Podcasts about missing data

Dealing with Missing Data Abstract Philip and Fred discuss a few challenges and approaches to deal with missing data. Key Points Join Philip and Fred as they discuss what can be done when a dataset has missing data. Topics include: Data collection and storage issues Dealing with data points within the overall dataset Dealing with […] The post SOR 987 Dealing with Missing Data appeared first on Accendo Reliability.

The use of stats and throwing around numbers in conversation is incredibly common, yet statistics itself is poorly understood. Dr. Liza Bolton from  @universityofauckland discusses the dark art that is statistics. Using examples, she takes us through some misconceptions and dispels the notion that numbers don't lie. We cover how to identify the best ice cream store, how to not be fooled when stats are mischaracterized in media and politics, why we worship the nat 20 dice roll and is the 27 club for musicians a real thing?Dr. Bolton's website: https://www.lizabolton.com/Missing data in police warrant information: https://www.stuff.co.nz/pou-tiaki/300938784/data-shows-ongoing-racial-bias-in-police-warrantless-searchesGang numbers: https://thespinoff.co.nz/politics/23-04-2024/have-gang-numbers-really-skyrocketed-in-recent-yearsSupport us and reach out!https://smoothbrainsociety.comInstagram: @thesmoothbrainsocietyTikTok: @thesmoothbrainsocietyTwitter/X: @SmoothBrainSocFacebook: @thesmoothbrainsocietyMerch and all other links: Linktreeemail: thesmoothbrainsociety@gmail.com

Missing data in aging studies, especially in the assessment of gait speed (the time it takes individuals to cover a set distance), presents a significant challenge. The elderly are more prone to health and functional issues, which often interfere with data collection efforts. Given that gait speed is a key indicator of functional status and overall health in older individuals, ensuring its availability and accurate measurement is essential for the integrity of aging research. In a new study, researchers Robert Thiesmeier, Ahmad Abbadi, Debora Rizzuto, Amaia Calderón-Larrañaga, Scott M. Hofer, and Nicola Orsini from Karolinska Institutet, Stockholm University, Stockholm Gerontology Research Center, and Oregon Health and Science University address the systematic challenge of missing gait speed data in aging research and explore the application of multiple imputation (MI), a statistical technique that has emerged as a constructive approach to handle such gaps in data. The team critically examined the implementation strategies, methodologies, and the impact that these missing variables could have on the outcomes of aging studies, thereby offering a framework to manage and interpret incomplete datasets in aging research. On February 14, 2024, their research paper was published in Aging's Volume 16, Issue 4, entitled, “Multiple imputation of systematically missing data on gait speed in the Swedish National Study on Aging and Care.” “[...] this study aims to investigate and assess the performance of different MI strategies specifically targeting the systematically missing discrete variable of gait speed in the SNAC [Swedish National Study on Aging and Care] IPDMA [individual participant data meta-analyses] with only four large cohort studies.” Full blog - https://aging-us.org/2024/03/overcoming-missing-data-in-the-swedish-national-study-on-aging/ Paper DOI - https://doi.org/10.18632/aging.205552 Corresponding authors - Robert Thiesmeier - robert.thiesmeier@ki.se Sign up for free Altmetric alerts about this article - https://aging.altmetric.com/details/email_updates?id=10.18632%2Faging.205552 Subscribe for free publication alerts from Aging - https://www.aging-us.com/subscribe-to-toc-alerts Keywords - aging, simulation, systematically missing values, individual participant data, meta-analysis, gait speed About Aging-US Launched in 2009, Aging-US publishes papers of general interest and biological significance in all fields of aging research and age-related diseases, including cancer—and now, with a special focus on COVID-19 vulnerability as an age-dependent syndrome. Topics in Aging-US go beyond traditional gerontology, including, but not limited to, cellular and molecular biology, human age-related diseases, pathology in model organisms, signal transduction pathways (e.g., p53, sirtuins, and PI-3K/AKT/mTOR, among others), and approaches to modulating these signaling pathways. Please visit our website at https://www.Aging-US.com​​ and connect with us: Facebook - https://www.facebook.com/AgingUS/ X - https://twitter.com/AgingJrnl Instagram - https://www.instagram.com/agingjrnl/ YouTube - https://www.youtube.com/@AgingJournal LinkedIn - https://www.linkedin.com/company/aging/ Pinterest - https://www.pinterest.com/AgingUS/ Spotify - https://open.spotify.com/show/1X4HQQgegjReaf6Mozn6Mc Media Contact 18009220957 MEDIA@IMPACTJOURNALS.COM

In this episode, Dr. Osterholm and Chris Dall discuss...

On December 7, 1941, Japan attacked Pearl Harbor, drawing America into World War II. Admiral Husband Kimmel, a decorated veteran and commander of the US naval fleet in Hawaii, was completely caught off guard. But it didn't have to be a surprise. How did the US forces at Pearl Harbor miss the critical warning signs? Mismanagement of important intelligence data played a significant role.Companies run on data. It's the backbone that allows them to understand their customers, make informed decisions, and see the big picture. But what happens when you don't know what data you have, where it is, or how to access it?Steve Twomey, author of Countdown to Pearl Harbor: The Twelve Days to the Attack, explains why the US forces at Pearl Harbor were unprepared for the attack, and Scott Bridgen, former Global GTM & Strategy Lead-GRC & Integrated Risk at OneTrust, explains why

Saloni Dattani is a Researcher at Our World in Data, and a founder & editor at the online magazine Works in Progress. She holds a PhD in psychiatric genetics from King's College London. You can see more links and a full transcript at hearthisidea.com/episodes/dattani. In this episode we talk about: The history of malaria and attempts to eradicate it The role of DDT and insecticide spraying campaigns — and why they were scaled down Why we didn't get a malaria vaccine sooner What comes after vaccine discovery — rolling out the RTS,S vaccine New funding models to accelerate similar life-saving research, like vaccines for TB and HIV Why so much global health data is missing, and why that matters How the ‘million deaths study' revealed that about 50,000 deaths per year from snakebites in India went uncounted by health agencies You can get in touch through our website or on Twitter. Consider leaving us an honest review wherever you're listening to this — it's the best free way to support the show. Thanks for listening!

