22,167 research outputs found

    A Rejection Principle for Sequential Tests of Multiple Hypotheses Controlling Familywise Error Rates

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    We present a unifying approach to multiple testing procedures for sequential (or streaming) data by giving sufficient conditions for a sequential multiple testing procedure to control the familywise error rate (FWER), extending to the sequential domain the work of Goeman and Solari (2010) who accomplished this for fixed sample size procedures. Together we call these conditions the "rejection principle for sequential tests," which we then apply to some existing sequential multiple testing procedures to give simplified understanding of their FWER control. Next the principle is applied to derive two new sequential multiple testing procedures with provable FWER control, one for testing hypotheses in order and another for closed testing. Examples of these new procedures are given by applying them to a chromosome aberration data set and to finding the maximum safe dose of a treatment

    Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 199

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    This bibliography lists 82 reports, articles, and other documents introduced into the NASA scientific and technical information system in October 1979

    Does the Quality of Training Programs Matter? Evidence from Bidding Processes Data

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    This paper estimates the effect of training quality on labor-market earnings using a Peruvian non-experimental training program, PROJOVEN, which targets disadvantaged youths aged 16 to 24 years. The identification of causal effects is possible because of two attractive features in the data. First, the selection of training courses is based on public bidding processes that assign standardized scores to multiple proxies for quality. Second, the program`s evaluation framework allows for the identification and comparison of individuals in the treatment and comparison groups six, 12, and 18 months after the program. Using difference-in-differences kernel matching methods, we find that individuals attending high-quality training courses have higher average and marginal treatment impacts. The external validity of our estimates was assessed by using five different calls of this program over a nine-year period.

    Point and interval estimation in two-stage adaptive designs with time to event data and biomarker-driven subpopulation selection

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    In personalized medicine, it is often desired to determine if all patients or only a subset of them benefit from a treatment. We consider estimation in two‐stage adaptive designs that in stage 1 recruit patients from the full population. In stage 2, patient recruitment is restricted to the part of the population, which, based on stage 1 data, benefits from the experimental treatment. Existing estimators, which adjust for using stage 1 data for selecting the part of the population from which stage 2 patients are recruited, as well as for the confirmatory analysis after stage 2, do not consider time to event patient outcomes. In this work, for time to event data, we have derived a new asymptotically unbiased estimator for the log hazard ratio and a new interval estimator with good coverage probabilities and probabilities that the upper bounds are below the true values. The estimators are appropriate for several selection rules that are based on a single or multiple biomarkers, which can be categorical or continuous

    Review of Sensitivity Analysis Methods and Experience for Geological Disposal of Radioactive waste and Spent Nuclear Fuel

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    This reports gives an overview of sensitivity methods (screening, global and response surface based) that are suitable for safety analysis of a repository for radioactive waste or spent nuclear fuel. The theorerical background of the methods, their limitations and suitability for different analyses are discussed and illustrated by examples.JRC.F.7-Energy systems evaluatio

    Optimal Decision Making in Drug Development

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    Change-point Problem and Regression: An Annotated Bibliography

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    The problems of identifying changes at unknown times and of estimating the location of changes in stochastic processes are referred to as the change-point problem or, in the Eastern literature, as disorder . The change-point problem, first introduced in the quality control context, has since developed into a fundamental problem in the areas of statistical control theory, stationarity of a stochastic process, estimation of the current position of a time series, testing and estimation of change in the patterns of a regression model, and most recently in the comparison and matching of DNA sequences in microarray data analysis. Numerous methodological approaches have been implemented in examining change-point models. Maximum-likelihood estimation, Bayesian estimation, isotonic regression, piecewise regression, quasi-likelihood and non-parametric regression are among the methods which have been applied to resolving challenges in change-point problems. Grid-searching approaches have also been used to examine the change-point problem. Statistical analysis of change-point problems depends on the method of data collection. If the data collection is ongoing until some random time, then the appropriate statistical procedure is called sequential. If, however, a large finite set of data is collected with the purpose of determining if at least one change-point occurred, then this may be referred to as non-sequential. Not surprisingly, both the former and the latter have a rich literature with much of the earlier work focusing on sequential methods inspired by applications in quality control for industrial processes. In the regression literature, the change-point model is also referred to as two- or multiple-phase regression, switching regression, segmented regression, two-stage least squares (Shaban, 1980), or broken-line regression. The area of the change-point problem has been the subject of intensive research in the past half-century. The subject has evolved considerably and found applications in many different areas. It seems rather impossible to summarize all of the research carried out over the past 50 years on the change-point problem. We have therefore confined ourselves to those articles on change-point problems which pertain to regression. The important branch of sequential procedures in change-point problems has been left out entirely. We refer the readers to the seminal review papers by Lai (1995, 2001). The so called structural change models, which occupy a considerable portion of the research in the area of change-point, particularly among econometricians, have not been fully considered. We refer the reader to Perron (2005) for an updated review in this area. Articles on change-point in time series are considered only if the methodologies presented in the paper pertain to regression analysis

    Bayes\u27 Law, Sequential Uncertainties, and Evidence of Causation in Toxic Tort Cases

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    Judges are the gatekeepers of evidence. Arguably, the most difficult duty for a judicial gatekeeper is to screen the reliability of expert opinions in scientific fields such as medicine that are beyond the ken of most judges. Yet, judges have a duty to scrutinize such expert opinion evidence to determine its reliability and admissibility. In toxic tort cases, the issue of causation-whether the alleged exposures actually caused the plaintiffs injury-is nearly always the central dispute, and determining admissibility of expert causation opinion is a daunting challenge for most judges. We present a comprehensive review of the courts\u27 struggles with the screening of scientific evidence in such cases. In addition, we propose an approach to the screening of causation opinions based on probability science and logic. Central to this approach is Bayes\u27 Law, a statistical tool that courts can use to analyze the extrinsic reliability of proffered causation testimony. We explain Bayes\u27 Law and illustrate its potential application for evaluating the reliability of medical and scientific causation testimony. All evidence is probabilistic. There are uncertainties attending all testimony, not only because the honesty or objectivity of witnesses may be doubtful, but also because even honest and unbiased witnesses may be mistaken in their perceptions. Reliability of causation evidence depends on both sensitivity and specificity of the tests used to determine causation. Highly sensitive tests of causation reflect an ability to identify a high percentage of those with the agent-induced disease, whereas highly specific tests of causation reflect an ability to reject a high percentage of those who have the disease, but not induced by the agent at issue. According to Bayes\u27 Law, the reliability of causation opinion depends not only on the sensitivity and specificity of the tests employed by the causation expert, but also on the base rate of the agent-induced disease in the population. Bayes\u27 Law dictates that the lower the rate of the agent-induced disease in the population, the less reliable the opinion that the agent at issue in fact caused the plaintiffs disease given certain levels of sensitivity and specificity. The base-rate problem and its effect on reliability of causation opinions are overlooked by judges when scrutinizing the reliability of proffered causation evidence. In this Article, we encourage courts to consider a Bayes\u27 Law approach to screen out, at an early stage, those claims of injury lacking reliable evidence that an injury was more likely than not caused by exposures to toxic agents. The goal of our Article is to provide a framework that helps the gatekeeper to screen out toxic tort claims insufficiently substantiated by the underlying scientific and medical data, and allow the factfinder to decide only those toxic tort claims for which there is reliable and relevant scientific support for each link of the causal chain, from subject exposure to the injury Scientific substantiation of each causal link determines the reliability of an experts opinion that the exposure more likely than not caused the plaintiffs injury
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