The clinical research units (CRUs) are one of the main spaces where both translational research and science take place. However, there is a lack of information about both best practices for CRU operations and, ultimately, benchmarks to evaluate CRU performance. The Research Unit Network (RUN) was created with the purpose to enable direct communication and collaboration among CRUs. An online survey was administered to further illustrate the functionality and impact of RUN. Thirty-one individual survey responses (39.2%) were included in the final analysis. The members value RUN monthly meetings (87.1%) as the most useful aspect of this network and CRU budgeting (67.7%) and staffing (61.3%) were the most relevant topics discussed. This is followed by EPIC – Research (58.1%), delegation of authority logs, unit signatures, and policies (51.6%), COVID-19 pandemic response (41.9%), the implementation of clinical trial management system (29.0%), and protocol deviations (19.4%). The intermediate goal of RUN is to identify best practices CRUs are establishing, implementing, and sharing these experiences with the goal to adopt them in different CRUs. The network’s long-term goal is to establish standard benchmarks that can be used for evaluating the performance of CRUs across the nation.

This article explores the economic issues related to financial crises at insurance companies, using an example from the Great Depression, the National Surety Company. National Surety was a large and diverse American insurance company that experienced a major crisis in 1933 due to losses from its guarantees of mortgage-backed securities. I find that policyholders were able to stage a massive run on the company by demanding the return of their unearned premiums. A key dynamic of the crisis was that policyholders at an insurance company have a dual role as holders of liabilities and as providers of income. In addition, I establish that government officials believed National Surety to be systemically important, due to the size of its insurance business and because many of its counterparties were societal actors that these officials sought to protect. As a result, the New York State Insurance Commissioner used emergency powers to reorganize the company, with the goal of providing continuity to its business lines outside mortgage-backed security insurance.

The longest stretch L(n) of consecutive heads in n independent and identically distributed coin tosses is seen from the prism of large deviations. We first establish precise asymptotics for the moment generating function of L(n) and then show that there are precisely two large deviation principles, one concerning the behavior of the distribution of L(n) near its nominal value log1∕p n and one away from it. We discuss applications to inference and to logarithmic asymptotics of functionals of L(n).

In this paper we study two distributions, namely the distribution of the waiting times until given numbers of occurrences of compound patterns and the distribution of the numbers of occurrences of compound patterns in a fixed number of trials. We elucidate the interrelation between these two distributions in terms of the generating functions. We provide perspectives on the problems related to compound patterns in statistics and probability. As an application, the waiting time problem of counting runs of specified lengths is considered in order to illustrate how the distributions of waiting times can be derived from our theoretical results.

A unifying technology is introduced for finding explicit closed form expressions for joint moment generating functions of various random quantities associated with some waiting time problems. Sooner and later waiting times are covered for general discrete- and continuous-time models. The models are either Markov chains or semi-Markov processes with a finite number of states. Waiting times associated with generalized phase-type distributions, that are of interest in survival analysis and other areas, are also covered.

We derive explicit closed expressions for the moment generating functions of whole collections of quantities associated with the waiting time till the occurrence of composite events in either discrete or continuous-time models. The discrete-time models are independent, or Markov-dependent, binary trials and the events of interest are collections of successes with the property that each two consecutive successes are separated by no more than a fixed number of failures. The continuous-time models are renewal processes and the relevant events are clusters of points. We provide a unifying technology for treating both the discrete and continuous-time cases. This is based on first embedding the problems into similar ones for suitably selected Markov chains or Markov renewal processes, and second, applying tools from the exponential family technology.