Group items tagged

This is an on-demand webinar. "Statistical errors are all too common in medical literature, and contribute to the reproducibility crisis currently plaguing science. Fortunately, you don't need a degree in statistics to catch these errors. While some errors are impossible to spot without access to the underlying dataset, many are detectable just by reviewing the information available in the paper. In many cases nothing more than common sense and simple arithmetic is required. In addition, there is an ever-increasing number of free, easy-to-use online statistical tools that facilitate error detection."

"I strive to advance the science of individual well-being by bridging and communicating statistical concepts with precision and empathy. I'm a data science statistician with 18+ years of experience. I build study design and analysis methods for individual-focused digital health using an n-of-1 / single-case causal inference framework."

"Objective: This randomized single-blind controlled trial tested the hypothesis that a prototype digital therapeutic developed to provide goal-based counseling with personalized passive and active game-based sound therapy would provide superior tinnitus outcomes, and similar usability, to a popular passive sound therapy app over a 12 week trial period. Methods: The digital therapeutic consisted of an app for iPhone or Android smartphone, Bluetooth bone conduction headphones, neck pillow speaker, and a cloud-based clinician dashboard to enable messaging and app personalization. The control app was a popular self-help passive sound therapy app called White Noise Lite (WN). The primary outcome measure was clinically meaningful change in Tinnitus Functional Index (TFI) between baseline and 12 weeks of therapy. Secondary tinnitus measures were the TFI total score and subscales across sessions, rating scales and the Client Oriented Scale of Improvement in Tinnitus (COSIT). Usability of the US and WN interventions were assessed using the System Usability Scale (SUS) and the mHealth App Usability Questionnaire (MAUQ). Ninety-eight participants who were smartphone app users and had chronic moderate-severe tinnitus (>6 months, TFI score > 40) were enrolled and were randomly allocated to one of the intervention groups. Thirty-one participants in the USL group and 30 in the WN group completed 12 weeks of trial. Results: Mean changes in TFI for the USL group at 6 (16.36, SD 17.96) and 12 weeks (17.83 points, SD 19.87) were clinically meaningful (>13 points reduction), the mean change in WN scores were not clinically meaningful (6 weeks 10.77, SD 18.53; 12 weeks 10.12 points, SD 21.36). A statistically higher proportion of USL participants achieved meaningful TFI change at 6 weeks (55%) and 12 weeks (65%) than the WN group at 6 weeks (33%) and 12 weeks (43%). Mean TFI, rating and COSIT scores favored the US group but were not statistically different from WN. Usability measures

"N-of-1 or single subject clinical trials consider an individual patient as the sole unit of observation in a study investigating the efficacy or side-effect profiles of different interventions. The ultimate goal of an n-of-1 trial is to determine the optimal or best intervention for an individual patient using objective data-driven criteria. Such trials can leverage study design and statistical techniques associated with standard population-based clinical trials, including randomization, washout and crossover periods, as well as placebo controls. Despite their obvious appeal and wide use in educational settings, n-of-1 trials have been used sparingly in medical and general clinical settings. We briefly review the history, motivation and design of n-of-1 trials and emphasize the great utility of modern wireless medical monitoring devices in their execution. We ultimately argue that n-of-1 trials demand serious attention among the health research and clinical care communities given the contemporary focus on individualized medicine. Keywords: clinical equipoise, early-phase trials, individualized medicine, n-of-1, remote phenotyping, single patient trial, treatment repositioning, wireless health"

Rosling and Gapminder creating stunning dynamic data visualizations of WHO (World Heath Organization) statistics. They are dedicated to proving with data the FACTS about our world. This is a way to a global view point that will help us all see that life on our planet is far better than it has ever been. Ready to use data to challenge your assumptions and shed your misconceptions? Dive in!

"You've never seen data presented like this. With the drama and urgency of a sportscaster, statistics guru Hans Rosling debunks myths about the so-called "developing world." Closing in on 13 million views -- 7 billion to go. This talk was presented at an official TED conference, and was featured by our editors on the home page."

Background With the rise of precision medicine efforts worldwide, our study objective was to describe and map the emerging precision medicine landscape. A Google search was conducted between June 19, 2017 to July 20, 2017 to examine how "precision medicine" and its analogous terminology were used to describe precision medicine efforts. Resulting web-pages were reviewed for geographic location, data type(s), program aim(s), sample size, duration, and the key search terms used and recorded in a database. Descriptive statistics were applied to quantify terminology used to describe specific precision medicine efforts. Qualitative data were analyzed for content and patterns.

Recommended by Trish Makowiak: How can you tell if the bold headlines seen on social media are truly touting the next big thing or if the article isn't worth the paper it's printed on? Understanding Medical Studies, will provide you with the tools and skills you need to critically interpret medical studies, and determine for yourself the difference between good and bad science. The course covers study-design, research methods, and statistical interpretation. It also delves into the dark side of medical research by covering fraud, biases, and common misinterpretations of data. Each lesson will highlight case-studies from real-world journal articles. By the end of this course, you'll have the tools you need to determine the trustworthiness of the scientific information you're reading and, of course, whether or not your Facebook friend is wrong. This course was made possible in part by the George M. O'Brien Kidney Center at Yale.

"Given the startling statistics of the opioid epidemic, it's no surprise that many are searching for safer ways to manage their pain. Whether they want to avoid the risks associated with conventional medications or feel they've exhausted other options, more and more people are turning to cannabidiol (CBD) for pain. So, what exactly is CBD? Does it really relieve pain? What does science say? Let's look at some CBD facts and examine the evidence around using CBD for pain."

"The coronavirus is killing Hispanic, Black and American Indian children at much higher numbers than their White peers, according to federal statistics released Tuesday."

"Late last week, Carnegie Mellon University (CMU) and Lumen Learning jointly issued a press release announcing their collaboration on an effort to integrate the Lumen-developed RISE analytical framework for curricular materials improvement analysis into the toolkit that Carnegie Mellon announced it will be contributing via open licenses (and unveiling at the Empirical Educator Project (EEP) summit that they are hosting in May)."