Methodology for the Calculation of Annualized Incremental Risks in Systems of Dams

In the past few years, the field of dam safety has approached risk informed methodologies throughout the world and several methodologies and programs are appearing to aid in the systematization of the calculations. The most common way of implementing these calculations is through the use of event trees, computing event probabilities, and incremental consequences. This methodology is flexible enough for several situations, but its generalization to the case of systems of several dams is complex and its implementation in a completely general calculation methodology presents some problems. Retaining the event tree framework, a new methodology is proposed to calculate incremental risks. The main advantage of this proposed methodology is the ease with which it can be applied to systems of several dams: with a single risk model that describes the complete system and with a single calculation the incremental risks of the system can be obtained, being able to allocate the risk of each dam and of each failure mode. The article shows how both methodologies are equivalent and also applies them to a case study.     

A Bayesian Evidence Synthesis for Estimating Campylobacteriosis Prevalence

Stakeholders making decisions in public health and world trade need improved estimations of the burden‐of‐illness of foodborne infectious diseases. In this article, we propose a Bayesian meta‐analysis or more precisely a Bayesian evidence synthesis to assess the burden‐of‐illness of campylobacteriosis in France. Using this case study, we investigate campylobacteriosis prevalence, as well as the probabilities of different events that guide the disease pathway, by (i) employing a Bayesian approach on French and foreign human studies (from active surveillance systems, laboratory surveys, physician surveys, epidemiological surveys, and so on) through the chain of events that occur during an episode of illness and (ii) including expert knowledge about this chain of events. We split the target population using an exhaustive and exclusive partition based on health status and the level of disease investigation. We assume an approximate multinomial model over this population partition. Thereby, each observed data set related to the partition brings information on the parameters of the multinomial model, improving burden‐of‐illness parameter estimates that can be deduced from the parameters of the basic multinomial model. This multinomial model serves as a core model to perform a Bayesian evidence synthesis. Expert knowledge is introduced by way of pseudo‐data. The result is a global estimation of the burden‐of‐illness parameters with their accompanying uncertainty.     

Regularized Posteriors in Linear Ill‐Posed Inverse Problems

Abstract. We study the Bayesian solution of a linear inverse problem in a separable Hilbert space setting with Gaussian prior and noise distribution. Our contribution is to propose a new Bayes estimator which is a linear and continuous estimator on the whole space and is stronger than the mean of the exact Gaussian posterior distribution which is only defined as a measurable linear transformation. Our estimator is the mean of a slightly modified posterior distribution called regularized posterior distribution. Frequentist consistency of our estimator and of the regularized posterior distribution is proved. A Monte Carlo study and an application to real data confirm good small‐sample properties of our procedure.     

Reproducibility of clinical data I: continuous outcomes

Across a variety of clinical settings, repeated measurements on an individual, obtained under identical circumstances, often differ from one another. This implies the measurements lack perfect reproducibility. Topics related to reproducibility of clinical measurements are introduced in this paper. In this first of two parts, continuous outcomes are addressed. The intraclass correlation coefficient, ρ, has been the traditional coefficient of reproducibility for continuous outcomes. The importance of ρ regarding observations on an individual, and observations among populations, is outlined. Estimation and inferential procedures for ρ are reviewed and worked examples are provided. Copyright © 2004 John Wiley & Sons, Ltd.     

A note on sample size savings with the use of a single well‐controlled clinical trial to support the efficacy of a new drug

In terms of the risk of making a Type I error in evaluating a null hypothesis of equality, requiring two independent confirmatory trials with two‐sided p‐values less than 0.05 is equivalent to requiring one confirmatory trial with two‐sided p‐value less than 0.001 25. Furthermore, the use of a single confirmatory trial is gaining acceptability, with discussion in both ICH E9 and a CPMP Points to Consider document. Given the growing acceptance of this approach, this note provides a formula for the sample size savings that are obtained with the single clinical trial approach depending on the levels of Type I and Type II errors chosen. For two replicate trials each powered at 90%, which corresponds to a single larger trial powered at 81%, an approximate 19% reduction in total sample size is achieved with the single trial approach. Alternatively, a single trial with the same sample size as the total sample size from two smaller trials will have much greater power. For example, in the case where two trials are each powered at 90% for two‐sided α=0.05 yielding an overall power of 81%, a single trial using two‐sided α=0.001 25 would have 91% power. Copyright © 2004 John Wiley & Sons, Ltd.     

