Rapid Web-Based Recruitment of Patients With Psoriasis: Multinational Cohort Study

BACKGROUND

Wide-ranging patient recruitment not restricted to the location of the investigator will provide a better representation of the patient population in clinical studies.

OBJECTIVE

Our goal was to assess the feasibility of a broad web-based recruitment strategy in an 8-week observational study of 500 study participants with psoriasis and healthy controls from locations remote from the investigator and to assess the cost associated with each participant.

METHODS

A decentralized team in Denmark recruited patients with psoriasis and healthy controls using Google and Facebook advertisements and posts to Facebook groups. All individuals were screened via the internet, and patients diagnosed with psoriasis were included. Questionnaires regarding itch and sleep were completed by both groups at inclusion, week 4, and week 8.

RESULTS

During a 2-week recruitment period, 12,887 unique advertisement views were registered, and 839 participants were enrolled, of which 507 completed the study (220 with psoriasis and 287 healthy controls) with a retention rate of 60.4%. Participants were recruited from 11 different countries on 4 separate continents, mainly from the United States, Canada, and the United Kingdom. The recruitment rate was 59.9 participants per day, and the conversion rate was 57.2%. Recruitment costs were US $13 per enrolled participant and US $22 per participant completing the study.

CONCLUSIONS

It is feasible and rapid to recruit a large number of participants from locations different from the investigator and to retain patients in an observational study with no visits to a clinical site at low costs.

A multi-method approach for the assessment of natural background levels in groundwater

The assessment of geochemical Natural Background Levels (NBLs) in groundwater, aims at distinguishing the naturally high levels of geogenic compounds from anthropogenic pollution. This is a fundamental issue in groundwater management, in particular when the concentration of inorganic compounds exceeds the threshold values set for the evaluation of the groundwater chemical status, as requested by environmental regulations. In this paper, we describe a new procedure that integrates the pre-selection method and statistical techniques, using the example of two case studies. The pre-selection aims to identify suitable groundwater samples for the NBLs assessment. The nitrate concentration threshold, for the removal of the groundwater samples affected by human activities, is established locally through different graphical and statistical approaches. Then, the statistical distribution of each compound is analyzed and the outliers are identified. Normality tests on the datasets allow one to select the most appropriate value, e.g. one percentile, to be adopted as NBL within the data distribution. In the selected case studies, we have defined the NBLs for As, F, Mn, Fe and SO₄. The two sites are part of a volcanic-sedimentary aquifer in central Italy, where the geochemical background is frequently well above the standards for human consumption. The results of the simple and easily reproducible pre-selection method are strengthened by integration with statistical techniques, notably in selecting the appropriate percentile. New criteria are suggested for the choice of the nitrate threshold to be used for the pre-selection of uncontaminated samples.

The Doctor Is In! Diagnostic Analysis

To make meaningful inferences based on our regression models, we must ensure that we have met the necessary assumptions of these tests. In this commentary, we review these assumptions and those for the t-test and analysis of variance, and introduce a variety of methods, formal and informal, numeric and visual, for assessing conformity with the assumptions.

GTest: a software tool for graphical assessment of empirical distributions' Gaussianity

In the present paper, the novel software GTest is introduced, designed for testing the normality of a user-specified empirical distribution. It has been implemented with two unusual characteristics; the first is the user option of selecting four different versions of the normality test, each of them suited to be applied to a specific dataset or goal, and the second is the inferential paradigm that informs the output of such tests: it is basically graphical and intrinsically self-explanatory. The concept of inference-by-eye is an emerging inferential approach which will find a successful application in the near future due to the growing need of widening the audience of users of statistical methods to people with informal statistical skills. For instance, the latest European regulation concerning environmental issues introduced strict protocols for data handling (data quality assurance, outliers detection, etc.) and information exchange (areal statistics, trend detection, etc.) between regional and central environmental agencies. Therefore, more and more frequently, laboratory and field technicians will be requested to utilize complex software applications for subjecting data coming from monitoring, surveying or laboratory activities to specific statistical analyses. Unfortunately, inferential statistics, which actually influence the decisional processes for the correct managing of environmental resources, are often implemented in a way which expresses its outcomes in a numerical form with brief comments in a strict statistical jargon (degrees of freedom, level of significance, accepted/rejected H0, etc.). Therefore, often, the interpretation of such outcomes is really difficult for people with poor statistical knowledge. In such framework, the paradigm of the visual inference can contribute to fill in such gap, providing outcomes in self-explanatory graphical forms with a brief comment in the common language. Actually, the difficulties experienced by colleagues and their request for an effective tool for addressing such difficulties motivated us in adopting the inference-by-eye paradigm and implementing an easy-to-use, quick and reliable statistical tool. GTest visualizes its outcomes as a modified version of the Q-Q plot. The application has been developed in Visual Basic for Applications (VBA) within MS Excel 2010, which demonstrated to have all the characteristics of robustness and reliability needed. GTest provides true graphical normality tests which are as reliable as any statistical quantitative approach but much easier to understand. The Q-Q plots have been integrated with the outlining of an acceptance region around the representation of the theoretical distribution, defined in accordance with the alpha level of significance and the data sample size. The test decision rule is the following: if the empirical scatterplot falls completely within the acceptance region, then it can be concluded that the empirical distribution fits the theoretical one at the given alpha level. A comprehensive case study has been carried out with simulated and real-world data in order to check the robustness and reliability of the software.

A guide to genome-wide association analysis and post-analytic interrogation

This tutorial is a learning resource that outlines the basic process and provides specific software tools for implementing a complete genome‐wide association analysis. Approaches to post‐analytic visualization and interrogation of potentially novel findings are also presented. Applications are illustrated using the free and open‐source R statistical computing and graphics software environment, Bioconductor software for bioinformatics and the UCSC Genome Browser. Complete genome‐wide association data on 1401 individuals across 861,473 typed single nucleotide polymorphisms from the PennCATH study of coronary artery disease are used for illustration. All data and code, as well as additional instructional resources, are publicly available through the Open Resources in Statistical Genomics project: http://www.stat-gen.org. © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.