What do (some of) our association measures measure (most)? Association?

This paper discusses the degree to which some of the most widely-used measures of association in corpus linguistics are not particularly valid in the sense of actually measuring association rather than some amalgam of a lot of frequency and a little association. The paper demonstrates these issues on the basis of hypothetical and actual corpus data and outlines implications of the findings. I then outline how to design an association measure that only measures association and show that its behavior supports the use of the log odds ratio as a true association-only measure but separately from frequency; in addition, this paper sets the stage for an analogous review of dispersion measures in corpus linguistics.

Two-Dimensional Index of Departure from the Symmetry Model for Square Contingency Tables with Nominal Categories

In the analysis of two-way contingency tables, the degree of departure from independence is measured using measures of association between row and column variables (e.g., Yule’s coefficients of association and of colligation, Cramér’s coefficient, and Goodman and Kruskal’s coefficient). On the other hand, in the analysis of square contingency tables with the same row and column classifications, we are interested in measuring the degree of departure from symmetry rather than independence. Over past years, many studies have proposed various types of indexes based on their power divergence (or diversity index) to represent the degree of departure from symmetry. This study proposes a two-dimensional index to measure the degree of departure from symmetry in terms of the log odds of each symmetric cell with respect to the main diagonal of the table. By measuring the degree of departure from symmetry in terms of the log odds of each symmetric cell, the analysis results are easier to interpret than existing indexes. Numerical experiments show the utility of the proposed two-dimensional index. We show the usefulness of the proposed two-dimensional index by using real data.

Formulas, Algorithms and Examples for Binomial Distributed Data Confidence Interval Calculation: Excess Risk, Relative Risk and Odds Ratio

Medical studies often involve a comparison between two outcomes, each collected from a sample. The probability associated with, and confidence in the result of the study is of most importance, since one may argue that having been wrong with a percent could be what killed a patient. Sampling is usually done from a finite and discrete population and it follows a Bernoulli trial, leading to a contingency of two binomially distributed samples (better known as 2×2 contingency table). Current guidelines recommend reporting relative measures of association (such as the relative risk and odds ratio) in conjunction with absolute measures of association (which include risk difference or excess risk). Because the distribution is discrete, the evaluation of the exact confidence interval for either of those measures of association is a mathematical challenge. Some alternate scenarios were analyzed (continuous vs. discrete; hypergeometric vs. binomial), and in the main case—bivariate binomial experiment—a strategy for providing exact p-values and confidence intervals is proposed. Algorithms implementing the strategy are given.

Fundamentals of Epidemiology for the Audiologist

Purpose Epidemiology is the study of the distribution and determinants of disease and other health-related events in populations. An understanding of epidemiology among audiologists is important for ear and hearing health care practice. This tutorial presents an overview of the fundamental concepts of epidemiology for the practicing audiologist and audiology students. Method The authors provide an overview of epidemiology and focus on its applicability to audiology. The most common epidemiologic study designs, measures of occurrence, and measures of association are highlighted and discussed. Concepts related to p values, confidence intervals, confounding, and bias are introduced. Finally, the authors discuss screening as a means to control adverse hearing health outcomes. Conclusions Epidemiologic approaches are of value to the audiologist involved in the evidence-based decision processes of planning, monitoring, and treating individuals with ear and hearing problems. For audiologists to make practice recommendations based on epidemiologic data, they need insight into epidemiologic study design and interpretation of data from these studies. Understanding the fundamentals of epidemiology and applying epidemiologic principles to the clinical practice of audiology can increase the quality of care individuals receive.

1419Cancer mortality burden and quality of life in Argentina: geographical pattern and measures of association