On today's episode, financial journalist Govindraj Ethiraj talks to Amit Khera, Senior Partner at Consulting firm Mckinsey & co as well as Amit Prothi, Director General of The Coalition for Disaster Resilient Infrastructure (CDRI).SHOW NOTES[00:50] Empowerment, the new way of measuring economic security at $12 per person with Amit Khera[08:14] China's Missing Data & India's Too[12:31] Govt suspects exporters of selling non basmati rice as basmati rice, clamps down[16:57] A new global disaster resilience coalition sets up shop in India with Amit ProthiFor more of our coverage check out thecore.inSubscribe to our NewsletterFollow us on:Twitter | Instagram | Facebook | Linkedin | Youtube | Telegram

It's no secret that communicable diseases can have dramatically different impacts on different groups; elderly people, for example, saw the worst effects of COVID, with a death rate far beyond California's average. Black Californians also had higher mortality rates from the disease.  What of California's LGBTQ community? The truth is, we don't know. SB 932, a 2020 bill inspired by concerns about potential disparate impacts of COVID on LGBTQ people, directed the California Department of Public Health to collect sexual orientation and gender identity (SOGI) data. A new report released by the State Auditor last month says that CDPH has fallen short and recommends that state law be amended to require more comprehensive practices around SOGI data collection.Sen. Scott Wiener, the author of SB 932, joined us to talk about why this data is so important, what's missing, and how California is bucking national trends on LGBTQ healthcare.And, as always, we tell you who had the Worst Week in California Politics. (Hint, it wasn't Jon Fleischman).Episode Notes2:04 The audit5:24 What's missing in the report7:11 Why hasn't this data been collected before?9:47 Intersection with Federal Law10:40 Why the resistance from CDPH?11:52 Compare this to other states13:56 Legislation?15:53 An aside about Housing17:55 WWCAWant to support the Capitol Weekly Podcast? Make your tax deductible donation here: capitolweekly.net/donations/Capitol Weekly Podcast theme is "Pickin' My Way" by Eddie Lang "#WorstWeekCA" Beat provided by freebeats.io

Optimal frequency-domain analysis for spacecraft time series: Introducing the missing-data multitaper power spectrum estimator by Sarah E. Dodson-Robinson et al. on Wednesday 30 November While the Lomb-Scargle periodogram is foundational to astronomy, it has a significant shortcoming: its variance does not decrease as more data are acquired. Statisticians have a 60-year history of developing variance-suppressing power spectrum estimators, but most are not used in astronomy because they are formulated for time series with uniform observing cadence and without seasonal or daily gaps. Here we demonstrate how to apply the missing-data multitaper power spectrum estimator to spacecraft data with uniform time intervals between observations but missing data during thruster fires or momentum dumps. The F-test for harmonic components may be applied to multitaper power spectrum estimates to identify statistically significant oscillations that would not rise above a white noise-based false alarm level. Multitapering improves the dynamic range of the power spectrum estimate and suppresses spectral window artifacts. We demonstrate multitapering on simultaneous measurements of the interplanetary magnetic field and the solar 10.7-cm radio flux, finding high coherence at frequencies associated with the sun's activity cycle and quasibiennial oscillations. Next we show that the multitaper--F-test combination applied to Kepler observations of KIC 6102338 detects differential rotation without requiring iterative sinusoid fitting and subtraction. Significant signals reside at harmonics of both rotation frequencies and suggest an antisolar rotation profile. Finally, we demonstrate the missing-data multitaper version of complex demodulation, which extracts the low-frequency envelope from a modulated signal. We argue that multitaper power spectrum estimators should be used for all time series with regular observing cadence. arXiv: http://arxiv.org/abs/http://arxiv.org/abs/2211.16549v1

In this week's episode Greg and Patrick get to explore modern methods for missing data analysis while belaboring quotes from Top Gun with their guest Craig Enders from the University of California at Los Angeles. Craig looks back over the past 20 years of developments in missing data analysis to discuss what has worked, what hasn't worked, and what new methods are available now that we didn't have back then. Along the way they also discuss Sean, Not Sean, going to the movies, grumpy old man mode, wiener boy, grave digger, Venice beach zoom backgrounds, Lie Awake, hung over GREs, Greg's grandmother, shiny objects, Motorola flip phones, Ask Jeeves, talking narwhals, mimeographs, unscrewing yourself, and who can be whose wingman.Stay in contact with Quantitude! Twitter: @quantitudepod Web page: quantitudepod.org Merch: redbubble.com

JAMA Statistical Editor Roger J. Lewis, MD, PhD, discusses Worst-Rank Score Methods—A Nonparametric Approach to Informatively Missing Data with John M. Lachin, ScD. Related Content: Worst-Rank Score Methods—A Nonparametric Approach to Informatively Missing Data

The federal budget is out next week. What should we expect the Treasurer to announce on Tuesday night? Plus: fresh employment numbers, insights from the Director Sentiment Index and China's delayed GDP data.  

