A value of information framework for assessing the trade-offs associated with uncertainty,duration, and cost of chemical toxicity testing

A number of investigators have explored the use of value of information (VOI) analysis to evaluate alternative information collection procedures in diverse decision-making contexts. This paper presents an analytic framework for determining the value of toxicity information used in risk-based decision making. The framework is specifically designed to explore the trade-offs between cost, timeliness, and uncertainty reduction associated with different toxicity-testing methodologies. The use of the proposed framework is demonstrated by two illustrative applications which, although based on simplified assumptions, show the insights that can be obtained through the use of VOI analysis. Specifically, these results suggest that timeliness of information collection has a significant impact on estimates of the VOI of chemical toxicity tests, even in the presence of smaller reductions in uncertainty. The framework introduces the concept of the expected value of delayed sample information, as an extension to the usual expected value of sample information, to accommodate the reductions in value resulting from delayed decision making. Our analysis also suggests that lower cost and higher throughput testing also may be beneficial in terms of public health benefits by increasing the number of substances that can be evaluated within a given budget. When the relative value is expressed in terms of return-on-investment per testing strategy, the differences can be substantial.     

A Bayesian method for safety signal detection in ongoing blinded randomised controlled trials

Sponsors have a responsibility to minimise risk to participants in clinical studies through safety monitoring. The FDA Final Rule for IND Safety Reporting requires routine aggregate safety evaluation, including in ongoing blinded studies. We are interested in estimating the probability that the true adverse event rate in the experimental arm exceeds that in the control arm. We developed a Bayesian approach that specifies an informative meta-analytic predictive prior on the event probability in the control arm and an uninformative prior on that in the experimental arm. We combined these priors with a mixture likelihood that considers each patient in the ongoing blinded study may belong to the experimental or control arm. This allowed us to estimate the quantity of interest without unblinding. We evaluated our method by simulation, pairing scenarios that differed only in whether a safety signal was present or missing, and quantifying the ability of our model to discriminate using signal detection theory. Our approach shows benefit. It detects safety signals more reliably with greater sample sizes and for common rather than rare events. Performance does not deteriorate markedly when historical studies exhibit heterogeneous hazards or non-constant hazards. Our method will allow us to monitor safety signals in ongoing blinded studies with the goal of earlier identification and risk mitigation. Our method could be adapted to use informative priors on both arms or predictive covariates where pertinent data exist. We stress that ongoing safety monitoring should involve a multi-disciplinary team where statistical methods are paired with medical judgement.     

机械类课程体系改革模式的探讨

从市场用人机制变化的角度 ,论述了课程体系改革的重要性与必要性 ,分析了课程体系改革与市场需求之间的内在联系 ,提出机械类课程体系改革的层次可扩充模型 ,并给出该模型的实施方案。     

AIDE: Adaptive intrapatient dose escalation designs to accelerate Phase I clinical trials

Designing Phase I clinical trials is challenging when accrual is slow or sample size is limited. The corresponding key question is: how to efficiently and reliably identify the maximum tolerated dose (MTD) using a sample size as small as possible? We propose model-assisted and model-based designs with adaptive intrapatient dose escalation (AIDE) to address this challenge. AIDE is adaptive in that the decision of conducting intrapatient dose escalation depends on both the patient's individual safety data, as well as other enrolled patient's safety data. When both data indicate reasonable safety, a patient may perform intrapatient dose escalation, generating toxicity data at more than one dose. This strategy not only provides patients the opportunity to receive higher potentially more effective doses, but also enables efficient statistical learning of the dose-toxicity profile of the treatment, which dramatically reduces the required sample size. Simulation studies show that the proposed designs are safe, robust, and efficient to identify the MTD with a sample size that is substantially smaller than conventional interpatient dose escalation designs. Practical considerations are provided and R code for implementing AIDE is available upon request.     

There Goes the Neighborhood: How Racial Threat Shapes the Formation of Integrated and Segregated Neighborhoods

Neighborhoods in the United States are highly segregated by race. Black–white segregation is particularly acute. Although racial segregation has been declining in recent years, it remains extremely high—especially in the largest metropolitan areas. The historical pattern has been that as African Americans move into a neighborhood, whites move out and thus the neighborhood (gradually or rapidly) transitions from predominantly white to predominantly black. A seemingly contradictory pattern has recently been observed: integrated neighborhoods are becoming more common. Why do some neighborhoods transition from white to black and others become integrated? We use racial competition theory to answer this question. We examine the characteristics of stable, integrated neighborhoods and contrast them with segregated predominantly white, predominantly black, and rapidly transitioning neighborhoods in six large metropolitan areas. We find that the location of black neighborhoods is a key factor in whether an integrated neighborhood remains integrated. Specifically, we find integrated neighborhoods are spatially distant from majority black neighborhoods while transitioning neighborhoods are adjacent to them. Our findings support the “sense of threat” hypothesis of racial competition theory.     

ORGANIZATIONAL CHOICE AND SOME CORRELATES OF INDIVIDUALS' EXPECTANCIES1

This study uses expectancy theory to predict the sorority choices of 121 undergraduate students. Results show that the complete, multiplicative expectancy model is only a slightly better predictor of choice behavior than other versions of the model. Several personal and situational correlates of individuals' expectancies of entering sororities also are examined. Results show significant relationships between expectancy and subjects' self-esteem, perception of value similarity with sorority members, and number of perceived friendships with sorority members.