Return a message that contains the results of statistical tests to compare the values of adaptive treatment strategies defined in a SMART data. The statistical tests include (1) a global test (2) a series of pairwise tests.
smartest(
data,
family = c("gaussian", "binomial")[1],
method = c("Gest", "IPW")[1],
digits = NULL,
common = FALSE,
alpha = 0.05,
adjust = NULL,
ntest = NULL
)Input data frame of the SMART data used for analysis, which include the variables of stage-1 treatments (A1), intermediate outcome (O2), stage-2 treatment (A2) and final primary outcome (Y). If stage-1 treatment takes into account baseline information, baseline information (O1) also needs to be included.
A character string to specify the type of final primary outcome. The default is family=<U+201C>gaussian<U+201D>, which refers to the continuous primary outcome. If family=<U+201D>binomial<U+201D> then the primary outcome will be treated as binary variable.
Method used to estimate the value of adaptive treatment strategy. "Gest" for G-computation method and "IPW" for Inversed Probabiliy Weight method. Default is method="Gest".
An integer indicating the number of decimal places for sequence-specific mean and variance. Default is digits=NULL.
If common=TRUE, the pooled variance across all the treatment sequences are used in estimation. Otherwise use the sequence-specific variance. The default is common=FALSE.
Significant level of confidence interval. The default is alpha=0.05.
A characteristic string to indicate whether the confidence intervals pairwise distance adjusted for multiple comparison. The default is adjust=NULL, which indicated no adjustment for multiple comparison. If adjust=<U+201D>Bon<U+201D>, the CIs are adjusted for the Bonferroni correction.
Number of pairwise tests adjusted for Bonferroni correction
An objects of <U+201C>Strategy<U+201D> is return, which lists all the adaptive treatment strategy defined in the input data with an index number.
ATS: Index of the treatment adaptive treatment strategy defined in the input dataset
ds: the sequence of decision makings that define an adaptive treatment strategy
N: number of subjects following an adaptive treatment strategy in the intut dataset
An objects of "Global.test" is return, which give the result of the global test.
size: the total number of subjects in the input dataset
nATS: the total number of adaptive treatment strategies defined in the input dataset
df: the degrees of freedom of the global test, which is a chisquare test
chisq: the chisquare test statistics for the global test
Pvalue: the P-value of the global test
An object of "Pairwise.test"
label: The labels of pairwise tests. The details of strategies are shown in $Strategy
diff: Estimated pairwise distance between treatment and control adaptive treatment strategy
lower.CI: Lower bound of confidence interval for pairwise distance
upper.CI: Upper bound of confidence interval for pairwise distance
Z: Test statistics of pairwise test
P-value: P-value of pairwise test
Murphy, S. A. (2005),``An experimental design for the development of adaptive treatment strategies'' Statistics in Medicine, 24, 1455-1481.
Ogbagaber S. B., Karp, J., and Wahed A.S. (2015), ``Design of sequentially randomization trials for testing adaptive treatment strategies,'' Statistics in Medicine, DOI: 10.1002/sim.6747.
# NOT RUN {
smartest(data=codiacs,family="gaussian",method="Gest",
common=FALSE,alpha=0.05,adjust="Bon")
# }
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