Addressing Unusual Assay Variability with Robust Statistics

Well-behaved, in vitro bioassays generally produce normally distributed values in their primary (efficacy) data. Accordingly, the best practices for statistical analysis are well documented and understood. However, assays may occasionally display unusually high variability and fall outside the assumptions inherent in these standard analyses. These assays may still be in the optimization phase, in which the source of variation could be identified and addressed. They might also represent the best available option to address the biological process being examined. In these cases, the use of robust statistical methods may provide a more appropriate set of tools for both data analysis and assay optimization. This article provides guidance on best practices for the use of robust statistical methods for the analysis of bioassay data as an alternative to standard methods. Impacts on experimental design and interpretation will be discussed.

Quantifying the impact of the COVID-19 pandemic on cancer screenings and diagnoses

COVID-19 impacted hospital systems across the globe. Focus shifted to responding to increased healthcare demand while mitigating COVID-19 spread on their campuses. Mitigation efforts limited medical professional-patient interactions, including patient access to preventive cancer screenings. This study tested five hypotheses: H1: Cancer screenings significantly decreased during North Carolina’s (NC) Stay-At-Home (SAH) orders; H2: Cancer diagnoses significantly decreased during NC’s SAH orders; H3: Cancer screenings significantly increased after the end of NC’s SAH orders; H4: Cancer diagnoses significantly increased after the end of NC’s SAH orders; and H5: Advanced cancer diagnoses significantly increased after the end of NC’s stay-at-home orders. Time series regression analysis was employed to quantify trends. Results suggested strong support of H1 and H3, moderate support of H4, mixed support of H5, and no support of H2. Implications of employing robust statistical methods to quantify trends in screenings or diagnoses during periods of health system disruption are discussed.

Editorial, special issue on “Advances in Robust Statistics”

Starting with 2020 volume, the journal Metron has decided to celebrate the centenary since its foundation with three special issues. This volume is dedicated to robust statistics. A striking feature of most applied statistical analyses is the use of methods that are well known to be sensitive to outliers or to other departures from the postulated model. Robust statistical methods provide useful tools for reducing this sensitivity, through the detection of the outliers by first fitting the majority of the data and then by flagging deviant data points. The six papers in this issue cover a wide orientation in all fields of robustness. This editorial first provides some facts about the history and current state of robust statistics and then summarizes the contents of each paper.

COVID-19 Vaccine Hesitancy and Its Associated Factors in Japan

The vaccine confidence index in Japan is one of the lowest worldwide. This study aimed to examine the proportion of COVID-19 vaccine hesitancy in the Japanese population using a larger sample and more robust statistical methods than previously, and to identify factors associated with vaccine hesitancy. We conducted a nationwide, cross-sectional Internet survey on 8–26 February 2021, and calculated the proportion and odds ratios for vaccine hesitancy. Among 23,142 responses analyzed, the proportion of COVID-19 vaccine hesitancy was 11.3% (10.9–11.7%). The proportion was higher among younger respondents and female respondents, and especially among younger female respondents (15.6%) compared with the lowest proportion among older male respondents (4.8%). The most cited reason for not getting vaccinated was concerns about adverse reactions in more than 70% of the respondents. The proportion of COVID-19 vaccine hesitancy in Japan was comparable to that in previous studies overseas, and the proportion among younger respondents was more than double that among older respondents. Factors associated with the hesitancy were female sex, living alone, low socioeconomic status, and presence of severe psychological distress, especially among older respondents. Thus, adequate measures should be taken to ensure that vaccines are delivered to people with these factors.

Intraprocedural Angiographic Signs Observed During Endovascular Thrombectomy in Patients With Acute Ischemic Stroke: A Systematic Review

Objective:In the real-world practice of endovascular thrombectomy (EVT) for acute ischemic stroke (AIS), the analysis of intraprocedural angiographic signs (IPASs) still challenges neurointerventionists. This review provides insights into the significance of these subtle changes for predicting underlying etiology, technical feasibility and patient prognosis, thus promoting the potential real-time application of these signs.Methods:A systematic literature search was conducted using PubMed, Ovid Medline/Embase, and Cochrane. The search focused on studies published between January 1995 and August 2020 that reported findings related to intraprocedural angiographic manifestations in endovascular recanalization therapy for AIS.Results:We identified 12 IPASs in 22 studies involving 1683 patients. The IPASs were assigned into 3 subsets according to their clinical meanings.Conclusion:The systematic analysis of IPAS in clinical trials and practice will lead to a better understanding of treatment effects, responses, and mechanisms during EVT. Studies of larger cohorts using more robust statistical methods are needed.