Where has Google's data gone? How to "align with consensus" according to the QRG. What Google says you should do with old events (probably delete them), and our thoughts about robots.txt.Sources Cited:https://pca.st/episode/2cc8cdde-b00a-4da7-8c9f-c4b79845ede0https://searchengineland.com/google-consensus-qrg-seo-387142https://www.seroundtable.com/google-seo-old-event-pages-33847.htmlhttps://developers.google.com/search/docs/advanced/robots/robots_txt

This week we have a great show for you! Join the Sell More Books Show Afterparty group on Facebook and be sure to leave us a review on Apple Podcast. Author Ad School is now open through July 25th and Claire's Kickstarter ends July 21st at midnight, you can visit the links in the show notes! Top Tips of the week include how to have a guilt free summer even if you don't write, how to find the best keywords for your books, and how to give your backlist a kick in the pants! The 5 News stories that matter most to indies this week include how to finish your first book, how BookTok and other influencers can help sell books, what author is making his living by doing less, what new reality show is following writers writing their books, and how ALLi is trying to track indie author sales and successes. Question of the week: If you had to pick one project to give your full attention to, which one would it be and why? Which projects would you let drop?

https://www.patreon.com/datameshradio (Data Mesh Radio Patreon) - get access to interviews well before they are released Episode list and links to all available episode transcripts (most interviews from #32 on) https://docs.google.com/spreadsheets/d/1ZmCIinVgIm0xjIVFpL9jMtCiOlBQ7LbvLmtmb0FKcQc/edit?usp=sharing (here) Provided as a free resource by DataStax https://www.datastax.com/products/datastax-astra?utm_source=DataMeshRadio (AstraDB) Data Mesh Radio is hosted by Scott Hirleman. If you want to connect with Scott, reach out to him at community at datameshlearning.com or on LinkedIn: https://www.linkedin.com/in/scotthirleman/ (https://www.linkedin.com/in/scotthirleman/) If you want to learn more and/or join the Data Mesh Learning Community, see here: https://datameshlearning.com/community/ (https://datameshlearning.com/community/) If you want to be a guest or give feedback (suggestions for topics, comments, etc.), please see https://docs.google.com/document/d/1WkXLhSH7mnbjfTChD0uuYeIF5Tj0UBLUP4Jvl20Ym10/edit?usp=sharing (here) All music used this episode was found on PixaBay and was created by (including slight edits by Scott Hirleman): https://pixabay.com/users/lesfm-22579021/ (Lesfm), https://pixabay.com/users/mondayhopes-22948862/?tab=audio (MondayHopes), https://pixabay.com/users/sergequadrado-24990007/ (SergeQuadrado), and/or https://pixabay.com/users/nevesf-5724572/ (nevesf) Data Mesh Radio is brought to you as a community resource by DataStax. Check out their high-scale, multi-region database offering (w/ lots of great APIs) and use code DAAP500 for a free $500 credit (apply under "add payment"): https://www.datastax.com/products/datastax-astra?utm_source=DataMeshRadio (AstraDB)

Jesper Dramsch is a scientist for machine learning at the European Centre for Medium-Range Weather forecasts. They have a phd in applied Machine Learning to Geoscience from Technical University of Denmark. They are a Kaggle Kernals Expert and TPU star, ranking at top 81/100k worldwide. We talked about weather forecasting, things they learned from Kaggle, how to deal with missing data and ourliers, deep learning, Keras vs Pytorch, XGBoost, their struggles as a phd student, working in the EU vs US. Follow @DalianaLiu for more updates on data science and this show. Resources shared by Jesper: The newsletter with missing data: https://buttondown.email/jesper/archive/towels-have-quite-a-dry-sense-of-humor/ The paper by Gael about missing data: https://academic.oup.com/gigascience/article/doi/10.1093/gigascience/giac013/6568998 The Huber Loss: https://en.wikipedia.org/wiki/Huber_loss Skill Scores: https://en.wikipedia.org/wiki/Forecast_skill Brier Skill in Weather: https://www.dwd.de/EN/ourservices/seasonals_forecasts/forecast_reliability.html CRPS Continuous Ranked Probability Score https://datascience.stackexchange.com/questions/63919/what-is-continuous-ranked-probability-score-crps ConvNext, Convnets for the 2020s: https://arxiv.org/abs/2201.03545 Transformers for ensemble forecasts: https://arxiv.org/abs/2106.13924 Books I recommend: https://www.amazon.com/shop/jesperdramsch/list/2DYS5KVR5TX0E Blog posts I wrote about these books: https://dramsch.net/tags/books/ Short I made about Test-Time Augmentation https://www.youtube.com/shorts/w4sAh9lKyls Their links: https://dramsch.net/links Their open PhD thesis: https://dramsch.net/phd Newsletter: https://dramsch.net/newsletter Twitter: https://dramsch.net/twitter Youtube: https://dramsch.net/youtube Linkedin: https://dramsch.net/linkedin Kaggle: https://dramsch.net/

Virus Outbreak North Korea Intro and Voicer

Avoid missing data, but, use Latin Squares Music ‘The Ukrainian National Anthem' by the Metropolitan Opera

A new model produces 3D trees that are close to reality using predicted data. You can read this post online at https://www.botany.one/2022/01/deep-learning-techniques-used-to-predict-missing-data/ You can read the original research at https://doi.org/10.1093/insilicoplants/diab036

If you've ever had to deal with missing data, you've probably wished you could avoid it completely in the future. So why on earth would anyone design a study where data are missing on purpose? When you set up a project, there are actually several advantages to selecting a subset of people to skip assessments, items, or waves on purpose. Jess and Sara describe those designs here, and just what the advantages are, as well as fun future directions for one specific subtype of these designs: The two-method measurement planned missingness design. Even though we start with a basic missing data overview, and you might think that missing data is scary or boring, we SWEAR this one is interesting. Links mentioned in this episode: The preprint on how to handle missing data decision tree: https://psyarxiv.com/mdw5r/ The paper that introduces the longitudinal version of the two method measurement design: https://doi.org/10.1177/0165025414542711 Menglin Xu and Jess's paper in JREE: https://www.tandfonline.com/doi/pdf/10.1080/19345747.2021.1875528 Overview of different types of planning missing data designs in education research: https://doi.org/10.1080/00461520.2016.1208094 Connect with the podcast on twitter @within_between, or email us letters about developmental science at withinandbetweenpod@gmail.com. More episodes and podcast information at WithinandBetweenPod.com. Follow Dr. Hart on twitter @Saraannhart Follow Dr. Logan on twitter @Jarlogan. Our theme music was composed by Jason Flowers. Our logo was created by Nathan Archer. Recorded November 16, 2021.

