Test: Test object

Description

This function creates an object of class Test which can be added to an object of class AnalysisModel.

Usage

Test(id, method, samples, par = NULL)

Arguments

defines the ID of the Test object.

method

defines the method of the Test object.

samples

defines a list of samples defined in the data model to be used within the selected Test object method.

par

defines the parameter(s) of the selected Test object method.

Details

Objects of class Test are used in objects of class AnalysisModel to define the statistical test to produce. Several objects of class Test can be added to an object of class AnalysisModel.

method argument defines the statistical test method. Several methods are already implemented in the Mediana package (listed below, along with the required parameters to define in the par parameter):

TTest: perform a two-sample t-test between the two samples defined in the samples argument. Optional parameter: larger (Larger value is expected in the second sample (TRUE or FALSE)). Two samples must be defined.
TTestNI: perform a non-inferiority two-sample t-test between the two samples defined in the samples argument. Required parameter: margin. Optional parameter: larger (Larger value is expected in the second sample (TRUE or FALSE)).Two samples must be defined.
WilcoxTest: perform a Wilcoxon-Mann-Whitney test between the two samples defined in the samples argument. Optional parameter: larger (Larger value is expected in the second sample (TRUE or FALSE)).Two samples must be defined.
PropTest: perform a two-sample test for proportions between the two samples defined in the samples argument. Optional parameter: yates (Yates' continuity correction TRUE or FALSE) and larger (Larger value is expected in the second sample (TRUE or FALSE)). Two samples must be defined.
PropTestNI: perform a non-inferiority two-sample test for proportions between the two samples defined in the samples argument. Required parameter: margin. Optional parameter: yates (Yates' continuity correction TRUE or FALSE) and larger (Larger value is expected in the second sample (TRUE or FALSE)). Two samples must be defined.
FisherTest: perform a Fisher exact test between the two samples defined in the samples argument. Optional parameter: larger (Larger value is expected in the second sample (TRUE or FALSE)). Two samples must be defined.
GLMPoissonTest: perform a Poisson regression test between the two samples defined in the samples argument. Optional parameter: larger (Larger value is expected in the second sample (TRUE or FALSE)). Two samples must be defined.
GLMNegBinomTest: perform a Negative-binomial regression test between the two samples defined in the samples argument. Optional parameter: larger (Larger value is expected in the second sample (TRUE or FALSE)).Two samples must be defined.
LogrankTest: perform a Log-rank test between the two samples defined in the samples argument. Optional parameter: larger (Larger value is expected in the second sample (TRUE or FALSE)). Two samples must be defined.
OrdinalLogisticRegTest: perform an Ordinal logistic regression test between the two samples defined in the samples argument. Optional parameter: larger (Larger value is expected in the second sample (TRUE or FALSE)). Two samples must be defined.

It is to be noted that the statistical tests implemented are one-sided and thus the sample order in the samples argument is important. In particular, the Mediana package assumes by default that a numerically larger value of the endpoint is expected in Sample 2 compared to Sample 1. Suppose, for example, that a higher treatment response indicates a beneficial effect (e.g., higher improvement rate). In this case Sample 1 should include control patients whereas Sample 2 should include patients allocated to the experimental treatment arm. The sample order needs to be reversed if a beneficial treatment effect is associated with a lower value of the endpoint (e.g., lower blood pressure), or alternatively (from version 1.0.6), the optional parameters larger must be set to FALSE to indicate that a larger value is expected on the first Sample.

References

http://gpaux.github.io/Mediana/

Examples

Run this code

# NOT RUN {
# Analysis model
analysis.model = AnalysisModel() +
                 Test(id = "Placebo vs treatment",
                      samples = samples("Placebo", "Treatment"),
                      method = "TTest")
# }