Reputation spillover effects from grant‐providing institutions

There is a broad academic discussion about the impact of funding grants from a foundation or a government department on individual support intentions toward the nonprofit organization receiving the grant. However, the role of the grant provider's reputation has frequently been overlooked. In this study, we experimentally tested whether there is a reputation spillover effect of a grant‐providing organization. Based on a real‐life example, we asked citizens to rate their willingness to donate to a nonprofit organization, and we experimentally manipulated the available information on funding sources. We test this for both a government department and a foundation as a grant provider. Our results suggest that not the act of receiving a grant, but the citizens' awareness about the funding organization—at least in the case of a foundation—has an impact on support intentions. In contrast, for a prominent government department as a grant provider, we did not find support for a reputation spillover effect.     

Are sponsors blamed for edging off? Consumer reactions to sponsorship terminations

Firms regularly terminate sponsorships, even without publicly known misconduct by the sponsee such as athlete doping. Consumer reactions to these sponsorship terminations by firms have not been studied despite being a regular occurrence. Using a set of experimental studies, this paper analyzes consumer reactions to these sponsorship terminations (i.e., early and non-renewal) that were not caused by a sponsee’s misconduct, the underlying process that causes the reactions, and the role of several moderating factors (trust, power balance, and locus of control). Our findings reveal that sponsorship terminations have a negative effect on sponsors’ brand images—particularly early terminations that occur before the end of a contract—because consumers perceive these sponsorship terminations as unfair. The results also suggest that a termination is particularly harmful for the sponsor’s perceived fairness if the sponsor is powerful and if the termination decision is under the sponsor’s control. Further, the termination effect is particularly strong for firms that consumers trust.     

On- and offline detection of structural breaks in thermal spraying processes

We investigate and develop methods for structural break detection, considering time series from thermal spraying process monitoring. Since engineers induce technical malfunctions during the processes, the time series exhibit structural breaks at known time points, giving us valuable information to conduct the investigations. First, we consider a recently developed robust online (also real-time) filtering (i.e. smoothing) procedure that comprises a test for local linearity. This test rejects when jumps and trend changes are present, so that it can also be useful to detect such structural breaks online. Second, based on the filtering procedure we develop a robust method for the online detection of ongoing trends. We investigate these two methods as to the online detection of structural breaks by simulations and applications to the time series from the manipulated spraying processes. Third, we consider a recently developed fluctuation test for constant variances that can be applied offline, i.e. after the whole time series has been observed, to control the spraying results. Since this test is not reliable when jumps are present in the time series, we suggest data transformation based on filtering and demonstrate that this transformation makes the test applicable.     

A pragmatic adaptive enrichment design for selecting the right target population for cancer immunotherapies

One of the challenges in the design of confirmatory trials is to deal with uncertainties regarding the optimal target population for a novel drug. Adaptive enrichment designs (AED) which allow for a data-driven selection of one or more prespecified biomarker subpopulations at an interim analysis have been proposed in this setting but practical case studies of AEDs are still relatively rare. We present the design of an AED with a binary endpoint in the highly dynamic setting of cancer immunotherapy. The trial was initiated as a conventional trial in early triple-negative breast cancer but amended to an AED based on emerging data external to the trial suggesting that PD-L1 status could be a predictive biomarker. Operating characteristics are discussed including the concept of a minimal detectable difference, that is, the smallest observed treatment effect that would lead to a statistically significant result in at least one of the target populations at the interim or the final analysis, respectively, in the setting of AED.     

Semi-parametric forecasts of the implied volatility surface using regression trees

We present a new semi-parametric model for the prediction of implied volatility surfaces that can be estimated using machine learning algorithms. Given a reasonable starting model, a boosting algorithm based on regression trees sequentially minimizes generalized residuals computed as differences between observed and estimated implied volatilities. To overcome the poor predictive power of existing models, we include a grid in the region of interest, and implement a cross-validation strategy to find an optimal stopping value for the boosting procedure. Back testing the out-of-sample performance on a large data set of implied volatilities from S&P 500 options, we provide empirical evidence of the strong predictive power of our model.     

On Berry-Esseen theorem for some functions of U-statistics

The rate of convergence in the central limit theorem and in the random central limit theorem for some functions of U-statistics are established. The theorems refer to the asymptotic behaviour of the sequence {g(U_n),n≥1}, where g belongs to the class of all differentiable functions g such that g′εL(δ) and U_n is a U-statistics.     

Integrating phase 2 into phase 3 based on an intermediate endpoint while accounting for a cure proportion—With an application to the design of a clinical trial in acute myeloid leukemia

For a trial with primary endpoint overall survival for a molecule with curative potential, statistical methods that rely on the proportional hazards assumption may underestimate the power and the time to final analysis. We show how a cure proportion model can be used to get the necessary number of events and appropriate timing via simulation. If phase 1 results for the new drug are exceptional and/or the medical need in the target population is high, a phase 3 trial might be initiated after phase 1. Building in a futility interim analysis into such a pivotal trial may mitigate the uncertainty of moving directly to phase 3. However, if cure is possible, overall survival might not be mature enough at the interim to support a futility decision. We propose to base this decision on an intermediate endpoint that is sufficiently associated with survival. Planning for such an interim can be interpreted as making a randomized phase 2 trial a part of the pivotal trial: If stopped at the interim, the trial data would be analyzed, and a decision on a subsequent phase 3 trial would be made. If the trial continues at the interim, then the phase 3 trial is already underway. To select a futility boundary, a mechanistic simulation model that connects the intermediate endpoint and survival is proposed. We illustrate how this approach was used to design a pivotal randomized trial in acute myeloid leukemia and discuss historical data that informed the simulation model and operational challenges when implementing it.     

Methods for Classifying Nonprofit Organizations According to their Field of Activity: A Report on Semi-automated Methods Based on Text

VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations - There are various methods for classifying nonprofit organizations (NPOs) according to their field of activity. We report...