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Multiple Endpoints (multiple + endpoint)
Selected AbstractsA Note on One-Sided Tests with Multiple EndpointsBIOMETRICS, Issue 1 2004Michael D. Perlman Summary. Testing problems with multivariate one-sided alternative hypotheses are common in clinical trials with multiple endpoints. In the case of comparing two treatments, treatment 1 is often preferred if it is superior for at least one of the endpoints and not biologically inferior for the remaining endpoints. Bloch et al. (2001, Biometrics57, 1039,1047) propose an intersection,union test (IUT) for this testing problem, but their test does not utilize the appropriate multivariate one-sided test. In this note we modify their test by an alternative IUT that does utilize the appropriate one-sided test. Empirical and graphical evidence show that the proposed test is more appropriate for this testing problem. [source] How to deal with multiple endpoints in clinical trialsFUNDAMENTAL & CLINICAL PHARMACOLOGY, Issue 6 2006Markus Neuhäuser Abstract Multiple endpoints are common in clinical trials. This article discusses statistical methods that can be applied to control the rate of false positive conclusions at an acceptable level. The considered methods include the Bonferroni adjustment and related methods, the intersection-union test, ordered hypotheses and gatekeeper procedures, composite endpoints and global assessment measures, closed testing procedures, and combinations of different approaches. [source] Sublethal effects of methylmercury on fecal metabolites of testosterone, estradiol, and corticosterone in captive juvenile white ibises (Eudocimus albus),ENVIRONMENTAL TOXICOLOGY & CHEMISTRY, Issue 5 2009Evan M. Adams Abstract Methylmercury (MeHg) is a globally distributed neurotoxin, endocrine disruptor, and teratogen, and its effects on birds are poorly understood, especially within an environmentally relevant exposure range. In an effort to understand the potential causal relationship between MeHg exposure and endocrine development, we established four dietary exposure groups (0 [control], 0.05, 0.1, and 0.3 mg/kg wet wt/d of MeHg) of postfledging white ibises (Eudocimus albus) in a divided, free-flight aviary that spanned the estimated range of environmental exposure for this species. Fecal samples were collected from individually identified ibises over six months in 2005 and processed for hormone evaluation. Significant sex-related differences in fecal estradiol concentrations, though unpredicted in direction, suggest that this steroid could be related to juvenile development in this species. Using repeated-measures general linear models, we tested a set of candidate models to explain variation in endocrine expression. We found that MeHg exposure led to significant differences in fecal estradiol concentrations between the control and medium-dose groups, whereas differences in fecal corticosterone concentrations were observed between the control and both the low- and high-dose groups. These results suggest highly nonlinear dose-response patterns for MeHg. Many endocrine-disrupting contaminants are theorized to affect multiple endpoints in a nonlinear manner, making results difficult to interpret using a traditional toxicological approach. The evidence presented here suggests that endocrine effects of MeHg exposure could behave similarly. [source] How to deal with multiple endpoints in clinical trialsFUNDAMENTAL & CLINICAL PHARMACOLOGY, Issue 6 2006Markus Neuhäuser Abstract Multiple endpoints are common in clinical trials. This article discusses statistical methods that can be applied to control the rate of false positive conclusions at an acceptable level. The considered methods include the Bonferroni adjustment and related methods, the intersection-union test, ordered hypotheses and gatekeeper procedures, composite endpoints and global assessment measures, closed testing procedures, and combinations of different approaches. [source] Avoidable burden of disease: conceptual and methodological issues in substance abuse epidemiologyINTERNATIONAL JOURNAL OF METHODS IN PSYCHIATRIC RESEARCH, Issue 4 2006Jürgen Rehm Abstract Determining the proportion of avoidable disease burden attributable to substance use is important for both policy development and intervention implementation. Current epidemiological theory has in principle provided a method to estimate avoidable burden of disease and the available statistical tools can provide first rough estimates. The method described in this paper, and its statistical procedures, are exemplified to estimate avoidable burden of tobacco-related disease in Canada. However, further effort is needed to find solutions in the methodological details, namely exposure measurement, risk factor multidimensionality, estimation of changes in exposure distribution over time, and estimation of risk relationships from multiple exposures changing over time with multiple endpoints (causal webs). The impetus to begin refining methods to obtain better starting points for estimating avoidable burden of disease is obvious and should be carried through in order to see real changes through evidence-based policy and intervention. Copyright © 2006 John Wiley & Sons, Ltd. [source] Power and sample size when multiple endpoints are consideredPHARMACEUTICAL STATISTICS: THE JOURNAL OF APPLIED STATISTICS IN THE PHARMACEUTICAL INDUSTRY, Issue 3 2007Stephen Senn Abstract A common approach to analysing clinical trials with multiple outcomes is to control the probability for the trial as a whole of making at least one incorrect positive finding under any configuration of true and false null hypotheses. Popular approaches are to use Bonferroni corrections or structured approaches such as, for example, closed-test procedures. As is well known, such strategies, which control the family-wise error rate, typically reduce the type I error for some or all the tests of the various null hypotheses to below the nominal level. In consequence, there is generally a loss of power for individual tests. What is less well appreciated, perhaps, is that depending on approach and circumstances, the test-wise loss of power does not necessarily lead to a family wise loss of power. In fact, it may be possible to increase the overall power of a trial by carrying out tests on multiple outcomes without increasing the probability of making at least one type I error when all null hypotheses are true. We examine two types of problems to illustrate this. Unstructured testing problems arise typically (but not exclusively) when many outcomes are being measured. We consider the case of more than two hypotheses when a Bonferroni approach is being applied while for illustration we assume compound symmetry to hold for the correlation of all variables. Using the device of a latent variable it is easy to show that power is not reduced as the number of variables tested increases, provided that the common correlation coefficient is not too high (say less than 0.75). Afterwards, we will consider structured testing problems. Here, multiplicity problems arising from the comparison of more than two treatments, as opposed to more than one measurement, are typical. We conduct a numerical study and conclude again that power is not reduced as the number of tested variables increases. Copyright © 2007 John Wiley & Sons, Ltd. [source] A Note on One-Sided Tests with Multiple EndpointsBIOMETRICS, Issue 1 2004Michael D. Perlman Summary. Testing problems with multivariate one-sided alternative hypotheses are common in clinical trials with multiple endpoints. In the case of comparing two treatments, treatment 1 is often preferred if it is superior for at least one of the endpoints and not biologically inferior for the remaining endpoints. Bloch et al. (2001, Biometrics57, 1039,1047) propose an intersection,union test (IUT) for this testing problem, but their test does not utilize the appropriate multivariate one-sided test. In this note we modify their test by an alternative IUT that does utilize the appropriate one-sided test. Empirical and graphical evidence show that the proposed test is more appropriate for this testing problem. 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