Easy and accurate variance estimation of the nonparametric estimator of the partial area under the ROC curve and its application.

The receiver operating characteristic (ROC) curve is a popular technique with applications, for example, investigating an accuracy of a biomarker to delineate between disease and non-disease groups. A common measure of accuracy of a given diagnostic marker is the area under the ROC curve (AUC). In contrast with the AUC, the partial area under the ROC curve (pAUC) looks into the area with certain specificities (i.e., true negative rate) only, and it can be often clinically more relevant than examining the entire ROC curve. The pAUC is commonly estimated based on a U-statistic with the plug-in sample quantile, making the estimator a non-traditional U-statistic. In this article, we propose an accurate and easy method to obtain the variance of the nonparametric pAUC estimator. The proposed method is easy to implement for both one biomarker test and the comparison of two correlated biomarkers because it simply adapts the existing variance estimator of U-statistics. In this article, we show accuracy and other advantages of the proposed variance estimation method by broadly comparing it with previously existing methods. Further, we develop an empirical likelihood inference method based on the proposed variance estimator through a simple implementation. In an application, we demonstrate that, depending on the inferences by either the AUC or pAUC, we can make a different decision on a prognostic ability of a same set of biomarkers. Copyright © 2016 John Wiley & Sons, Ltd.

Patient education integrated with acupuncture for relief of cancer-related fatigue randomized controlled feasibility study.

BACKGROUND: Cancer-related fatigue (CRF) is a prominent clinical problem. There are calls for multi-modal interventions. METHODS: We assessed the feasibility of delivering patient education integrated with acupuncture for relief of CRF in a pilot randomized controlled trial (RCT) with breast cancer survivors using usual care as control. Social cognitive and integrative medicine theories guided integration of patient education with acupuncture into a coherent treatment protocol. The intervention consisted of two parts. First, patients were taught to improve self-care by optimizing exercise routines, improving nutrition, implementing some additional evidence-based cognitive behavioral techniques such as stress management in four weekly 50-minute sessions. Second, patients received eight weekly 50-minute acupuncture sessions. The pre-specified primary outcome, CRF, was assessed with the Brief Fatigue Inventory (BFI). Secondary outcomes included three dimensions of cognitive impairment assessed with the FACT-COGv2. RESULTS: Due to difficulties in recruitment, we tried several methods that led to the development of a tailored recruitment strategy: we enlisted oncologists into the core research team and recruited patients completing treatment from oncology waiting rooms. Compared to usual care control, the intervention was associated with a 2.38-point decline in fatigue as measured by the BFI (90% Confidence Interval from 0.586 to 5.014; p <0.10). Outcomes associated with cognitive dysfunction were not statistically significant. CONCLUSIONS: Patient education integrated with acupuncture had a very promising effect that warrants conducting a larger RCT to confirm findings. An effective recruitment strategy will be essential for the successful execution of a larger-scale trial. TRIAL REGISTRATION: NCT00646633.

What is the Proper Sample Size for Studies of Periodontal Treatment?

Small sample sizes are a common problem in biomedical research, and the periodontal literature is no exception. It is a problem leading to not only reduced statistical power but also an inappropriate statistical inference of a treatment effect. Using statistical methods with an insufficient sample size may give rise to an increased chance of falsely detecting treatment efficacy. This article provides some guidelines to cope with the small sample size problem. The authors discuss adequate sample sizes in several statistical tests and then suggest alternative statistical methods that are valid with a small sample size.