Podcasts about statistical methods

Uusi tutkimusnäyttö saattaa viitata siihen, että Raamatun syntiinlankeemuskertomuksessa on jälkiä kymmeniä tuhansia vuosia vanhasta myytistä, josta eri kulttuurien lohikäärmetarusto polveutuu. Eedenin käärmeen tulkitseminen lohikäärmeeksi paljastaa uusia puolia Aadamin ja Eevan tarinasta - ja auttaa yllättävällä tavalla purkamaan sen päälle kasautuneita naisvihamielisiä tulkintoja. Lähteet ja linkit d'Huy, J. (2013). Le motif du dragon serait paléolithique: mythologie et archéologie. Préhistoire du Sud-Ouest, 21(2), 195–215. d'Huy, J. (2014). Mythologie et statistique. Reconstructions, évolution et origines paléolithiques du combat contre le dragon. Mythologie française, 256, 17–23. d'Huy, J. (2015). Statistical Methods for Studying Mythology: Three Peer Reviewed Papers and a Short History of the Dragon Motif. The Retrospective Methods Network Newsletter, 9, 125 127. d'Huy, J. (2016). Première reconstruction statistique d'un rituel paléolithique : autour du motif du dragon. Nouvelle Mythologie Comparée / New Comparative Mythology, 3. - BibleProject: Did God Punish Women with Pain in Childbirth? https://youtu.be/h_zIJt0Kpes?feature=shared Aiemmat lohikäärmeaiheiset jaksot -Harhaoppia 13. Raamatun kaaoshirviöt https://open.spotify.com/episode/1LtFenXwE0EQoRPmfDL335?si=5rXqFHTcTHmDEs6mfgub4w - Harhaoppia 28. Markus ja Benjamin ratkaisevat kärsimyksen ongelman https://open.spotify.com/episode/3K17ALdTjVgNCp7agGtsgk?si=_UF-fABWQx-Q8_iRVBETCQ Harhaoppia 75. Eedenin maantiede https://open.spotify.com/episode/41tUck4lNdvmLXAS3d7fQ4?si=LFsCsvk1SG-SWjMTx-6-hg - Harhaoppia 78. Näin Raamatun alkukertomukset muokkaavat vanhempia myyttejä https://open.spotify.com/episode/6Mt1Bm8NDSPCWmfGwsTFXp?si=Qrzj2m6aRPy6FefPAL81Ow - Uskonto on tylsää 65. Myytit https://open.spotify.com/episode/78fpb5AFhYAPs6MFCNbYjN?si=XviLTlyUQzeu2ec6yms-oQ  ___ Harhaoppia on väitöskirjatutkija Markus Finnilän (TM) ohjelma täynnä epätervettä teologiaa ja vääriä vastauksia elämän suurimpiin kysymyksiin, jossa etsitään suuntaviittoja kohti sellaista uskoa, joka ei tarkoita omantunnon tai järjen hylkäämistä. Palautetta, kysymyksiä ja kommentteja voi lähettää osoitteeseen harhaoppia@gmail.com sekä Facebookin tai Instagramin kautta. Joihinkin viesteihin Markuksen kerrotaan joskus muistaneen vastatakin.   Kuuntele myös Harhaoppia-podcastin sisarpodcast Uskonto on tylsää.   Teemamusa Dan Koch

At the start of October, lots of the Unit's clinical trials methodology researchers travelled to Edinburgh, for the 7th International Clinical Trials Methodology Conference (ICTMC). ICTMC is the largest academic-led conference on clinical trials, bringing together trialists from across the globe to present their latest work in trials methodology. This year's conference featured a wide variety of workshops, talks and poster presentations from MRC Clinical Trials Unit researchers. In this latest episode of the Trial Talk podcast, four of the Unit's early career researchers discuss the work they presented at ICTMC 2024. Gideon Darko Asamoah tells us how he will deliver a core outcome set for trials in severe malaria; Jingyi Xuan describes her project addressing intercurrent events in platform trials; Dongquan Bi discusses the effects of different cluster sizes in cluster trials; and finally, Kate Roberts explains how she aims to improve the wording around consent for accessing trial participants' healthcare records. For questions or feedback on the series, message us at mrcctu.engage@ucl.ac.uk For more information and to access the transcript: https://bit.ly/4f9grVi As a listener, your opinion is very valuable to us. Please help us to improve the podcast in future by filling in this short survey: forms.office.com/e/PjfjQ5Mn6g More resources: • Developing a core outcome set for severe malaria trials poster: https://www.mrcctu.ucl.ac.uk/posters/identifying-a-comprehensive-long-list-of-outcomes-for-developing-a-core-outcome-set-for-severe-malaria-treatment-trials-a-systematic-review-and-inclusion-of-outcomes-important-to-patients-and-caregivers/ • Specifying estimands and estimators in trials with complex designs poster: https://www.mrcctu.ucl.ac.uk/posters/specifying-estimands-and-estimators-in-trials-with-complex-designs/ • Demystifying estimands in cluster-randomised trials paper in Statistical Methods in Medical Research: https://journals.sagepub.com/doi/10.1177/09622802241254197 • Estimands in cluster-randomised trials paper in International Journal of Epidemiology: https://academic.oup.com/ije/article/52/1/107/6644521 • Current practice around the use of estimands in cluster randomised trials, and the impact of informative cluster size on inference poster: https://www.mrcctu.ucl.ac.uk/posters/current-practice-around-the-use-of-estimands-in-cluster-randomised-trials-and-the-impacts-of-informative-cluster-size-on-inferences/ • ICTMC 2024 Poster 167 – A rap song (Lyrics by Dongquan Bi, beat by David Fitzgerald): https://youtu.be/s7qZ5TnOlWU?si=iXwfFfSAWwJ-fAmk • CrossWord poster: https://www.mrcctu.ucl.ac.uk/posters/crossword-consent-to-access-and-use-healthcare-systems-data-for-clinical-trials-a-review-of-current-language-in-participant-facing-materials/