Two‐sample empirical likelihood ratio tests for medians in application to biomarker evaluations

The median is a commonly used parameter to characterize biomarker data. In particular, with two vastly different underlying distributions, comparing medians provides different information than comparing means; however, very few tests for medians are available. We propose a series of two‐sample median‐specific tests using empirical likelihood methodology and investigate their properties. We present the technical details of incorporating the relevant constraints into the empirical likelihood function for in‐depth median testing. An extensive Monte Carlo study shows that the proposed tests have excellent operating characteristics even under unfavourable occasions such as non‐exchangeability under the null hypothesis. We apply the proposed methods to analyze biomarker data from Western blot analysis to compare normal cells with bronchial epithelial cells from a case–control study. The Canadian Journal of Statistics 39: 671–689; 2011. © 2011 Statistical Society of Canada     

Reporting cumulative proportion of subjects with an adverse event based on data from multiple studies

Experience has shown us that when data are pooled from multiple studies to create an integrated summary, an analysis based on naïvely‐pooled data is vulnerable to the mischief of Simpson's Paradox. Using the proportions of patients with a target adverse event (AE) as an example, we demonstrate the Paradox's effect on both the comparison and the estimation of the proportions. While meta analytic approaches have been recommended and increasingly used for comparing safety data between treatments, reporting proportions of subjects experiencing a target AE based on data from multiple studies has received little attention. In this paper, we suggest two possible approaches to report these cumulative proportions. In addition, we urge that regulatory guidelines on reporting such proportions be established so that risks can be communicated in a scientifically defensible and balanced manner. Copyright © 2010 John Wiley & Sons, Ltd.     

ROC Curves in Non‐Parametric Location‐Scale Regression Models

Abstract. The receiver operating characteristic (ROC) curve is a tool of extensive use to analyse the discrimination capability of a diagnostic variable in medical studies. In certain situations, the presence of a covariate related to the diagnostic variable can increase the discriminating power of the ROC curve. In this article, we model the effect of the covariate over the diagnostic variable by means of non‐parametric location‐scale regression models. We propose a new non‐parametric estimator of the conditional ROC curve and study its asymptotic properties. We also present some simulations and an illustration to a data set concerning diagnosis of diabetes.     

Some tests for uniformity of circular distributions powerful against multimodal alternatives

The author introduces new statistics suited for testing uniformity of circular distributions and powerful against multimodal alternatives. One of them has a simple expression in terms of the geometric mean of the sample of chord lengths. The others belong to a family indexed by a continuous parameter. The asymptotic distributions under the null hypothesis are derived. We compare the power of the new tests against Stephens's alternatives with those of Ajne, Watson, and Hermans‐Rasson's tests. Some of the new tests are the most powerful when the alternative has three or four modes. A heuristic justification of this feature is given. An application to the analysis of archaeological data is provided. The Canadian Journal of Statistics 38:80–96; 2010 © 2010 Statistical Society of Canada     

Smoothed empirical likelihood confidence intervals for the relative distribution with left‐truncated and right‐censored data

The study of differences among groups is an interesting statistical topic in many applied fields. It is very common in this context to have data that are subject to mechanisms of loss of information, such as censoring and truncation. In the setting of a two‐sample problem with data subject to left truncation and right censoring, we develop an empirical likelihood method to do inference for the relative distribution. We obtain a nonparametric generalization of Wilks' theorem and construct nonparametric pointwise confidence intervals for the relative distribution. Finally, we analyse the coverage probability and length of these confidence intervals through a simulation study and illustrate their use with a real data set on gastric cancer. The Canadian Journal of Statistics 38: 453–473; 2010 © 2010 Statistical Society of Canada