Description Usage Arguments Value Examples
Let N and P denote the number of subjects and number of variables in the dataset, respectively. Let N** denote the total number of visits, summed over all subjects in the study [i.e. N** denotes the number of diagnostic test results available for all subjects in the study]. This algorithm builds a user-defined number of survival trees, using bootstrapped datasets. Using the out of bag (OOB) data in each tree, a permutation-based measure of variable importance for each of the P variables is obtained.
1 2 | treebuilder(data, subject, testtimes, result, sensitivity, specificity, Xmat,
root.size, ns, pval = 1)
|
data |
name of the data frame that includes the variables subject, testtimes, result |
subject |
vector of subject IDs of length N**x1. |
testtimes |
vector of visit or test times of length N**x1. |
result |
vector of binary diagnostic test results (0 = negative for event of interest; 1 = positive for event of interest) of length N**x1. |
sensitivity |
the sensitivity of the diagnostic test. |
specificity |
the specificity of the diagnostic test. |
Xmat |
a N x P matrix of covariates. |
root.size |
the minimum number of subjects in a terminal node. |
ns |
number of covariate selected at each node to split the tree. |
pval |
P-value threshold of the Likelihood Ratio Test. |
a vector of the ensembled variable importance for modified random survival forest (icRSF).
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