Description Usage Arguments Details Value Note Author(s) References See Also Examples
Compute global tests for factorial dose-response designs following Hung (2000) or by a bootstrap algorithm.
1 2 |
C |
An object of class |
test |
Either |
method |
The calculation method - use |
nboot |
The number of bootstrap iterations to use. |
simerror |
Prespecified simulation standard error. |
... |
Any further arguments. |
When handling with data from factorial clinical trial designs, one
is often interested in the question whether dose combinations in the trial
have got a better effect than all of their component drugs, because
regulatoric requirements demand a contribution to the efficacy by all components. The decision if any of the tested combination drugs has
got this property can be based on the AVE- or MAX-statistics
proposed by Hung, Chi and Lipicky (1993). The hypothesis that this is true for
none of the combinations is rejected if the largest or the average of
the min-statistics is sufficiently high. The functions
avetest
and maxtest
calculate the corresponding p-values
on carpet
or cube
objects with a new bootstrap
algorithm, which is default, or by the
multivariate method for unbalanced designs from Hung (2000). A
resampling-based method is available also for binary data
applications. The desired simulation accuracy always needs to be specified by the
number nboot
of simulations to perform or an upper bound
simerror
for the simulation standard error. If both are
given, the two constraints will be held simultaneously. 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.
An object of class avetest
or maxtext
, respectively, with the
following slots. The slot name
is available for the MAX-test only.
p |
p-value for the AVE- or MAX-test. |
stat |
Observed AVE- or MAX-statistic. |
test |
Type of test statistic which the AVE- or MAX-test was based on. |
method |
Algorithm used for the calculation. |
nboot |
Total number of resampling iterations. |
simerror |
Simulation standard error. |
name |
Combination group where the maximum of the min-statistics was observed. |
duration |
Total computing duration in seconds. |
call |
The function call. |
The performance of the bootstrap-based approach and the method from Hung (2000) has been compared and discussed. All algorithms perform very conservative if the means in the marginal treatment groups are close for the combinations.
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
Hellmich M, Lehmacher W (2005): Closure procedures for monotone bi-factorial dose-response designs. Biometrics 61, pp. 269-276
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
bifactorial
, carpet
, cube
, mintest
, margint
1 2 3 4 5 6 7 8 9 10 11 12 | #Hypertension example from Hung (2000)
n<-c(75,75,74,48,74,75,74,49,48,50,48,48)
m<-c(0,1.4,2.7,4.6,1.8,2.8,5.7,8.2,2.8,4.5,7.2,10.9)
s<-rep(7.07,12)
x<-list(12)
for(i in 1:12){
x[[i]]<-rnorm(n[i],mean=0,sd=1)
x[[i]]<-((x[[i]]-mean(x[[i]]))*(s[i]/sd(x[[i]])))+m[i]
}
hung<-carpet(x,D=c(2,3))
avetest(hung,test="ttest",nboot=20000)
maxtest(hung,test="ttest",nboot=20000)
|
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