In this JCO Precision Oncology Article Insights episode, Miki Horiguchi summarizes an editorial: “Expanding the Reach of Personalized Medicine in Cancer Care: Current Progress and Future Directions of JCO Precision Oncology” by Dr. Yushu Shi et al. published on May 30, 2024. TRANSCRIPT Hello and welcome to JCO Precision Oncology Article Insights. I'm your host Miki Horiguchi, an ASCO Journals Editorial Fellow. Today, I will be providing a summary of the article titled “Expanding the Reach of Personalized Medicine in Cancer Care: Current Progress and Future Directions of JCO Precision Oncology”. This is an editorial by Dr. Yushu Shi and colleagues that investigated trends in publication, peer review, and global influence of JCO precision oncology. Before getting into the editorial, I would like to briefly introduce to precision oncology and the JCO Precision Oncology journal as a leading platform for research in this field. Precision oncology is a personalized medicine approach that leverages advances in genomics and molecular profiling of tumors, biomarker-driven decisions, and targeted therapies to enhance clinical care for patients with various cancer types. Since there are many aspects to consider, such as biologic, clinical, and statistical aspects, advances in precision oncology also come with numerous challenges. These include identifying targetable mutations and addressing tumor heterogeneity and drug resistance. Other challenges are developing new study designs and statistical analysis methods to evaluate new approaches, as well as developing methods to manage large and complex datasets.  Since the American Society of Clinical Oncology introduced the journal JCO Precision Oncology (or JCO PO) in 2016, it has played an important role as a dedicated platform for publishing high-quality research and promoting discussions on those challenges. JCO PO is a peer-reviewed, online-only, article-based journal publishing articles across multiple categories. These include original reports, case reports, review articles, commentaries, correspondence, editorials, special articles, and molecular tumor board case discussions. The journal's contribution to the advancement of the field is reflected in the journals' impact factor, which was 4.6 in 2022 and 5.3 in 2023.  In the editorial, Dr. Shi and colleagues first investigated the publication trends from 2017 to 2022, highlighting cancer types, article types, the number of citations, and topics of papers published in JCO PO that have had broad impact. The papers accepted at JCO PO covered a broad range of research topics, including genomics-driven tumor treatments, molecularly selected targeted therapy, translational oncology, cancer biomarkers, gene expression and profiling, biostatistics and clinical trial methodology, epidemiology, and cancer prevention and control. The most common cancer types are thoracic, GI, and breast cancers. Original reports were more likely to be cited than case reports. The average number of annual citations for original reports was 4.33, while it was 1.39 for case reports. The authors listed the 10 most cited papers published in JCO PO in a table. The most cited paper was an original report titled “Landscape of Microsatellite Instability Across 39 Cancer Types” by Bonneville and colleagues. The paper has been cited more than 600 times since it was published in 2017.   Next, the authors conducted an analysis to see trends in peer-review. When manuscripts are submitted to JCO PO, they go through a rigorous peer-review process. Reviewers evaluate them based on five key metrics: importance of the study, originality, quality of writing, relevance to clinical practice, and scientific strength. Each metric is rated on a scale from 1 to 5, with higher scores indicating better performance. Dr. Shi and colleagues compared the rating scores between accepted and rejected manuscripts of original reports and case reports. They found that the median score of accepted manuscripts was above 3.5 for all metrics. The findings highlight that no single metric determines acceptance, underscoring the importance of excelling in all five areas when developing manuscripts. Finally, the authors looked at trends in global influence in JCO PO. Counting the country where the corresponding author's institution is located, Dr. Shi and colleagues found that JCO PO has accepted manuscripts from 36 countries, indicating a steady increase in its global reach. The United States accounts for about 71% of the total contributors. The other top contributors include France, Canada, Italy, Australia, the Netherlands, Germany, Japan, the United Kingdom and China. Notably, global collaborations among authors have significantly increased, with the proportion of papers from multiple countries more than doubling from 