Sample size determination for clinical trials with co‐primary outcomes: exponential event times

Clinical trials with event‐time outcomes as co‐primary contrasts are common in many areas such as infectious disease, oncology, and cardiovascular disease. We discuss methods for calculating the sample size for randomized superiority clinical trials with two correlated time‐to‐event outcomes as co‐primary contrasts when the time‐to‐event outcomes are exponentially distributed. The approach is simple and easily applied in practice. Copyright © 2012 John Wiley & Sons, Ltd.     

A convenient formula for sample size calculations in clinical trials with multiple co-primary continuous endpoints

The clinical efficacy of a new treatment may often be better evaluated by two or more co-primary endpoints. Recently, in pharmaceutical drug development, there has been increasing discussion regarding establishing statistically significant favorable results on more than one endpoint in comparisons between treatments, which is referred to as a problem of multiple co-primary endpoints. Several methods have been proposed for calculating the sample size required to design a trial with multiple co-primary correlated endpoints. However, because these methods require users to have considerable mathematical sophistication and knowledge of programming techniques, their application and spread may be restricted in practice. To improve the convenience of these methods, in this paper, we provide a useful formula with accompanying numerical tables for sample size calculations to design clinical trials with two treatments, where the efficacy of a new treatment is demonstrated on continuous co-primary endpoints. In addition, we provide some examples to illustrate the sample size calculations made using the formula. Using the formula and the tables, which can be read according to the patterns of correlations and effect size ratios expected in multiple co-primary endpoints, makes it convenient to evaluate the required sample size promptly.     

Spontaneous Facial Expressions Reveal New Action Units for the Sad Experiences

Facial expressions related to sadness are a universal signal of nonverbal communication. Although results of many psychology studies have shown that drooping of the lip corners, raising of the chin, and oblique eyebrow movements (a combination of inner brow raising and brow lowering) express sadness, no report has described a study elucidating facial expression characteristics under well-controlled circumstances with people actually experiencing the emotion of sadness itself. Therefore, spontaneous facial expressions associated with sadness remain unclear. We conducted this study to accumulate important findings related to spontaneous facial expressions of sadness. We recorded the spontaneous facial expressions of a group of participants as they experienced sadness during an emotion-elicitation task. This task required a participant to recall neutral and sad memories while listening to music. We subsequently conducted a detailed analysis of their sad and neutral expressions using the Facial Action Coding System. The prototypical facial expressions of sadness in earlier studies were not observed when people experienced sadness as an internal state under non-social circumstances. By contrast, they expressed tension around the mouth, which might function as a form of suppression. Furthermore, results show that parts of these facial actions are not only related to sad experiences but also to other emotional experiences such as disgust, fear, anger, and happiness. This study revealed the possibility that new facial expressions contribute to the experience of sadness as an internal state.     

Inter-organizational collaborative research networks in semiconductor lasers 1975–1994

How does the development of collaborative research networks vary? This paper investigates how research networks have developed in the same technological fields in North America, Europe, and Asia. Exploring inter-organizational co-authored papers published in the Applied Physics Letters from 1975 to 1994, we first map the collaborative research networks and demonstrate that research networks were more developed in scale and scope in North America than in Europe and Asia in semiconductor laser technology. U.S. organizations played a nodal role in networking throughout this time period. Addressing how research networks were more developed in the U.S. than in Japan in particular, this paper concludes that engineer mobility, academic–industry relationships, and entrepreneurial startups were the key factors for the development of collaborative networks during the periods studied.     

Preference Aggregation With Incomplete Information

We show in an environment of incomplete information that monotonicity and the Pareto property applied only when there is common knowledge of Pareto dominance imply (i) there must exist a common prior over the smallest common knowledge event, and (ii) aggregation must be ex ante and ex post utilitarian with respect to that common prior and individual von Neumann–Morgenstern utility indices.     

The power of the modified Wilcoxon rank-sum test for the one-sided alternative

When testing hypotheses in two-sample problems, the Wilcoxon rank-sum test is often used to test the location parameter, and this test has been discussed by many authors over the years. One modification of the Wilcoxon rank-sum test was proposed by Tamura [On a modification of certain rank tests. Ann Math Stat. 1963;34:1101–1103]. Deriving the exact critical value of the statistic is difficult when the sample sizes are increased. The normal approximation, the Edgeworth expansion, the saddlepoint approximation, and the permutation test were used to evaluate the upper tail probability for the modified Wilcoxon rank-sum test given finite sample sizes. The accuracy of various approximations to the probability of the modified Wilcoxon statistic was investigated. Simulations were used to investigate the power of the modified Wilcoxon rank-sum test for the one-sided alternative with various population distributions for small sample sizes. The method was illustrated by the analysis of real data.