Reliable and accurate diagnostics from highly multiplexed sequencing assays

Scalable, inexpensive, and secure testing for SARS-CoV-2 infection is crucial for control of the novel coronavirus pandemic. Recently developed highly multiplexed sequencing assays (HMSAs) that rely on high-throughput sequencing can, in principle, meet these demands, and present promising alternatives to currently used RT-qPCR-based tests. However, reliable analysis, interpretation, and clinical use of HMSAs requires overcoming several computational, statistical and engineering challenges. Using recently acquired experimental data, we present and validate a computational workflow based on kallisto and bustools, that utilizes robust statistical methods and fast, memory efficient algorithms, to quickly, accurately and reliably process high-throughput sequencing data. We show that our workflow is effective at processing data from all recently proposed SARS-CoV-2 sequencing based diagnostic tests, and is generally applicable to any diagnostic HMSA.

Twelve-Year Analysis of NO2 Concentration Measurements at Belisario Station (Quito, Ecuador) Using Statistical Inference Techniques

In this paper, a robust analysis of nitrogen dioxide (NO2) concentration measurements taken at Belisario station (Quito, Ecuador) was performed. The data used for the analysis constitute a set of measurements taken from 1 January 2008 to 31 December 2019. Furthermore, the analysis was carried out in a robust way, defining variables that represent years, months, days and hours, and classifying these variables based on estimates of the central tendency and dispersion of the data. The estimators used here were classic, nonparametric, based on a bootstrap method, and robust. Additionally, confidence intervals based on these estimators were built, and these intervals were used to categorize the variables under study. The results of this research showed that the NO2 concentration at Belisario station is not harmful to humans. Moreover, it was shown that this concentration tends to be stable across the years, changes slightly during the days of the week, and varies greatly when analyzed by months and hours of the day. Here, the precision provided by both nonparametric and robust statistical methods served to comprehensively proof the aforementioned. Finally, it can be concluded that the city of Quito is progressing on the right path in terms of improving air quality, because it has been shown that there is a decreasing tendency in the NO2 concentration across the years. In addition, according to the Quito Air Quality Index, most of the observations are in either the desirable level or acceptable level of air pollution, and the number of observations that are in the desirable level of air pollution increases across the years.

Robust statistical methods for point of sale designs, the example of healthcare snack and drink vending

Purpose The experiment introduces nutritional labelling, healthier products and product placement designs to the hospital vending machines, to promote healthy lifestyles. Design/methodology/approach The site where this experiment happens is a major London hospital, serving over a million patients every year. In the experiment, the hospital’s snack and drink vending machines are redesigned. The impact on product sales are then analysed using robust statistical methods. Findings Nutritional labelling has a statistically significant impact on product sales. Less of the unhealthiest products are sold. Healthier products and product placement designs have a larger impact but with less statistical significance. They require further testing. Research limitations/implications Experts in service operations can use this experiment’s regression modelling methods. The methods are ideal for measuring change over time in counting data sets in complex real world environments. Practical implications There are suggestions for practical vending service change in this research. They are in line and add a practical example to Government policy guidance. Social implications People using the redesigned vending machines have more opportunity for healthy lifestyle choices. Originality/value The experiment provides statistical evidence in support of catering for healthier lifestyles.

The usefulness of robust multivariate methods: A case study with the menu items of a fast food restaurant chain

Multivariate statistical methods have been playing an important role in statistics and data analysis for a very long time. Nowadays, with the increase in the amounts of data collected every day in many disciplines, and with the raise of data science, machine learning and applied statistics, that role is even more important. Two of the most widely used multivariate statistical methods are cluster analysis and principal component analysis. These, similarly to many other models and algorithms, are adequate when the data satisfies certain assumptions. However, when the distribution of the data is not normal and/or it shows heavy tails and outlying observations, the classic models and algorithms might produce erroneous conclusions. Robust statistical methods such as algorithms for robust cluster analysis and for robust principal component analysis are of great usefulness when analyzing contaminated data with outlying observations. In this paper we consider a data set containing the products available in a fast food restaurant chain together with their respective nutritional information, and discuss the usefulness of robust statistical methods for classification, clustering and data visualization.

Robust Estimation of Carbon Monoxide Measurements

This paper presents a robust analysis of carbon monoxide (CO) concentration measurements conducted at the Belisario air-quality monitoring station (Quito, Ecuador). For the analysis, the data collected from 1 January 2008 to 31 December 2019 were considered. Additionally, each of the twelve years analyzed was considered as a random variable, and robust location and scale estimators were used to estimate the central tendency and dispersion of the data. Furthermore, classic, nonparametric, bootstrap, and robust confidence intervals were used to group the variables into categories. Then, differences between categories were quantified using confidence intervals and it was shown that the trend of CO concentration at the Belisario station in the last twelve years is downward. The latter was proven with the precision provided by both nonparametric and robust statistical methods. The results of the research work robustly proved that the CO concentration at Belisario station in the last twelve years is not considered a health risk, according to the criteria established by the Quito Air Quality Index.