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Many assessment instruments used in the evaluation of toxicity, safety, pain, or disease progression consider multiple ordinal endpoints to fully capture the presence and severity of treatment effects. Contingency tables underlying these correlated responses are often sparse and imbalanced, rendering asymptotic results unreliable or model fitting prohibitively complex without overly simplistic assumptions on the marginal and joint distribution. Instead of a modeling approach, we look at stochastic order and marginal inhomogeneity as an expression or manifestation of a treatment effect under much weaker assumptions. Often, endpoints are grouped together into physiological domains or by the body function they describe. We derive tests based on these subgroups, which might supplement or replace the individual endpoint analysis because they are more powerful. The permutation or bootstrap distribution is used throughout to obtain global, subgroup, and individual significance levels as they naturally incorporate the correlation among endpoints. We provide a theorem that establishes a connection between marginal homogeneity and the stronger exchangeability assumption under the permutation approach. Multiplicity adjustments for the individual endpoints are obtained via stepdown procedures, while subgroup significance levels are adjusted via the full closed testing procedure. The proposed methodology is illustrated using a collection of 25 correlated ordinal endpoints, grouped into six domains, to evaluate toxicity of a chemical compound. [ABSTRACT FROM AUTHOR]

Several goodness-of-fit tests of a lifetime distribution have been suggested in the literature; many take into account censoring and/or truncation of event times. In some contexts, a goodness-of-fit test for the truncation distribution is of interest. In particular, better estimates of the lifetime distribution can be obtained when knowledge of the truncation law is exploited. In cross-sectional sampling, for example, there are theoretical justifications for the assumption of a uniform truncation distribution, and several studies have used it to improve the efficiency of their survival estimates. The duality of lifetime and truncation in the absence of censoring enables methods for testing goodness of fit of the lifetime distribution to be used for testing goodness of fit of the truncation distribution. However, under random censoring, this duality does not hold and different tests are required. In this article, we introduce several goodness-of-fit tests for the truncation distribution and investigate their performance in the presence of censored event times using simulation. We demonstrate the use of our tests on two data sets. [ABSTRACT FROM AUTHOR]

The objective of this article is to develop a hypothesis-testing procedure to determine whether a common source outbreak has ended. We consider the case when neither the calendar date of exposure to the pathogen nor the exact incubation period distribution is known. The hypothesis-testing procedure is based on the spacings between ordered calendar dates of disease onset of the cases. A simulation study was performed to evaluate the robustness of the methods to various models for the incubation period of infectious diseases. We investigated the impact of multiple testing on the overall outbreak-wise type I error probability. We derive expressions for the outbreak-wise type I error probability and show that multiple testing has minimal effect on inflating that error probability. The results are discussed in the context of the 2001 U.S. anthrax outbreak. [ABSTRACT FROM AUTHOR]

Asymptotic Distribution of Score Statistics for Spatial Cluster Detection with Censored Data Cook et Lee (20007) ont étendu la statistique de scan de Kulldorff (1997) pour la détection de clusters à partir de données de survie. Leur approche était basée sur la statistique du score et ils on proposé une distribution de permutation pour le maximum des score tests. L’utilisation de la statistique du score permet d’introduire des variables explicatives. Cependant nous montrons que la distribution de permutation demande des hypothèses fortes d’indépendance entre les clusters potentiels et à la fois la variable de censure et les variables explicatives. Nous présentons une approche utilisant la distribution asymptotique du maximum des statistique de score qui ne demande pas ces hypothèses. Inference for Proportions in a 2 × 2 Contingency Table: HPD or not HPD? Les intervalles fondés sur la densité a posteriori la plus élevée sont courants en inférence bayésienne, mais ils ne sont pas invariants après transformations, comme noté par Agresti et Min (2005). Agresti & Min suggèrent de préférer l’emploi d’intervalles centraux ou ≪en queue≫ dans le contexte du risque relatif et celui de l’odds-ratio. Nous proposons une modification pour des résultats extrêmes, l’invariance étant assurée en remplaçant les intervalles centraux par des intervalles unilatéraux. Les a priori de bayes-Laplace pour le paramètre binomial apparaissent ici préférables aux à priori de Jeffrey, contrairement à la suggestion d’Agresti et Min, basée sur la couverture fréquentiste. [ABSTRACT FROM AUTHOR]