Background Health outcomes are often related to the conditions in which people live. Previous studies indicated that cancer mortality is non-randomly distributed between social groups and regions in Argentina. We aimed to analyze geographical pattern of mortality due to the most prevalent cancers in Argentina (2013-2015), from a quality of life approach. Methods Age-standardized mortality rates (ASMR) for specific cancer types (breast, lung, prostate, colon, cervix, stomach) was estimated by direct method (2013-2015 period). Mortality maps at a departmental scale (n = 511) were constructed by using a Getis-Ord Hot Spot spatial analysis. A Quality of Life Index (QLI, continuous score) for Argentina 2010 was used. Poisson mixed models with a random intercept were performed to estimate IRRs (relative risk) as measures of association. Results Breast, lung, and colon cancers show the most clear ASMR geographical patterns, which locate an extensive cold spot (lower and correlated rates) in the northwestern region of Argentina and a hot spot (higher and correlated rates) in the Pampeana (center-east) region . Particularly, breast and cervix cancers showed confronting mortality geographic patterns and opposite relationships with the departmental QLI (IRR 1.23 and 0.78, respectively). A direct association was found for lung (IRR 1.46/1.15 for women/men) and colon cancer (IRR 1.57/1.49 for women/men). Conclusions Results evidence geographic disparities in cancer mortality burden linked to the quality of life of populations at departmental scale in Argentina. Key messages Geographic disparities in cancer mortality are linked to the quality of life in Argentina. Mortality-QLI relationship varies according the cancer type.

Can Vaccination Trigger Autoimmune Disorders? A Meta-Analysis

Vaccination as an important tool in the fight against infections has been suggested as a possible trigger of autoimmunity over the last decades. To confirm or refute this assumption, a Meta-analysis of Autoimmune Disorders Association With Immunization (MADAWI) was conducted. Included in the meta-analysis were a total of 144 studies published in 1968–2019 that were available in six databases and identified by an extensive literature search conducted on 30 November 2019. The risk of bias classification of the studies was performed using the Newcastle–Ottawa Quality Assessment Scale. The strength of evidence was assessed using the Grading of Recommendations Assessment, Development, and Evaluation. While our primary analysis was conducted in terms of measures of association employed in studies with a low risk of bias, the robustness of the MADAWI outcome was tested using measures independent of each study risk of bias. Additionally, subgroup analyses were performed to determine the stability of the outcome. The pooled association of 0.99 (95% confidence interval, 0.97–1.02), based on a total of 364 published estimates, confirmed an equivalent occurrence of autoimmune disorders in vaccinated and unvaccinated persons. The same level of association reported by studies independently of the risk of bias was supported by a sufficient number of studies, and no serious limitation, inconsistency, indirectness, imprecision, and publication bias. A sensitivity analysis did not reveal any discrepancy in the primary result. Current common vaccination is not the cause of any of the examined autoimmune disorders in the medium and long terms.

Association of Exposure to Particulate Matters and Multiple Sclerosis: A Systematic Review and Meta-Analysis

The association between air pollution and multiple sclerosis (MS) is not entirely clear. This meta-analysis was aimed at determining the correlation between particulate matter (PM)2.5, PM10, and MS incidence/relapse. The literature search was performed in EMBASE, Web of Science, PubMed, and the gray literature. Sixteen articles were retrieved, and ten articles were included and evaluated. Three measures of association were used for the meta-analysis: odds ratio (cross-sectional and case-control studies), incidence rate ratio, or hazard ratio (cohort studies). Meta-analysis of those 3 studies on PM2.5 indicated that exposure to PM2.5 was associated with MS relapse and incidence ([95% confidence interval; CI] 1.178 [1.102, 1.279]), <i>p</i> &#x3e; 0.05. Also, assessment of risk ratio for all studies showed a correlation between PMs (PM10 and PM2.5) and MS incidence and relapse ([95% CI] 1.28, [1.13–1.43]) <i>p</i> &#x3c; 0.05. Collectively, we found that PM exposure (PM10 and PM2.5) in MS patients associates with the occurrence and relapse of disease.

Weighted mixed-effects dose–response models for tables of correlated contrasts

Recognizing a dose–response pattern based on heterogeneous tables of contrasts is hard. Specification of a statistical model that can consider the possible dose–response data-generating mechanism, including its variation across studies, is crucial for statistical inference. The aim of this article is to increase the understanding of mixed-effects dose–response models suitable for tables of correlated estimates. One can use the command drmeta with additive (mean difference) and multiplicative (odds ratios, hazard ratios) measures of association. The postestimation command drmeta_graph greatly facilitates the visualization of predicted average and study-specific dose–response relationships. I illustrate applications of the drmeta command with regression splines in experimental and observational data based on nonlinear and random-effects data-generation mechanisms that can be encountered in health-related sciences.