                0.8494 1.0000 0.2387       0.1335 0.1158 0.1917
TRAP               0.3122 0.2387 1.0000       0.3417 0.2232 0.5550
Thromb.count       0.2833 0.1335 0.3417       1.0000 0.8869 0.7054
ADP                0.1708 0.1158 0.2232       0.8869 1.0000 0.5818
TRAP               0.3755 0.1917 0.5550       0.7054 0.5818 1.0000
 
Alternative hypotheses: True differences greater than the margins 
 
  comparison     endpoint margin estimate statistic degr.fr p.value.raw
1      B - S Thromb.count      0   0.1217    1.3327   12.25      0.1001
2      B - S          ADP      0   0.2121    2.6398   12.25      0.0108
3      B - S         TRAP      0   0.1053    0.7402   12.25      0.2339
4      H - S Thromb.count      0   0.0435    0.4244   14.27      0.3383
5      H - S          ADP      0   0.0842    1.1949   14.27      0.1260
6      H - S         TRAP      0   0.0711    0.4894   14.27      0.3148
  p.value.adj
1      0.3204
2      0.0431
3      0.5877
4      0.7294
5      0.3749
6      0.7018
 

SimComp documentation built on Aug. 26, 2019, 5:03 p.m.