We are just now starting to understand how geography has mattered in understanding COVID-19 mortality. On this episode, we talk about local patterns of COVID-19 deaths with two researchers from the University of North Carolina at Chapel Hill, Alexis Dennis and Jaclyn Karasik.

Depending on where you look, you can find a statistic that will tell you any number you want to see about how often students are bullied in school. Why is that? Why are bullying numbers all over the place? And how does it cause students to fall through the cracks? Host: Gabriella Paul. Reporter: McKenna Beery. Guests: Itea Aslanian, Deborah Temkin, Brooke Greier. 

Miriam deals with being stuck by a mysterious growth of mushrooms. Deborah and Nerea hash it out. Trigger Warning: Isolation, Hallucinations, Paranoia, Implied Suicidal Ideation, Swearing, Drowning, The sound of shattering glass, and Mold/Mushrooms. Miriam Clyde was played by Ell Gamester The Unknown Voice/id was played by Kate Butler (Check Out Witches of The City!) Nerea was played by Grace Gamester and Deborah was played by Lee Gorman Well! It's been about a year! Quarantine was rough, but I've got this episode done, and more are coming soon! Thank you so much for listening! and... Can you hear that?

Episode sponsored by Tidelift: https://tidelift.com/ (tidelift.com) We already mentioned multilevel regression and post-stratification (MRP, or Mister P) on this podcast, but we didn’t dedicate a full episode to explaining how it works, why it’s useful to deal with non-representative data, and what its limits are. Well, let’s do that now, shall we? To that end, I had the delight to talk with Lauren Kennedy! Lauren is a lecturer in Business Analytics at Monash University in Melbourne, Australia, where she develops new statistical methods to analyze social science data. Working mainly with R and Stan, Lauren studies non-representative data, multilevel modeling, post-stratification, causal inference, and, more generally, how to make inferences from the social sciences. Needless to say that I asked her everything I could about MRP, including how to choose priors, why her recent paper about structured priors can improve MRP, and when MRP is not useful. We also talked about missing data imputation, and how all these methods relate to causal inference in the social sciences. If you want a bit of background, Lauren did her Undergraduates in Psychological Sciences and Maths and Computer Sciences at Adelaide University, with Danielle Navarro and Andrew Perfors, and then did her PhD with the same advisors. She spent 3 years in NYC with Andrew Gelman’s Lab at Columbia University, and then moved back to Melbourne in 2020. Most importantly, Lauren is an adept of crochet — she’s already on her third blanket! Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ (https://bababrinkman.com/) ! Thank you to my Patrons for making this episode possible! Yusuke Saito, Avi Bryant, Ero Carrera, Brian Huey, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, Demetri Pananos, James Ahloy, Jon Berezowski, Robin Taylor, Thomas Wiecki, Chad Scherrer, Vincent Arel-Bundock, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, George Ho, Colin Carroll and Nathaniel Burbank. Visit https://www.patreon.com/learnbayesstats (https://www.patreon.com/learnbayesstats) to unlock exclusive Bayesian swag ;) Links from the show: Lauren's website: https://jazzystats.com/ (https://jazzystats.com/) Lauren on Twitter: https://twitter.com/jazzystats (https://twitter.com/jazzystats) Lauren on GitHub: https://github.com/lauken13 (https://github.com/lauken13) Improving multilevel regression and poststratification with structured priors: https://arxiv.org/abs/1908.06716 (https://arxiv.org/abs/1908.06716) Using model-based regression and poststratification to generalize findings beyond the observed sample: https://arxiv.org/abs/1906.11323 (https://arxiv.org/abs/1906.11323) Lauren's beginners Bayes workshop: https://github.com/lauken13/Beginners_Bayes_Workshop (https://github.com/lauken13/Beginners_Bayes_Workshop) MRP in RStanarm: https://github.com/lauken13/rstanarm/blob/master/vignettes/mrp.Rmd (https://github.com/lauken13/rstanarm/blob/master/vignettes/mrp.Rmd) Choosing your rstanarm prior with prior predictive checks: https://github.com/stan-dev/rstanarm/blob/vignette-prior-predictive/vignettes/prior-pred.Rmd (https://github.com/stan-dev/rstanarm/blob/vignette-prior-predictive/vignettes/prior-pred.Rmd) Mister P -- What’s its secret sauce?: https://statmodeling.stat.columbia.edu/2013/10/09/mister-p-whats-its-secret-sauce/ (https://statmodeling.stat.columbia.edu/2013/10/09/mister-p-whats-its-secret-sauce/) Bayesian Multilevel Estimation with Poststratification -- State-Level Estimates from National Polls: https://pdfs.semanticscholar.org/2008/bee9f8c2d7e41ac9c5c54489f41989a0d7ba.pdf... Support this podcast

Welcome back to the Hot Topics podcast with Neal Tucker.Finally some hope with an imminent COVID vaccine. But how good is it really and where is the data? Meanwhile general practice has been set up as the saviour of the nation but can we really vaccinate the entire population? The Committee of Horribly Absurd Orders thinks so or is it part of their long-game to say CHAO to GPs? And also some good news that we may not have to store this new vaccine at -70oC after all...For published research we look at SSRIs for acute COVID in the community, suspect rapid antibody tests and the harm from delayed cancer treatments.ReferencesNS Vaccine review and report on storageJAMA SSRI and COVID trialBMJ Accuracy of Rapid Antibody TestsBMJ Mortality due to cancer treatment delay

Supporting Links Join The Conversation! home Me on Twitch https://www.twitch.tv/canadapoli Me on Periscope https://www.pscp.tv/MarkParalovos/1vOGwkzNPdLGB Headlines and More! https://canadareport.co/ Discussion Based https://speakingmoistly.co/ Me on Twitter Tweets … Read more… The post 354 – Covid, Vaccine Interference and The Case of the Missing Data appeared first on CanadaPoli.

