Published papers

Hangovers are associated with negative economic consequences due to decreased job performance or frequent visits to physicians. Thus, a new strategy for the alleviation of hangover-related symptoms is needed to avoid this detriment to society. The purpose of this nationwide randomized, double-blind, placebo-controlled physicians’ trial was to evaluate the efficacy of loxoprofen sodium for the alleviation of fatigue, headache, and nausea after hangover. A total of 229 participants were randomized to receive loxoprofen sodium (60 mg once orally) or placebo. The study was closed when the first 150 participants (n = 74 in the loxoprofen vs. n = 76 in the placebo groups) experienced hangovers. The primary endpoint was set as the difference in severity of general fatigue before and 3 h after taking the test drugs and was evaluated using a visual analogue scale. Secondary endpoints included difference in severity of headache, nausea, and incidence of adverse events. The study participants were 34 (interquartile range; 30–39) years old, 92.0% were men, and both groups were comparable for baseline characteristics. The alleviation of general fatigue did not differ statistically between the loxoprofen and placebo groups (24 [14–49] vs. 19 [9–35], p = 0.07). However, the alleviation of headache was statistically greater in the loxoprofen group (25 [10–50] vs. 10 [2–30], adjusted difference 14, 95% confidence interval 8–21, p < 0.001), whereas, there was no difference in nausea (7 [0–27] vs. 10 [0–24], p = 0.68). The incidence of adverse symptoms such as epigastric discomfort was also comparable between groups (2.7% vs. 3.9%, p = 0.25). Loxoprofen sodium was effective for relieving headaches after hangovers but did not alleviate general fatigue or nausea.

It has been reported that using unlabeled data together with labeled data to construct a discriminant function works successfully in practice. However, theoretical studies have implied that unlabeled data can sometimes adversely affect the performance of discriminant functions. Therefore, it is important to know what situations call for the use of unlabeled data. In this paper, asymptotic relative efficiency is presented as the measure for comparing analyses with and without unlabeled data under the heteroscedastic normality assumption. The linear discriminant function maximizing the area under the receiver operating characteristic curve is considered. Asymptotic relative efficiency is evaluated to investigate when and how unlabeled data contribute to improving discriminant performance under several conditions. The results show that asymptotic relative efficiency depends mainly on the heteroscedasticity of the covariance matrices and the stochastic structure of observing the labels of the cases.

The predictive performance of biomarkers is a central concern in biomedical research. This is often evaluated by comparing two statistical models: a “new” model incorporating additional biomarkers and an “old” model without them. In 2008, the integrated discrimination improvement (IDI) was proposed for cases when the response variable is binary, and it is now widely applied as a promising alternative to conventional measures, such as the difference of the area under the receiver operating characteristic curve. However, the IDI can erroneously identify a significant improvement in the new model even if no additional information has been provided by new biomarkers. In order to overcome problems with existing measures, in this study, we propose the power-IDI as a measure of incremental predictive value. Our study explains why the IDI cannot avoid false detection of apparent improvements in a new model and we show that our proposed measure is better able to capture improvements in prediction. Numerical simulations and examples using real empirical data reveal that the power-IDI is not only more powerful but also incurs fewer false detections of improvement.

Consider adding new covariates to an established binary regression model to improve prediction performance. Although difference in the area under the ROC curve (delta AUC) is typically used to evaluate the degree of improvement in such situations, its power is not high due to being a rank-based statistic. As an alternative to delta AUC, integrated discrimination improvement (IDI) has been proposed by Pencina et al. (2008). However, several papers have pointed out that IDI erroneously detects meaningless improvement. In the present study, we propose a novel index for prediction improvement having Fisher consistency, implying that it overcomes the problems in both delta AUC and IDI. Furthermore, our proposed index also has an advantage that the index we proposed in our previous study (Hayashi and Eguchi 2019) lacked: it does not require any hyperparameters or complicated transformations that would make interpretation difficult.

Clinical trials often face the challenge of post-randomization events, such as the initiation of rescue therapy or the premature discontinuation of randomized treatment. Such events, called “intercurrent events” (ICEs) in ICH E9(R1), may influence the estimation and interpretation of treatment effects. According to ICH E9(R1), there are five strategies for handling ICEs. This study focuses on the composite strategy, which incorporates ICEs in the outcome of interest and defines the treatment effects using composite endpoints that combine the measured continuous variables and ICEs. An advantage of this strategy is that it avoids the occurrence of missing data because they are defined as part of the outcome of interest. In this study, we propose a new composite estimand: the difference in the partial areas under the curves (pAUCs) of the cumulative distribution function. While the pAUC is closely related to the trimmed mean approach proposed by Permutt and Li, it offers the advantage of allowing pre-specification of the cutoff value for a “good” response based on clinical considerations. This ensures that the pAUC can be calculated irrespective of the proportion of ICEs. We describe the causal interpretation of our method and its relationship with two other strategies (treatment policy and hypothetical strategies) using a potential outcome framework. We present simulation results in which our method performs reasonably well compared to several existing approaches in terms of type I error, power, and the proportion of undefined test statistics.