Yu-Mei Chang

Identification of the minimum effective dose for right-censored survival data

In this paper, we consider identifying the minimum effective dose (MED) in a dose-response study when survival data are subject to random right-censorship, where the MED is defined to be the smallest dose level under study that has survival advantage over the zero-dose control. To this end, we suggest single-step-down testing procedures based on three different types of weighted logrank statistics, respectively. The comparative results of a Monte Carlo error rate and power/bias study for a variety of survival and censoring distributions are then presented and discussed. The application of the testing procedures for identifying the MED is finally illustrated by using a numerical example of prostate cancer data.

Confidence bands for the difference between two median survival times as a function of covariates

In this paper, the estimation of the difference between two median survival times is considered when two treatment groups of right-censored data and the associated covariates are available. To identify the possible range of covariates over which the two treatments would produce different median survival times, two confidence bands for the difference as a function of the covariates are proposed under the stratified and treatment-specific Cox models, respectively. The results of a simulation study indicate that the latter generally maintains its confidence level and the former holds its confidence level and preserves a narrower width when the two treatments satisfy the stratified Cox model. An application of the proposed confidence bands is finally illustrated with a data set in a two-arm lung cancer study.

A jackknife-based versatile test for two-sample problems with right-censored data

For testing the equality of two survival functions, the weighted logrank test and the weighted Kaplan–Meier test are the two most widely used methods. Actually, each of these tests has advantages and defects against various alternatives, while we cannot specify in advance the possible types of the survival differences. Hence, how to choose a single test or combine a number of competitive tests for indicating the diversities of two survival functions without suffering a substantial loss in power is an important issue. Instead of directly using a particular test which generally performs well in some situations and poorly in others, we further consider a class of tests indexed by a weighted parameter for testing the equality of two survival functions in this paper. A delete-1 jackknife method is implemented for selecting weights such that the variance of the test is minimized. Some numerical experiments are performed under various alternatives for illustrating the superiority of the proposed method. Finally, the proposed testing procedure is applied to two real-data examples as well.

Identification of the minimum effective dose based on weighted Kaplan-Meier Statistics.

Testing procedures are considered for identifying the minimum effective dose (MED) in a dose–response study with randomly right-censored survival data, where the MED is defined to be the smallest dose level under study that has survival advantage over the zero dose control. The proposed testing procedures are implemented in a step-down manner together with three different types of weighted Kaplan–Meier statistics. Comparative results of a Monte Carlo error rate and power/bias study for a variety of survival and censoring distributions are then presented and discussed. The application of the proposed procedures is finally illustrated for identifying the MED of the diethylstilbestrol in the treatment of prostate cancer.

Model validation based on ensemble empirical mode decomposition.

We proposed a new model validation method through ensemble empirical mode decomposition (EEMD) and scale separate correlation. EEMD is used to analyze the nonlinear and nonstationary ozone concentration data and the data simulated from the Taiwan Air Quality Model (TAQM). Our approach consists of shifting an ensemble of white noise-added signal and treats the mean as the final true intrinsic mode functions (IMFs). It provides detailed comparisons of observed and simulated data in various temporal scales. The ozone concentration of Wan-Li station in Taiwan is used to illustrate the power of this new approach. Our results show that, at an urban station, the ozone concentration fluctuation has various cycles that include semi-diurnal, diurnal, and weekly time scales. These results serve to demonstrate the anthropogenic origin of the local pollutant and long-range transport effects were all important. The validation tests indicate that the model used here performs well to simulate phenomena of all temporal scales.

Estimation of the simultaneous confidence regions for the ratios of quantile residual lifetimes

ABSTRACT In many clinical studies, especially when the associated diseases can be incurable, it is critical to evaluate the effect of treatments on mean residual lifetime or quantile residual lifetime. In this article, for right-censored survival data, we concern the construction of simultaneous confidence regions for the ratios of quantile residual lifetimes between treatment and control groups. We propose two approaches: the first is based on the score-type test of Jeong et al. [Nonparametric inference on median residual life function. Biometrics. 2008;64:157–163], while the second is based on the empirical likelihood ratio test of Zhou and Jeong [Empirical likelihood ratio test for median and mean residual lifetime. Statist. Med. 2011;30:152–159]. The performance of the associated coverage probability and the expected coverage area are investigated via a simulation study. The proposed method is illustrated using a real data set.

Adaptive Cox Model Averaging for Right-Censored Data

ABSTRACT In medical studies, Cox proportional hazards model is a commonly used method to deal with the right-censored survival data accompanied by many explanatory covariates. In practice, the Akaikes information criterion (AIC) or the Bayesian information criterion (BIC) is usually used to select an appropriate subset of covariates. It is well known that neither the AIC criterion nor the BIC criterion dominates for all situations. In this paper, we propose an adaptive-Cox model averaging procedure to get a more robust hazard estimator. First, by applying AIC and BIC criteria to perturbed datasets, we obtain two model averaging (MA) estimated survival curves, called AIC-MA and BIC-MA. Then, based on Kullback–Leibler loss, a better estimate of survival curve between AIC-MA and BIC-MA is chosen, which results in an adaptive-Cox estimate of survival curve. Simulation results show the superiority of our approach and an application of the proposed method is also presented by analyzing the German Breast Cancer Study dataset.

Confidence intervals for the difference between two median survival times for clustered survival data

ABSTRACT Clustered survival data arise often in clinical trial design, where the correlated subunits from the same cluster are randomized to different treatment groups. Under such design, we consider the problem of constructing confidence interval for the difference of two median survival time given the covariates. We use Cox gamma frailty model to account for the within-cluster correlation. Based on the conditional confidence intervals, we can identify the possible range of covariates over which the two groups would provide different median survival times. The associated coverage probability and the expected length of the proposed interval are investigated via a simulation study. The implementation of the confidence intervals is illustrated using a real data set.