12.5% in 2016 to 26.5% in 2022. These facts reflect JCO PO's ongoing commitment to engaging with the international precision oncology community and encouraging global research submissions. At the end of the editorial, the authors provided some guidance for future authors. Across original reports and case reports, successful submissions to JCO PO typically have a translational focus. They provided a mechanistic understanding of tumor biology and utilized cancer genomics to inform clinical decision making. The authors also highlighted several underrepresented but growing areas of interest at JCO PO. These include pediatric oncology, sarcomas, ethics, trial methodology, informatics, computational approaches, and statistical methods related to precision oncology. Furthermore, the increasing significance of germline genetics, pharmacogenetics, molecular diagnostics, and molecular epidemiology in precision oncology has been recognized and valued by JCO PO. JCO PO also has special series issues. The special series feature timely research topics, such as Equity in Precision Medicine, Statistical Methods for Precision Oncology, and Next Generation Sequencing. Through these special series, JCO PO continues to lead the advancement of the application of precision oncology across a diverse patient population. The authors also provided points to consider when submitting case reports. For successful case report submissions, especially n-of-1 reports that showcase novel findings with potential clinical impact, it is crucial to include robust data to support the clinical observations, investigate underlying mechanisms, and ensure proper protection of patients' identity and autonomy. An n-of-1 report alone is often insufficient for publication. Successful case reports typically extend beyond a single patient, examining the phenomenon in multiple patients and providing mechanistic validation, either in vitro or through preclinical models. Thank you for listening to JCO Precision Oncology Article Insights and please tune in for the next topic. Don't forget to give us a rating or review and be sure to subscribe, so you never miss an episode. You can find all ASCO shows at asco.org/podcasts.  

Hello and Greetings Listeners!  We have another great Research Insights Podcast to bring to you today.  Listen to the conversation between Host Dale Hall, Managing Director of Research at the Research Institute and Guests Erica Baird, Principal and Consulting Actuary at Milliman and Joe Long, Consulting Actuary & Data Scientist at Milliman discussing the report "Statistical Methods for Imputing Race and Ethnicity."  After listening to the podcast, visit the landing page to read the report and check out the tutorial. Report Landing Page:  https://www.soa.org/resources/research-reports/2024/stat-methods-imputing-race-ethnicity/ We welcome your feedback!  ResearchInsights@soa.org

  The ONS led the way informing the UK response to the Coronavirus pandemic. But what lessons can be learned and how can we best prepare not only ourselves, but the rest of the world, for the next pandemic?     Transcript  MILES FLETCHER  This is Statistically Speaking, the Office for National Statistics (ONS) Podcast. I'm Miles Fletcher, and as we approach the darkest months of winter, we're revisiting COVID-19.   Now the ONS doesn't do predictions, and we're certainly not forecasting a resurgence of the virus, either here in the UK or anywhere else. But pandemic preparedness has been the driving force behind two important pieces of work that we're going to be talking about this time. Looking beyond our shores, how well equipped now is the world in general to spot and monitor emerging infections? We'll hear from Josie Golding of the Wellcome Trust on that, including how even weather events like El Nino could affect the spread of viruses. We'll also talk to my ONS colleague, Joy Preece about the pandemic preparedness toolkit, a five-year project backed by Wellcome to create and develop resources that will help countries with health surveillance in the event of future pandemics.   But first, and closer to home, a new UK winter surveillance study to gather vital data on COVID-19 is now well underway. Jo Evans is its head of operations. Jo, this is a brand new COVID-19 survey the ONS is running in partnership with the UK Health Security Agency (UKHSA). What is the new survey and what's it going to be monitoring over the winter?  JO EVANS  So this is now the winter COVID infection study. And we're going to be going out to, I think we've got 145,000 people signed up, and we're going to ask them to take a lateral flow test to see if they are testing positive for COVID-19. Then we'll ask them to tell us a little bit about how they're feeling, what symptoms they have and some other household information - what work do they do? Do they have caring responsibilities? And so on.   