Oh we're back, baby!You'll be glad the hiatus is over as the Blackbox crew return to discuss the issues of interest from just this week in the Australian TV industry. A most excellent welcome also to Yvie and Robbo, filling in for Ben and a conspicuously absent Molk (what a slacker that guys is!).Each week the TV Blackbox team take you behind the scenes, wade through the PR spin and launch into the reality that is the Australian television business.2.58 - Ransomware attackers bring down the OzTam ratings system7.41 - The TV news wars take a new turn as everybody wins!10.41 - ABC News Channel celebrates a decade of 24/7 news17.01 - David Koch signs a new contract with Seven22.27 - Guy Sebastian's brother wins The Voice... but was it fair?29.10 - The Ben, Rob & Robbo Show celebrates 50 episodes30.53 - Hatches & Dispatches34.00 - The Big Issue50.30 - TV BlackvaultHuge thanks to David 'Robbo' Robinson and Yvie Jones for joining us this week!Follow the @TVBB_podcast crew:@rob_mcknight@shrimptank@viscountbrooky@BenjaminJNorris@SteveMolkFollow us on Twitter: https://twitter.com/tv_blackboxFind us on Facebook: https://facebook.com/TVBlackbox/Visit our website: https://tvblackbox.com.au Support this show http://supporter.acast.com/tv-blackbox. See acast.com/privacy for privacy and opt-out information.

Every Friday, BPR's Helen Chickering reviews the week's coronavirus news with NC Health News founding editor Rose Hoban. This week they discuss the latest COVID-19 metrics , the "have and have-not" impact on hospital reporting, possible mask mandate, missing racial data and the impact of the coronavirus pandemic on foster care HC : Let's start with the number of, so it looks like North Carolina will hit 50,000 new COVID-19 cases. sometime this weekend(on Saturday: 51, 389). There's a record number of people in the hospital. Before we hit the record button, you brought up hospital reporting. I look at the data every day. So first off, we have 871 folks who are currently hospitalized as of Friday, with 91% of hospitals reporting. Like any good reporter I've developed contacts over the years. They send me information - "here's a screenshot of our hospital's dashboard." And it's very interesting. You can see how many ICU cases a hospital has or how many COVID patients are on the floor and

This podcast presents a session on planning for and addressing missing data form the 2019 Mobile in Clinical Trials conference.  When a protocol dictates a set period of time for data collection, what happens when there's a gap in the data due to technical glitches or human error? What are the regulatory and operational implications? Discussion points include: How to prepare for missing dataHow do we handle missing data?How to rewrite protocols so that you avoid the trapForewarning and communicating with regulatorsRegulatory advice on how to address missing data Moderator: Jennifer Goldsack, MBA, Executive Director, Digital Medicine Society (DiMe) Panelists: Ariel Dowling, PhD, Associate Director of Digital Clinical Devices, Data Sciences Institute, Takeda Pharmaceuticals Marie McCarthy, MBA, Senior Director of Product Innovation, Information Technology, ICON plc Carrie Northcott, PhD, Project Team Lead, Early Clinical Development, Digital Medicine, Pfizer Shyamal Patel, PhD, Director of Data Science, Digital Medicine & Translational Imaging, Pfizer   The 2020 Mobile in Clinical Trials event will take place on September 21. Learn more here.

In today’s episode, Greg and Patrick start a conversation about how COVID-19 has had an instantaneous and lasting impact on research, what we can do about it right now, and what we can be thinking about for the future as we move forward, together. Along the way they also manage to mention... elevators of blood, Joe Exotic, wisdom of crowds, a fatted ox, skinned knees, intellectual judo, I meant to do that, truck drivin’ subtlety, Apollo 13, hitting a change-up, naive optimism, Thomas Kuhn, and losing your balance.

Greg and Patrick start with what seems like a reasonable metaphor linking musical compositions to planned missing data designs, and then they proceed to beat it to death. Then, just when you think they've buried it, they dig it back up and beat the carcass some more before eventually just running out of time. In addition to planned missing designs, they also mention the Kenny G paternity test, every trumpet player's sworn duty, tied 16th notes, Benny und die Jets, the Durham Bullhorns, wastebasket toilets, free steak knives, 81 minutes, doors and flashlights, Alyssa, Jay and Sean, and respecting the dart.

In Episode 16 of Quantitude, Patrick and Greg have more fun than is probably socially acceptable when talking about missing data. In addition to embracing the Zen-like paradox of "the presence of missing data," they also discuss West Point, "is" versus "are," middle school English teachers, relentless tenacity, talking narwhals, being completely pregnant, taking in shows at Cat's Cradle, circus tents, and books by Richard Bachman.

Missing data are common in randomized, controlled trials. Why does this happen? How do we interpret these missing data? In this episode of Curbside Consults, Dr. David Harrington, statistical editor at the NEJM, joins us for a discussion on missing data and how to interpret missing data in studies.

