A multi-lab test of the facial feedback hypothesis by the Many Smiles Collaboration.

Following theories of emotional embodiment, the facial feedback hypothesis suggests that individuals' subjective experiences of emotion are influenced by their facial expressions. However, evidence for this hypothesis has been mixed. We thus formed a global adversarial collaboration and carried out a preregistered, multicentre study designed to specify and test the conditions that should most reliably produce facial feedback effects. Data from n = 3,878 participants spanning 19 countries indicated that a facial mimicry and voluntary facial action task could both amplify and initiate feelings of happiness. However, evidence of facial feedback effects was less conclusive when facial feedback was manipulated unobtrusively via a pen-in-mouth task.

A distribution-free procedure for testing versatile alternative in medical multisample comparison studies.

We propose a test for multisample comparison studies that can be applied without strict assumptions, especially when the underlying population distributions are far from normal. The new test can detect differences not only in location or scale but also in shape parameters among parent population distributions. We are motivated by numerous medical studies, where the variables are not normally distributed and may present in the various groups more complex differences than simple differences in a particular aspect of underlying distributions, such as location or scale. In these situations, traditional ANOVA and Kruskal-Wallis tests are unreliable since the underlying assumptions are not valid. The proposed procedure also allows the researcher to determine which aspects are more responsible for a significant result. This is an important practical advantage over procedures that test for general differences among the distribution functions but cannot identify which aspects lead to significant results. The asymptotic distribution of the test statistic is analyzed along with its small sample behavior against several competing tests. The practical advantages of the proposed procedure are illustrated with a multisample comparison study of a biomarker for liver damage in patients with hepatitis C.

Interpoint distance tests for high-dimensional comparison studies.

Modern data collection techniques allow to analyze a very large number of endpoints. In biomedical research, for example, expressions of thousands of genes are commonly measured only on a small number of subjects. In these situations, traditional methods for comparison studies are not applicable. Moreover, the assumption of normal distribution is often questionable for high-dimensional data, and some variables may be at the same time highly correlated with others. Hypothesis tests based on interpoint distances are very appealing for studies involving the comparison of means, because they do not assume data to come from normally distributed populations and comprise tests that are distribution free, unbiased, consistent, and computationally feasible, even if the number of endpoints is much larger than the number of subjects. New tests based on interpoint distances are proposed for multivariate studies involving simultaneous comparison of means and variability, or the whole distribution shapes. The tests are shown to perform well in terms of power, when the endpoints have complex dependence relations, such as in genomic and metabolomic studies. A practical application to a genetic cardiovascular case-control study is discussed.

Biostatistics for Parasitologists - A Primer to Quantitative Parasitology.

The aggregated distributions of host-parasite systems require several different infection parameters to characterize them. We advise readers how to choose infection indices with clear and distinct biological interpretations, and recommend statistical tests to compare them across samples. A user-friendly and free software is available online to overcome technical difficulties.

Tests for comparison of multiple endpoints with application to omics data.

In biomedical research, multiple endpoints are commonly analyzed in "omics" fields like genomics, proteomics and metabolomics. Traditional methods designed for low-dimensional data either perform poorly or are not applicable when analyzing high-dimensional data whose dimension is generally similar to, or even much larger than, the number of subjects. The complex biochemical interplay between hundreds (or thousands) of endpoints is reflected by complex dependence relations. The aim of the paper is to propose tests that are very suitable for analyzing omics data because they do not require the normality assumption, are powerful also for small sample sizes, in the presence of complex dependence relations among endpoints, and when the number of endpoints is much larger than the number of subjects. Unbiasedness and consistency of the tests are proved and their size and power are assessed numerically. It is shown that the proposed approach based on the nonparametric combination of dependent interpoint distance tests is very effective. Applications to genomics and metabolomics are discussed.

Bias-corrected estimation of the Rudas-Clogg-Lindsay mixture index of fit.

