Bayesian inference in JASP: a guide for students

This is a collection of standalone handouts covering the most common Bayesian statistical analyses available in JASP for students studying Biological Sciences. Datasets used in this document are available for download from https://osf.io/8qtu2

Using the weighted Kendall Distance to analyze rank data in psychology

Although the Kendall distance is a standard metric in computer science, it is less widely used in psychology. We demonstrate the usefulness of the Kendall distance for analyzing psychological data that take the form of ranks, lists, or orders of items. We focus on weighted extensions of the metric that allow for heterogeneity of item importance, item position, and item similarity, as well showing how the metric can accommodate missingness in the form of top-k lists. To demonstrate how the Kendall distance can help address research questions in psychology, we present four applications to previous data. These applications involve the recall of events on September 11, people's preference rankings for the months of the year, people's free recall of animal names in a clinical setting, and expert predictions involving American football outcomes

A Bayesian perspective on Biogen's aducanumab trial

This perspective is a companion to a recent editorial on the use of Bayesian analysis in clinical research. We aim to introduce and highlight the relevance and advantages that Bayesian inference offers to clinical trials using the data on the amyloid antibody aducanumab presented at a Food and Drug Administration hearing in November 2020 as an applied example. We apply Bayesian analysis of model plausibility and effect sizes based on simulated data of the two phase 3 trials of aducanumab in prodromal and mild dementia stages of Alzheimer's disease (AD). Bayesian analysis can quantify evidence in favor of, or against, the presence of an effect (i.e., provide evidence of absence), as well as assess the strength of the effect. This is in contrast to the binary conclusions provided by frequentist tests

The association of Epstein-Barr virus infection with CXCR3+ B-cell development in multiple sclerosis: impact of immunotherapies

Epstein–Barr virus (EBV) infection of B cells is associated with increased multiple sclerosis (MS) susceptibility. Recently, we found that CXCR3-expressing B cells preferentially infiltrate the CNS of MS patients. In chronic virus-infected mice, these types of B cells are sustained and show increased antiviral responsiveness. How EBV persistence in B cells influences their development remains unclear. First, we analyzed ex vivo B-cell subsets from MS patients who received autologous bone marrow transplantation (n = 9), which is often accompanied by EBV reactivation. The frequencies of nonclass-switched and class-switched memory B cells were reduced at 3–7 months, while only class-switched B cells returned back to baseline at 24–36 months posttransplantation. At these time points, EBV DNA load positively correlated to the frequency of CXCR3+

Multiple perspectives on inference for two simple statistical scenarios

When data analysts operate within different statistical frameworks (e.g., frequentist versus Bayesian, emphasis on estimation versus emphasis on testing), how does this impact the qualitative conclusions that are drawn for real data? To study this question empirically we selected from the literature two simple scenarios—involving a comparison of two proportions and a Pearson correlation—and asked four teams of statisticians to provide a concise analysis and a qualitative interpretation of the outcome. The results showed considerable overall agreement; nevertheless, this agreement did not appear to diminish the intensity of the subsequent debate over which statistical framework is more appropriate to address the questions at hand

Consensus-based guidance for conducting and reporting multi-analyst studies

International audienceAny large dataset can be analyzed in a number of ways, and it is possible that the use of different analysis strategies will lead to different results and conclusions. One way to assess whether the results obtained depend on the analysis strategy chosen is to employ multiple analysts and leave each of them free to follow their own approach. Here, we present consensus-based guidance for conducting and reporting such multi-analyst studies, and we discuss how broader adoption of the multi-analyst approach has the potential to strengthen the robustness of results and conclusions obtained from analyses of datasets in basic and applied research

The JASP guidelines for conducting and reporting a Bayesian analysis

Despite the increasing popularity of Bayesian inference in empirical research, few practical guidelines provide detailed recommendations for how to apply Bayesian procedures and interpret the results. Here we offer specific guidelines for four different stages of Bayesian statistical reasoning in a research setting: planning the analysis, executing the analysis, interpreting the results, and reporting the results. The guidelines for each stage are illustrated with a running example. Although the guidelines are geared towards analyses performed with the open-source statistical software JASP, most guidelines extend to Bayesian inference in general

Bayes factors for mixed models: A discussion

van Doorn et al. (2021) outlined various questions that arise when conducting Bayesian model comparison for mixed effects models. Seven response articles offered their own perspective on the preferred setup for mixed model comparison, on the most appropriate specification of prior distributions, and on the desirability of default recommendations. This article presents a round-table discussion that aims to clarify outstanding issues, explore common ground, and outline practical considerations for any researcher wishing to conduct a Bayesian mixed effects model comparison