Don't have empty cells Music 'Stretch' by Dice

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The Las Vegas Massacre's Missing Data, The Future of Gun Sanctuaries, With the Kavanaugh Charade, Do Polls Now Replace the Constitution?, Feinstein: The Greater of Two Evils,  Lock N Load is Presented by;   AR500 Armor  …

The Las Vegas Massacre's Missing Data, The Future of Gun Sanctuaries, With the Kavanaugh Charade, Do Polls Now Replace the Constitution?, Feinstein: The Greater of Two Evils,  Lock N Load is Presented by;   AR500 Armor  …

Get equal numbers of subjects per group if you can.  Oh and don't have empty cells. Music 'Stretch' by Dice

[ Dinle ]Ruby Türkiye Buluşmaları23 Eylül Buluşması13 Ekim Ankara Buluşması18 Ekim İstanbul #3 BuluşmasıRuby Türkiye Youtube KanalıCould we drop Symbols from Ruby?Ruby Dev SummitModule Builder PatternHanami released v1.1.0.beta3Introducing to SurrealistAccess Ruby Hash Values with Fallbacks for Missing DataRuby Arrays in some wordsRuby 2.4 has optimized enumerable min max methodsWhat Did I Learn After Going Eighteen Months Without RailsRöportaj: Leyla KapiHurriyet Ruby Wrapper - Yiğit ÖzkavcıHurriyet Ruby CLI - Ender Ahmet Yurt

  Lorrie Carr joined ZappRx in April 2016 as Chief Commercial Officer. She has over 20 years' experience in the health care industry – primarily focused in the pharma/biotech and specialty pharmacy industries. Just prior to joining ZappRx, Ms. Carr was the Divisional VP of Enterprise Specialty Sales and Product Management for Walgreens Specialty Pharmacy. Ms. Carr transformed the strategy, operations and deployment of the over 300-person Specialty and Infusion sales teams resulting in consistent double-digit year over year growth for the multi-billion-dollar business. In addition, she created, built and successfully led the product management team which developed and executed on growth strategies for high priority specialty disease areas. Prior to Walgreens, Ms. Carr was the Head of U.S. / Global Market Access for Millennium Pharmaceuticals (now Takeda Oncology) where she built the first Reimbursement & Market Access team for the company, advised global R&D on access related trial design decisions, led the European patient access strategy, and played a material role in developing pricing, contracting, distribution and patient assistance program strategies for pipeline and marketed products. She started her pharmaceutical career at Schering-Plough (now Merck) where she held various roles in account management, and ultimately, became a senior leader of the US Market Access marketing group. Ms. Carr received her Bachelor's degree from Columbia College and holds her MBA from UMASS Isenberg School of Management. 00:00 Specialty Pharmaceutical Products. 02:30 Inefficiencies in the system. 04:00 The paper-fill process for Patient Prescription/Enrollment with Specialty Pharmacies. 06:30 Enrollment Forms for prescription drugs filled at Specialty Pharmacies. 10:20 The overwhelming cost and administrative overhead for Providers having to keep track of all of these forms. 13:00 How the process changes for Providers with ZappRx 19:00 Getting the drug out to the patient without conflict. 20:00 Double check for patients and Specialty Pharmacy. 22:00 Why ZappRx is collaborative, not competitive. 23:30 ZappRx's work on the Provider side. 24:25 Gathering Data at the front-end of the prescribing process. 25:15 ZappRx's long-term business model. 25:30 Missing Data in the healthcare and pharmacy markets. 27:45 Aggregating data and identifying trends. 28:30 ZappRx's five key focus Disease Areas. 30:00 You can learn more about ZappRx at www.zapprx.com.

Radiation spikes in Europe: Cause? Source? Coverup? Missing Data? Shaun McGee digs up the story. + SimplyInfo's Fukushima 6th Anniversary Report – Fukushima Facts w/Nancy Foust

Radiation spikes in Europe: Cause? Source? Coverup? Missing Data? Shaun McGee digs up the story. + SimplyInfo's Fukushima 6th Anniversary Report – Fukushima Facts w/Nancy Foust

3.27.2016 Missing Data.

In a Fintech Era when Big Data is all the rage Equity Crowdfunding rather looks like a very poor relation indeed. Less Small Data even and more No Data. As I have written about in The Strange Case of Missing Data on UK Equity Crowdfunding none of the platforms, the UK Crowdfunding Association or the FCA appear to be providing or asking for even the bare minimum, I am pleased to be joined in this curious vacuum by Rupert Taylor MD of AltFiData who first appeared on the show a year ago giving an overview of the whole alternative finance scene.  The AltFi Data team recently released a report City lawyers Nabarro entitled “Where are they now? A report into the status of companies that have raised finance using equity crowdfunding in the UK" For those of you who aren't aware of the depths of the challenge in this area the acting head of the FCA and chairman of the FCA were grilled recently by the UK government's Treasury Select Committee.  Perhaps the most shocking aspect of this was the lack of grip the FCA appeared to have on data on the sector – venturing that p2p losses were “around 1%” and that there was a lack of performance data around ECF.  However the FCA did mention that they insist that ECF platforms make it clear that the majority of startups fail. In this episode we discuss: - Rupert's charity Momentum which aims to give children from a state academy work experience which he sees as the essential tyranny which can hold those back whose parents aren't well-off/well-connected (especially as much experience/intern-ing can be unpaid :-() - the problems of the English language, despite which we decided to continue using it :-) - the necessity of asset classes demonstrating their performance. A comparison of #ecf with #p2p which is doing a far far better job of transparency around track-record - the huge difficulty of finding out even simple statistics like how many deals have been done and how many of those companies are alive today. Companies house Dickensian latency around data; social media; telephone calls and many other "old world" brute force techniques of discovering - the twin aims of the report being this basic "survival" data and the desire that the #ecf industry pick up the ball and run with it - in it's own interests as well as that of their investors (who they purport to serve) - the parallel between professionally-managed stock markets and #ecf 2015 - the media treatment (both old and new) to the report to spin it into saying what they wanted to in the first place - £140m raises to June '15; over 2015 £145m is expected to be raised - more than in its prior history; - the average age of ecf-raising companies being 3yrs which is not a startup - although it is early days, especially for companies that raised funds in 2014/2015, the data shows that 4 out of 5 companies that raised ecf are still going.  Contrast this with the oft-reported (but apparently groundless) stat that 9 out of 10 startups fail.  Stats based in research are along the lines that roughly 1 in 2 startups failed (RSA, Nesta, ONS) - the fact that an ecf-raising company is very different from a startup (which stats relate to)

