Lava is All You Need: R Package Reduces Bias for Correlations among Censored Variable

Data censoring occurs when researchers have only partial information about the value of a variable. For example, one study investigated depression among participants taking psilocybin (magic mushrooms). If participants took extra psilocybin outside of the study context, then the dosage is known to be at least as much as a certain value, but it might have been higher. Left censoring occurs when the left-hand side of a distribution is obscured by censoring; right censoring when the right-hand side is obscured. The R package lava can estimate the correlation that would have been obtained between the uncensored variables when provided with the data from the censored variables. We conducted a Monte Carlo study to evaluate the extent to which lava estimates are biased for data sets of 500 cases with various correlations (-.95, -.50, -.05, .25, .50, and .95) and various degrees of left censoring (10% on both variables, 50% on both, 20% on one and 80% on the other, and 95% on both). When there was low to moderate censoring, lava estimates were unbiased. However, when there was 95% censoring on both variables, lava estimates were biased. When the correlation was -.05 or -.50, bias was large and negative (-.24 or -.35, respectively). For other correlations, bias was typically moderate (e.g., -.02 to .06). If researchers are interested in negative correlations between variables that may be left censored, we recommend they minimize censoring to avoid biased estimates.https://digitalscholarship.unlv.edu/durep_podium/1005/thumbnail.jp

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Jamovi Assignments for Introductory Statistics: Instructor Site

Overview: This site contains a series of 10 free Jamovi assignments, which teach students how to complete the analyses covered in introductory statistics courses. The assignments, data, scoring keys, and instructions are given here. The assignments and data by themselves are given on the student site. These 10 Jamovi assignments teach students how to complete the analyses covered in introductory statistics courses. In these assignments, students implement the ideas they have seen in videos or textbooks, from the basics of opening data files to the complexities of creating a professional conference poster. These assignments use real data from 679 participants, allowing detailed frequency tables and scatterplots, filtering, and subgroup analyses. At the beginning of each assignment, students are given links to short videos (https://datalab.cc/jamovi/) and written descriptions (https://www.learnstatswithjamovi.com/). Therefore, no prior knowledge of Jamovi is required. Moreover, each assignment is designed to stand alone, so they may be used in any order. The order we use for our course is as follows: (1) Introduction to Jamovi (2) Graphs in Jamovi (3) Descriptive Statistics in Jamovi (4) Subgroups in Jamovi (5) Recoding Data in Jamovi (6) Single Sample t-test in Jamovi (7) Independent Samples in Jamovi and Writing a Research Report (8) Dependent Samples in Jamovi (9) Power in Jamovi (uses jpower module) (10) Correlation in Jamovi and Conference Poster Most assignments take 30-60 minutes to complete, including the time to watch the videos or to read the written explanations. However, Independent Samples in Jamovi requires students to summarize their findings in a research paper and takes 1 to 2 hours. Additionally, Correlation in Jamovi and Conference Poster requires students to create a research poster on one of three topics and takes 2 to 3 hours. Each assignment comes with a key to score student submissions. These keys have been used by both graduate and undergraduate teaching assistants. Because the keys are clear and concise, they can be used to provide specific feedback to students, which increases student learning and minimizes student questions about grades. Each assignment also has an accompanying “Instructions for Instructors” document, which explains how we have successfully used these assignments and provides tips for ease of use. The assignments, data, scoring keys, and instructions are given on the Open Science Framework: This is the Instructor site, which contains instructions for instructors, scoring keys, and links to assignments and data. The student site contains just the assignments and data

Jamovi for Intro Stats

This site contains a series of 10 free Jamovi assignments, which will teach you how to complete the analyses covered in introductory statistics courses. In these assignments, you will implement the ideas you have seen in videos or textbooks, from the basics of opening data files to the complexities of creating a professional conference poster. At the beginning of each assignment, you will be given links to short videos and written descriptions. Therefore, no prior knowledge of Jamovi is required. Then these assignments provide step-by-step instructions to guide you through the analyses, using real data that were collected at the University of Nevada, Las Vegas. After completing these assignments, you will be better prepared for your later courses, for the rigors of the workplace, and for graduate-level research