Presentation Type
Poster
Student
Yes
Track
Methodology
Abstract
Although the expected value is popular, many researches in the health and social sciences involve skewed distributions and inferences concerning quantiles. Most standard multiple comparison procedures require the normality assumption. For example, few methods exist for comparing the medians of independent samples or quantiles of several distributions in general. To our knowledge, there is no general-purpose method for constructing simultaneous confidence intervals for multiple contrasts of quantiles. In this paper, we develop an asymptotic method for constructing such intervals and extend the idea to that of time-to-event data in survival analysis. Small-sample performance of the proposed method is assessed in terms of coverage probability and average width of the simultaneous confidence intervals. Good coverage probabilities are observed for most of the distributions considered in the simulations. The proposed method is applied to biomedical data and time-to-event data in survival analysis.
Start Date
2-11-2020 1:00 PM
Included in
Asymptotic Simultaneous Estimations for Contrasts of Quantiles
Volstorff A
Although the expected value is popular, many researches in the health and social sciences involve skewed distributions and inferences concerning quantiles. Most standard multiple comparison procedures require the normality assumption. For example, few methods exist for comparing the medians of independent samples or quantiles of several distributions in general. To our knowledge, there is no general-purpose method for constructing simultaneous confidence intervals for multiple contrasts of quantiles. In this paper, we develop an asymptotic method for constructing such intervals and extend the idea to that of time-to-event data in survival analysis. Small-sample performance of the proposed method is assessed in terms of coverage probability and average width of the simultaneous confidence intervals. Good coverage probabilities are observed for most of the distributions considered in the simulations. The proposed method is applied to biomedical data and time-to-event data in survival analysis.