Falsification or choice among alternatives: The unsolved dilemma of hypothesis testing

Summary The scientific attitude towards statistical method has always pursued two basic objectives: identifying false assumptions and selecting, amongst the likely assertions, those which are most consistent with a given system. The methodological demarcation between rejection of a statistical statement, because it is ?false?, or exclusion, because it is ?least probable?, lies in the fundamental premises of inferential procedures. In the first class we find the methods proposed by Fisher, Neyman and Pearson; in the second one, the Bayesian techniques. Even if different inferential theories may coexist, any particular solution has a limit of validity strictly bouded, to the conventional procedural rules on which it is based. Invited paper at the Conference on ?Statistical Tests: Methodology and Econometric Applications?, held in Bologna, Italy, 27–28 May 1993.     

Estimand framework: Are we asking the right questions? A case study in the solid tumor setting

The estimand framework requires a precise definition of the clinical question of interest (the estimand) as different ways of accounting for “intercurrent” events post randomization may result in different scientific questions. The initiation of subsequent therapy is common in oncology clinical trials and is considered an intercurrent event if the start of such therapy occurs prior to a recurrence or progression event. Three possible ways to account for this intercurrent event in the analysis are to censor at initiation, consider recurrence or progression events (including death) that occur before and after the initiation of subsequent therapy, or consider the start of subsequent therapy as an event in and of itself. The new estimand framework clarifies that these analyses address different questions (“does the drug delay recurrence if no patient had received subsequent therapy?” vs “does the drug delay recurrence with or without subsequent therapy?” vs “does the drug delay recurrence or start of subsequent therapy?”). The framework facilitates discussions during clinical trial planning and design to ensure alignment between the key question of interest, the analysis, and interpretation. This article is a result of a cross-industry collaboration to connect the International Council for Harmonisation E9 addendum concepts to applications. Data from previously reported randomized phase 3 studies in the renal cell carcinoma setting are used to consider common intercurrent events in solid tumor studies, and to illustrate different scientific questions and the consequences of the estimand choice for study design, data collection, analysis, and interpretation.     

Measuring the impact of the Web: Rasch modelling for survey evaluation

In 2012, the World Wide Web Foundation launched for the first time the Web Index (WI), which combines the existing secondary data with new primary data to rank countries according to their progress and use of the Web. Primary data are gathered via a multi-country specifically designed questionnaire. The aim of our analysis is (1) to evaluate the measurement properties of the expert assessment survey and to provide survey designers with some insights into possible problematic questions and/or unexpectedly behaving countries and (2) to assess the experts’ perception of the state and the value of the Web. To do so the Rating Scale Rasch model is employed. Results show that about 10% of survey questions are detected as misfitting and need to be reworded. Possible reasons are: counter-orientation with respect to the WI polarity, difficulty in understanding the question's words or binary instead of the multiple response scale. Country analysis shows that no country can be considered as an outlier due to notable unexpected pattern of answers. Since the survey is to be expanded in future editions of the WI, the results of our analysis are very important in pin-pointing the questions most in need of refinement for the next edition of the Index.     

Object-Oriented Bayesian Networks for Modeling the Respondent Measurement Error

In this article, Object-Oriented Bayesian Networks (OOBN) are proposed as a tool to model measurement errors in a categorical variable due to respondent. A mixed measurement error model is presented and an OOBN implementing such a model is introduced. The insertion of evidence represented by the observed value and its propagation throughout the network yields for each unit the probability distribution of the true value given the observed. Two methods are used to predict the individual true value and their performance is evaluated via simulation.     

Hierarchical Bayesian Models for the Estimation of Correlated Effects in Multilevel Data: A Simulation Study to Assess Model Performance

In this article, we aim at assessing hierarchical Bayesian modeling for the analysis of multiple exposures and highly correlated effects in a multilevel setting. We exploit an artificial data set to apply our method and show the gains in the final estimates of the crucial parameters. As a motivating example to simulate data, we consider a real prospective cohort study designed to investigate the association of dietary exposures with the occurrence of colon-rectum cancer in a multilevel framework, where, e.g., individuals have been enrolled from different countries or cities. We rely on the presence of some additional information suitable to mediate the final effects of the exposures and to be arranged in a level-2 regression to model similarities among the parameters of interest (e.g., data on the nutrient compositions for each dietary item).     

Parameterized complexity of k-anonymity: hardness and tractability

The problem of publishing personal data without giving up privacy is becoming increasingly important. A precise formalization that has been recently proposed is the k-anonymity, where the rows of a table are partitioned into clusters of sizes at least k and all rows in a cluster become the same tuple after the suppression of some entries. The natural optimization problem, where the goal is to minimize the number of suppressed entries, is hard even when the stored values are over a binary alphabet or the table consists of a bounded number of columns. In this paper we study how the complexity of the problem is influenced by different parameters. First we show that the problem is W[1]-hard when parameterized by the value of the solution (and k). Then we exhibit a fixed-parameter algorithm when the problem is parameterized by the number of columns and the number of different values in any column. Finally, we prove that k-anonymity is still APX-hard even when restricting to instances with 3 columns and k=3.     

Health for immigrants and refugees in the 1990s. A comparative study in seven receiving countries

Health policies for immigrants implemented in seven receiving countries (France, United Kingdom, Switzerland, Italy, Sweden, United States, and Canada) have been comparatively reviewed, taking into account the structure of the health system as well as immigration policy. Countries where immigrant communities have access to political representation, and whose health systems are comprehensive in scope and coverage, tend to have adopted specific health policies for immigrants, which, in turn, led to better access to health services and more information for planning and evaluation.