Rudas, Clogg, and Lindsay (1994, J. R Stat Soc. Ser. B, 56, 623) introduced the so-called mixture index of fit, also known as pi-star (π*), for quantifying the goodness of fit of a model. It is the lowest proportion of 'contamination' which, if removed from the population or from the sample, makes the fit of the model perfect. The mixture index of fit has been widely used in psychometric studies. We show that the asymptotic confidence limits proposed by Rudas et al. (1994, J. R Stat Soc. Ser. B, 56, 623) as well as the jackknife confidence interval by Dayton (, Br. J. Math. Stat. Psychol., 56, 1) perform poorly, and propose a new bias-corrected point estimate, a bootstrap test and confidence limits for pi-star. The proposed confidence limits have coverage probability much closer to the nominal level than the other methods do. We illustrate the usefulness of the proposed method in practice by presenting some practical applications to log-linear models for contingency tables.

Multivariate tests based on interpoint distances with application to magnetic resonance imaging.

The multivariate location problem is addressed. The most familiar method to address the problem is the Hotelling test. When the hypothesis of normal distributions holds, the Hotelling test is optimal. Unfortunately, in practice the distributions underlying the samples are generally unknown and without assuming normality the finite sample unbiasedness of the Hotelling test is not guaranteed. Moreover, high-dimensional data are increasingly encountered when analyzing medical and biological problems, and in these situations the Hotelling test performs poorly or cannot be computed. A test that is unbiased for non-normal data, for small sample sizes as well as for two-sided alternatives and that can be computed for high-dimensional data has been recently proposed and is based on the ranks of the interpoint Euclidean distances between observations. Five modifications of this test are proposed and compared to the original test and the Hotelling test. Unbiasedness and consistency of the tests are proven and the problem of power computation is addressed. It is shown that two of the modified interpoint distance-based tests are always more powerful than the original test. Particularly, the modified test based on the Tippett criterium is suggested when the assumption of normality is not tenable and/or in case of high-dimensional data with complex dependence structure which are typical in molecular biology and medical imaging. A practical application to a case-control study where functional magnetic resonance imaging is used is discussed.

Does bad inference drive out good?

The (mis)use of statistics in practice is widely debated, and a field where the debate is particularly active is medicine. Many scholars emphasize that a large proportion of published medical research contains statistical errors. It has been noted that top class journals like Nature Medicine and The New England Journal of Medicine publish a considerable proportion of papers that contain statistical errors and poorly document the application of statistical methods. This paper joins the debate on the (mis)use of statistics in the medical literature. Even though the validation process of a statistical result may be quite elusive, a careful assessment of underlying assumptions is central in medicine as well as in other fields where a statistical method is applied. Unfortunately, a careful assessment of underlying assumptions is missing in many papers, including those published in top class journals. In this paper, it is shown that nonparametric methods are good alternatives to parametric methods when the assumptions for the latter ones are not satisfied. A key point to solve the problem of the misuse of statistics in the medical literature is that all journals have their own statisticians to review the statistical method/analysis section in each submitted paper.

Multivariate multidistance tests for high-dimensional low sample size case-control studies.

A class of multivariate tests for case-control studies with high-dimensional low sample size data and with complex dependence structure, which are common in medical imaging and molecular biology, is proposed. The tests can be applied when the number of variables is much larger than the number of subjects and when the underlying population distributions are heavy-tailed or skewed. As a motivating application, we consider a case-control study where phase-contrast cinematic cardiovascular magnetic resonance imaging has been used to compare many cardiovascular characteristics of young healthy smokers and young healthy non-smokers. The tests are based on the combination of tests on interpoint distances. It is theoretically proved that the tests are exact, unbiased and consistent. It is shown that the tests are very powerful under normal, heavy-tailed and skewed distributions. The tests can also be applied to case-control studies with high-dimensional low sample size data from other medical imaging techniques (like computed tomography or X-ray radiography), chemometrics and microarray data (proteomics and transcriptomics).