Description Usage Arguments Details Value Note Author(s) References See Also Examples
Compute adjusted p-values and simultaneous confidence intervals for given bi- and trifactorial design data.
1 2 3 |
C |
An object of class |
method |
The calculation method - use |
test |
Either |
alpha |
Simultaneous level of the confidence intervals. |
nboot |
Number of resampling iterations to use. |
simerror |
Prespecified simulation standard error. |
... |
Any further arguments. |
The generic functions mintest
and margint
calculate
adjusted p-values and simultaneous confidence intervals for the test
of parametric differences between prespecified treatment groups on bi-
or trifactorial design clinical trials. If an object of class
carpet
is commited, mintest
will return adjusted
p-values for the min-test on combination superiority in bifactorial
clinical trial designs (Laska and Meisner, 1989). The alternative
hypothesis of this test is that the detected effect size for the
combination treatment is better than for both single component groups;
i.e. the test results in only one p-value for each combination. The
generic function margint
will, when applied to carpet
objects, return simultaneous confidence intervals for the parametric
differences between each combination treatment group and its
respective components. Depending on the type of data, the calculations
can be based on Student's t-test for metric data or the Z-statistic
for binary applications.
By default, the calculations are performed by a resampling-based
approach. The desired simulation accuracy always needs to be specified
by the number nboot
of bootstrap iterations to perform or an
upper bound simerror
for the simulation standard error. If both
are given, the two constraints will be held simultaneously. On the
other hand, the multivariate normal approach for unbalanced designs
from Hung (2000) is available when the argument method
is set
to the value "hung"
. For the trifactorial case, no such
approach is available and thus the calculations are based on the
bootstrap approach, performing a generalized min-test on the data, if
an object of class cube
is commited. The interval calculations
are based on the multivariate t-distribution if "tdistr"
is
specified.
In the classical approach to the min-test, a normality assumption for the data is used and the desired critical values are calculated using quantiles of the multivariate t-distribution. However, this method fails when handling with data that are skewed or heteroscedastic over the treatment groups. When using the bootstrap, only the empirical distribution of the data is used and thus the results are always valid, provided that a sufficiently large samples are available. When handling with data from bifactorial clinical trial designs, bootstrap methods need much less analytical framework on the distributional properties of the tests than if the approach given by Hung (2000) is used. In particular, the restriction to only two compounds is not needed and binary data applications can be handled analogously. The theory of resampling-based multiple testing has been extensively discussed by Westfall and Young (1993).
The calculation of simultaneous confidence intervals is much easier because the c.d.f. of the min-statistic is not needed. Hence this is leading to an ordinary multiple contrast problem.
An object of class mintest
or margint
with the following slots.
p |
Adjusted p-values for the respective combination groups. |
stat |
The observed values of the min-statistics. |
kiu |
The lower limits of the confidence intervals. |
kio |
The upper limits of the confidence intervals. |
alpha |
One minus the nominal coverage probability of the confidence intervals. |
gnames |
Names of the combination groups. |
cnames |
The names of the contrasts for comparisons of the combinations with their respective components. |
test |
Type of test statistics that the min-tests were based on. |
method |
The method used for calculation. |
nboot |
Number of bootstrap replications used. |
simerror |
Maximum of the simulation standard errors in the combination groups. |
duration |
Total computing duration in seconds. |
call |
Function call. |
Performance of the implemented methods has been evaluated and compared. The min-test performs very conservative if the means in the marginal treatment groups are close for a combination.
Peter Frommolt, University of Cologne peter.frommolt@uni-koeln.de
http://portal.ccg.uni-koeln.de
Frommolt P, Hellmich M (2009): Resampling in multiple-dose factorial designs. Biometrical J 51(6), pp. 915-31
Hung HMJ, Chi GYH, Lipicky RJ (1993): Testing for the existence of a desirable dose combination. Biometrics 49, pp. 85-94
Hung HMJ, Wang SJ (1997): Large-sample tests for binary outcomes in fixed-dose combination drug studies. Biometrics 53, pp. 498-503
Hung HMJ (2000): Evaluation of a combination drug with multiple doses in unbalanced factorial design clinical trials. Statist Med 19, pp. 2079-2087
Hellmich M, Lehmacher W (2005): Closure procedures for monotone bi-factorial dose-response designs. Biometrics 61, pp. 269-276
Laska EM, Meisner MJ (1989): Testing whether an identified treatment is best. Biometrics 45, pp. 1139-1151
Snapinn SM (1987): Evaluating the efficacy of a combination therapy. Statist Med 6, pp. 657-665
Westfall PH, Young SS (1993): Resampling-based multiple testing. John Wiley & Sons, Inc., New York
bifactorial
, carpet
, cube
, avetest
, maxtest
,
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | #AML example from Huang et al. (2007) with data from
#Issa et al. (2004) and Petersdorf et al. (2007)
n<-c(10,31,17,100,50,50,101,50,50)
p<-c(0.00,0.45,0.65,0.30,0.71,0.70,0.59,0.64,0.75)
y<-list()
for(i in 1:9){
y[[i]]<-0
while((sum(y[[i]])!=round(n[i]*p[i]))||(length(y[[i]])==1)){
y[[i]]<-rbinom(n[i],1,p[i])
}
}
aml<-carpet(data=y,D=c(2,2))
mintest(aml,test="ztest",nboot=25000)
margint(aml,test="ztest",nboot=25000)
|
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