Eunsik Park

Covariate-adjusted response-adaptive designs for longitudinal treatment responses: PEMF trial revisited

Response-adaptive designs have become popular for allocation of the entering patients among two or more competing treatments in a phase III clinical trial. Although there are a lot of designs for binary treatment responses, the number of designs involving covariates is very small. Sometimes the patients give repeated responses. The only available response-adaptive allocation design for repeated binary responses is the urn design by Biswas and Dewanji [Biswas A and Dewanji AA. Randomized longitudinal play-the-winner design for repeated binary data. ANZJS 2004; 46: 675–684; Biswas A and Dewanji A. Inference for a RPW-type clinical trial with repeated monitoring for the treatment of rheumatoid arthritis. Biometr J 2004; 46: 769–779.], although it does not take care of the covariates of the patients in the allocation design. In this article, a covariate-adjusted response-adaptive randomisation procedure is developed using the log-odds ratio within the Bayesian framework for longitudinal binary responses. The small sample performance of the proposed allocation procedure is assessed through a simulation study. The proposed procedure is illustrated using some real data set.

On a class of optimal covariate-adjusted response adaptive designs for survival outcomes

A class of optimal covariate-adjusted response adaptive procedures is developed for phase III clinical trials when the treatment response is a survival time and there is random censoring. The basic aim is to develop an allocation design by combining the ethical aspects with statistical precision in a reasonable way under the presence of covariate information. Considering minimisation of total hazards as the ethical requirement, the proposed procedure is assessed in terms of the assignment to the better treatment and the efficiency (i.e. power) to detect a small departure in treatment effectiveness. The applicability of the proposed methodology is also illustrated using a real data set.

Active learning for penalized logistic regression via sequential experimental design

Penalized logistic regression is useful for classification that not only provides class probability estimates but also can overcome overfitting problem. Traditionally, supervised classifier learning has required a lot of labeled data. Due to technical innovation, it is easy to collect large amounts of unlabeled data, while labeling is usually expensive and difficult. Active learning aims to select the most informative subjects for labeling to decrease the amount of labeling requests. Recently, active learning using experimental design techniques have attracted considerable attention. The typical criteria attempt to reduce the generalization error of a model by minimizing either its estimation variance or estimation bias. However, they fail to take into account both components simultaneously. In this article, we introduce a new algorithm of active learning using penalized logistic regression. The most informative subjects are selected as those with the smallest mean squared estimation error. This criterion, integrated with the idea of sequential design, is exploited in our algorithms to guide a procedure for a new subject selection. Experiments on extensive real-world data sets demonstrate the effectiveness and efficiency of the proposed method compared to several state-of-the-art active-learning alternatives.

Comparison of Paired ROC Curves through a Two-Stage Test.

The area under the receiver operating characteristic (ROC) curve (AUC) is a popularly used index when comparing two ROC curves. Statistical tests based on it for analyzing the difference have been well developed. However, this index is less informative when two ROC curves cross and have similar AUCs. In order to detect differences between ROC curves in such situations, a two-stage nonparametric test that uses a shifted area under the ROC curve (sAUC), along with AUCs, is proposed for paired designs. The new procedure is shown, numerically, to be effective in terms of power under a wide range of scenarios; additionally, it outperforms two conventional ROC-type tests, especially when two ROC curves cross each other and have similar AUCs. Larger sAUC implies larger partial AUC at the range of low false-positive rates in this case. Because high specificity is important in many classification tasks, such as medical diagnosis, this is an appealing characteristic. The test also implicitly analyzes the equality of two commonly used binormal ROC curves at every operating point. We also apply the proposed method to synthesized data and two real examples to illustrate its usefulness in practice.

Sequential analysis of longitudinal data in a prospective nested case-control study.

The nested case-control design is a relatively new type of observational study whereby a case-control approach is employed within an established cohort. In this design, we observe cases and controls longitudinally by sampling all cases whenever they occur but controls at certain time points. Controls can be obtained at time points randomly scheduled or prefixed for operational convenience. This design with longitudinal observations is efficient in terms of cost and duration, especially when the disease is rare and the assessment of exposure levels is difficult. In our design, we propose sequential sampling methods and study both (group) sequential testing and estimation methods so that the study can be stopped as soon as the stopping rule is satisfied. To make such a longitudinal sampling more efficient in terms of both numbers of subjects and replications, we propose applying sequential sampling methods to subjects and replications, simultaneously, until the information criterion is fulfilled. This simultaneous sequential sampling on subjects and replicates is more flexible for practitioners designing their sampling schemes, and is different from the classical approaches used in longitudinal studies. We newly define the σ-field to accommodate our proposed sampling scheme, which contains mixtures of independent and correlated observations, and prove the asymptotic optimality of sequential estimation based on the martingale theories. We also prove that the independent increment structure is retained so that the group sequential method is applicable. Finally, we present results by employing sequential estimation and group sequential testing on both simulated data and real data on childrens diarrhea.

Multiple-stage sampling procedure for covariate-adjusted response-adaptive designs:

Covariate-adjusted response-adaptive (CARA) design becomes an important statistical tool for evaluating and comparing the performance of treatments when targeted medicine and adaptive therapy become important medical innovations. Due to the nature of the adaptive therapies of interest and how subjects accrue to a sampling procedure, it is of interest how to control the sample size sequentially such that the estimates of treatment effects have satisfactory precision in addition to its asymptotic properties. In this paper, we apply a multiple-stage sequential sampling method to CARA design in such a way that the control of the sample size is more feasible. The theoretical properties of the proposed method, including the estimates of regression parameters and the allocation probabilities under this randomly stopped sampling procedure, are discussed. The numerical results based on synthesized data and a real example are presented.

A new evaluation measure of diagnostic tests based on modified area under the receiver operating characteristic curve

In this paper, a new diagnostic/classification measure based on modified AUC (MAUC) is proposed. By this measure, we penalize the margin of features between diseased and non-diseased groups in AUC. Its threshold independent, and under normal distribution assumption, we can prove that higher MAUC always means higher PAUC (within relatively low FPR) when AUCs are close to each other. Our simulations and experiment about prostate cancer MS data can also help to demonstrate it.