Kevin Hall’s study “Calorie for Calorie, Dietary Fat Restriction Results in More Body Fat Loss than Carbohydrate Restriction in People with Obesity” was picked up by almost all the popular news outlets. The BBC, The Guardian, Time Magazine, The Washington Post and LA Times all weighed in. Stephen Guyenet and Bill Lagakos both wrote very interesting articles worthy of your attention. The examine.com and NHS commentary were also good. Dr. Tommy Wood described the study as “groundbreaking” and “the most well designed and best controlled ever”. From the title, the conclusion was clear, the low-fat diet resulted in more fat-loss than the low-carb diet. Unfortunately, and as always, it’s a bit more complicated than that. Professor Richard Feinman described the study as "a very distressing paper", not for its conclusion, but for the missing data. "Nobody loses an average amount of weight," said Feinman, "habeas corpus datorum" (show me the body of the data). During the interview, Dr. Feinman mentions QED Statistics, and the two studies “Carbohydrate restriction improves the features of Metabolic Syndrome. Metabolic Syndrome may be defined by the response to carbohydrate restriction” and “Dietary carbohydrate restriction as the first approach in diabetes management: Critical review and evidence base”. Richard David Feinman is Professor of Biochemistry at the State University of New York (SUNY) Downstate Medical Center in Brooklyn, New York. Dr. Feinman’s original area of research was in protein chemistry and enzyme mechanism, particularly in blood coagulation and related processes. Dr. Feinman has talked about flawed studies on my podcast once before. Dr. Tommy Wood is a qualified medical doctor, graduating from Oxford University in 2011. He has a previous Bachelor's degree in Natural Sciences and Biochemistry from Cambridge University. After working as a junior doctor in the UK for two years, Dr. Wood is now working towards a Ph.D. in neonatal brain metabolism at the University of Oslo, Norway. Tommy is a regular contributor to my podcast, and for that I am ever grateful. Addendum, 7 September, 2015 During the interview, Dr. Feinman said, "They've chased out everybody who has integrity". This statement was the result speaking of-the-cuff, and what he meant was "They’ve chased out everybody who can’t put up with the state of the field. The remaining ones may have great integrity but greater tolerance for the mess in nutrition".

This week Facebook privacy concerns, Chinese hackers, FireEye and visa create a pact, and more vulnerable routers.

Reviewing the methodology surrounding missing data in research and statistical analysis, clarifying why it can contribute to misleading results. Missing data is present in almost all research. However, it is also a well-recognised problem in the analysis and reporting of clinical research due to its potential to introduce bias into the results. Patient-reported outcomes measures, which are increasingly used in clinical research, can be particularly susceptible to missing data. This presentation will review the methodology surrounding missing data in research and statistical analysis, clarifying why it can contribute to misleading results. Guidance for the handling and reporting of missing data in clinical research will be presented, and compared to current practice, with a focus on randomised controlled trials.

Reviewing the methodology surrounding missing data in research and statistical analysis, clarifying why it can contribute to misleading results. Missing data is present in almost all research. However, it is also a well-recognised problem in the analysis and reporting of clinical research due to its potential to introduce bias into the results. Patient-reported outcomes measures, which are increasingly used in clinical research, can be particularly susceptible to missing data. This presentation will review the methodology surrounding missing data in research and statistical analysis, clarifying why it can contribute to misleading results. Guidance for the handling and reporting of missing data in clinical research will be presented, and compared to current practice, with a focus on randomised controlled trials.

Thu, 1 Jan 2015 12:00:00 +0100 https://epub.ub.uni-muenchen.de/25568/1/MA_Klug.pdf Klug, Felix ddc:500, Ausgewählte Abschlussarbeiten, Statistik

Missing data is an important issue in almost all fields of quantitative research. A nonparametric procedure that has been shown to be useful is the nearest neighbor imputation method. We suggest a weighted nearest neighbor imputation method based on Lq-distances. The weighted method is shown to have smaller imputation error than available NN estimates. In addition we consider weighted neighbor imputation methods that use selected distances. The careful selection of distances that carry information on the missing values yields an imputation tool that outperforms competing nearest neighbor methods distinctly. Simulation studies show that the suggested weighted imputation with selection of distances provides the smallest imputation error, in particular when the number of predictors is large. In addition, the selected procedure is applied to real data from different fields.

We watched so much TV that we lost the notes. LOST. I strongly suspect that the cat ate them. Timestamps are approximate: MISSING DATA due to cat snacking We have a NEW phone number: 617-858-0733. We also have a NEW email address: thebigredpodcast@gmail.com. Find us on Twitter: @bigredpodcast Visit us at www.bigredpodcast.com or email us: thebigredpodcast@gmail.com Musical score provided by Ernesto Burden, www.ernestoburden.com.

This week Beate Wieseler from IQWiG (Institut für Qualität und Wirtschaftlichkeit im Gesundheitswesen) tells us how they uncovered data on the antidepressant reboxetine. Also Angela Thomas and Julia Anderson, haematologists from the Comprehensive Care Haemophilia and Thrombosis Centre, Royal Infirmary of Edinburgh, explain how to investigate a child who bruises easily.

The problem of missing data is well known, especially in cases where drug companies conceal evidence. However pharmaceutical industry misconduct is not the only cause, and a cluster of papers in this week's BMJ show how aspects of the culture of medical science contribute to the problem. Elizabeth Loder, BMJ's clinical editor, talks to Harlan Krumholz (Harold H Hines Jr professor of medicine at Yale University) and Joseph Ross (assistant professor of medicine, also at Yale) about missing data from US publicly funded trials. Lisa Bero (professor at the Department of Clinical Pharmacy, University of California) describes how adding missing data to meta-analyses of drug trials can change the results, and Richard Riley (senior lecturer in medical statistics at Birmingham University) explains why individual participant meta-analyses aren't as balanced as we may think.

