Evaluating the effect of optimized cutoff values in the assessment of prognostic factors
Computational Statistics & Data Analysis
An application of changepoint methods in studying the effect of age on survival in breast cancer
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis
A cautionary note on segmenting a cyclical covariate by minimum P-value search
Computational Statistics & Data Analysis
A note on change point estimation in dose-response trials
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis
Expert Systems with Applications: An International Journal
Estimating the functional form of a continuous covariate's effect on survival time
Computational Statistics & Data Analysis
Generalised indirect classifiers
Computational Statistics & Data Analysis
Gene Selection Using Iterative Feature Elimination Random Forests for Survival Outcomes
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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The construction of simple classification rules is a frequent problem in medical research. Maximally selected rank statistics allow the evaluation of cutpoints, which provide the classification of observations into two groups by a continuous or ordinal predictor variable. The computation of the exact distribution of a maximally selected rank statistic is discussed and a new lower bound of the distribution is derived based on an extension of an algorithm for the exact distribution of a linear rank statistic. Therefore, the test based on the upper bound of the P-value is of level α. For small to moderate sample sizes the lower bound of the exact distribution is a substantial improvement compared to approximations based on an improved Bonferroni inequality or based on the asymptotic Gaussian process. The lower bound of the distribution is compared to the exact distribution by means of a simulation study and the proposal is illustrated by three clinical studies.