Sponsored by the Center for Interdisciplinary Research on AIDS (CIRA) at Yale University's Interdisciplinary Research Methods Core. The presenters are Russell Barbour, Ph.D., CIRA, and Eugenia Buta, Ph.D., CIRA and The Yale Center of Analytical Studies (YCAS).

We end ANOVA and move on to correlation and simple regression Music "Don't Give Up" by Rotten Bark

Empty Cells lecture

Variable selection has been suggested for Random Forests to improve their efficiency of data prediction and interpretation. However, its basic element, i.e. variable importance measures, can not be computed straightforward when there is missing data. Therefore an extensive simulation study has been conducted to explore possible solutions, i.e. multiple imputation, complete case analysis and a newly suggested importance measure for several missing data generating processes. The ability to distinguish relevant from non-relevant variables has been investigated for these procedures in combination with two popular variable selection methods. Findings and recommendations: Complete case analysis should not be applied as it lead to inaccurate variable selection and models with the worst prediction accuracy. Multiple imputation is a good means to select variables that would be of relevance in fully observed data. It produced the best prediction accuracy. By contrast, the application of the new importance measure causes a selection of variables that reflects the actual data situation, i.e. that takes the occurrence of missing values into account. It's error was only negligible worse compared to imputation.

Random Forests are widely used for data prediction and interpretation purposes. They show many appealing characteristics, such as the ability to deal with high dimensional data, complex interactions and correlations. Furthermore, missing values can easily be processed by the built-in procedure of surrogate splits. However, there is only little knowledge about the properties of recursive partitioning in missing data situations. Therefore, extensive simulation studies and empirical evaluations have been conducted to gain deeper insight. In addition, new methods have been developed to enhance methodology and solve current issues of data interpretation, prediction and variable selection. A variable’s relevance in a Random Forest can be assessed by means of importance measures. Unfortunately, existing methods cannot be applied when the data contain miss- ing values. Thus, one of the most appreciated properties of Random Forests – its ability to handle missing values – gets lost for the computation of such measures. This work presents a new approach that is designed to deal with missing values in an intuitive and straightforward way, yet retains widely appreciated qualities of existing methods. Results indicate that it meets sensible requirements and shows good variable ranking properties. Random Forests provide variable selection that is usually based on importance mea- sures. An extensive review of corresponding literature led to the development of a new approach that is based on a profound theoretical framework and meets important statis- tical properties. A comparison to another eight popular methods showed that it controls the test-wise and family-wise error rate, provides a higher power to distinguish relevant from non-relevant variables and leads to models located among the best performing ones. Alternative ways to handle missing values are the application of imputation methods and complete case analysis. Yet it is unknown to what extent these approaches are able to provide sensible variable rankings and meaningful variable selections. Investigations showed that complete case analysis leads to inaccurate variable selection as it may in- appropriately penalize the importance of fully observed variables. By contrast, the new importance measure decreases for variables with missing values and therefore causes se- lections that accurately reflect the information given in actual data situations. Multiple imputation leads to an assessment of a variable’s importance and to selection frequencies that would be expected for data that was completely observed. In several performance evaluations the best prediction accuracy emerged from multiple imputation, closely fol- lowed by the application of surrogate splits. Complete case analysis clearly performed worst.

Abstract: Survey data often include missing values due to nonresponse. Especially, sensitive questions such as questions about income or assets tend to show higher percentage of missing values. When missing values occur, complete-case analysis may lead to biased estimates of parameters. Korean Longitudinal Study of Aging(KLoSA) is a longitudinal study to evaluate aging trends in the Korean population and apply the results to the social welfare and labor policy. KLoSA collected baseline data in 2006 and first follow-up data in 2008. We conducted multiple imputation based on hotdeck to handle missing values in KLoSA baseline and first follow-up data. In this study, we explain the imputation strategy adopted for filling in missing values of major outcome variables in KLoSA.

Missing Data, run for your lives!

Consider the sample of two binary variables X and Y with some missing structure within X or Y. The knowledge about the corresponding values of the observed covariate allows to play through all possible `originally' complete data sets. After defining the notation, including some theoretical work, a test for non--MCAR within the complete case table is presented. Simulating all possible tables enables some testing on non--MAR. A simulation experiment is used to illustrate this context.

Assuming the nonavailability of some observations and the availability of some stochastic linear constraints connecting the coefficients in a linear regression, the technique of mixed regression estimation is considered and a set of five unbiased estimators for the vector of coefficeints is presented. They are compared with respect to the criterion of variance covariance matrix and conditions are obtained for the superiority of one estimator over the other.

This paper deals with the application of the weighted mixed regression estimation of the coefficients in a linear model when some values of some of the regressors are missing. Taking the weight factor as an arbitrary scalar, the performance of weighted mixed regression estimator in relation to the conventional least squares and mixed regression estimators is analyzed and the choice of scalar is discussed. Then taking the weight factor as a specific matrix, a family of estimators is proposed and its performance properties under the criteria of bias vector and mean squared error matrix are analyzed.

Arminger and Sobel(1990) proposed an approach to estimate mean- and covariance structures in the presence of missing data. These authors claimed that their method based on Pseudo Maximum Likelihood (PML) estimation may be applied if the data are missing at random (MAR) in the sense of Little and Rubin (1987). Rotnitzky and Robins (1995), however, stated that the PML approach may yield inconsistent estimates if the data are (MAR). We show that the adoption of the PML approach for mean- and covariance structures to mean structures in the presence of missing data as proposed by Ziegler (1994) is identical to the complete case (CC) estimator. Nevertheless, the PML approach has the computational advantage in that the association structure remains the same.