MF  So we're gonna be getting people to take a test and everyone's familiar of course now with administering their own lateral flow test, that wasn't the case back in the early days of the pandemic, when it was a new thing for the vast majority of us. So they'll take a test that'll tell us whether they are positive or negative for COVID-19. And on top of that, we're going to be gathering data in the form of a questionnaire.   JE  That's right. And then this is a collaboration this time, so we'll be working with the UKHSA. I mean, we've worked with them on the COVID infection study before, but this time what we'll be doing is looking at those responses of how many people are telling us that they have COVID-19 And we'll be trying to understand that by where people live or their age group and so on, but we'll be sharing that information with UKHSA and they will then be looking at what the impact is on hospitals. So what they call the infection hospitalisation rate, how many people are going into hospital because they have COVID, so it'll really help us understand what pressures there are on the NHS over this winter period.  MF  And that will give us some inkling, once again, about how many people are infected but not actually displaying any symptoms?  JE  Absolutely. And we do ask people about their symptoms and if they tell us they test positive, we'll then be sending them a second questionnaire, a follow up, asking them to keep testing until they get two consecutive negative tests so that we can see how long they are testing positive, but we'll also ask them how long did their symptoms last and did they need to go and see a doctor, did they take any medication, so really trying to understand how they're experiencing that period of illness.  MF  So during those critical winter months, that'll give us some insight into what's really going on on-the- ground and in communities.   JE  That's right and we're running this study from November right through to March so that we can understand that, because COVID, unlike flu, it's not a seasonal virus, but we know that the NHS really suffers through the winter with those increased pressures, with more people needing their services. And this is about understanding what's happening out there. In the community, and what impact that is having on our healthcare services.  MF  Another very important aspect of that is we're going to be monitoring for people who say they're suffering the symptoms of what is popularly known as long-COVID, ongoing impacts of the virus, and that will fill a very important evidence gap won't it.  JE  Absolutely. We will in a follow up questionnaire be asking people how long they've had COVID for and whether they have long-COVID. And interestingly, in some research we did when we were designing the questionnaire, long-COVID sufferers told us that they know precisely what date their symptoms started and how long they've had it because of the impact it's been having on their lives. So we are hopeful that this study will provide some really useful information.  MF  So 145,000 people taking part. Has it been difficult to get as many people as that involved?  JE  Do you know what, we got halfway there within the first 48 hours, people were so keen to take part in this study. We've really been surprised about that.  MF  It's probably a reflection of the success of the profile that the original study had.  JE  I think so, people are really keen to do their bit here and get involved in this study. And we've had a lot of participants, particularly in the older age groups, who have signed up so we will have to do something that we call ‘weighting of the data' across the different age groups, but we do this all the time and we are also going out to those under 16s, right up to the over 70s.  MF  And as well as taking part in a very important public study, people get a COVID test for free and can see for themselves whether they've been affected.  JE Yeah, think that's one of the things people are keen to do, particularly over the winter periods when we're going to be mingling and visiting family, that reassurance really that you're going to test every month and find out whether or not you have COVID, I think we all want to make sure that we are virus free before we go and see our loved ones over Christmas, for example.  MF  Well, we're meeting to discuss this in mid-November. The first results are still a few weeks away but how are things going, we've got enough people? Are the tests out in the field yet?  JE  The tests are out in the field. I think we're looking to get two publications in before Christmas, so testing windows start next week. We're expecting around 25,000 people a week to take their tests and answer their questionnaire.  MF  And over the course of a month then, all 145 we hope will have been covered?   JE  Yes, I mean 145 is a fantastic number, and if we get all of them taking their test kits each month, then yes, that number will be higher. But even if we were looking at a 50 or 60% response rate, that is excellent for a social survey.  MF  Yes, and all the time, what we've heard in other contexts, is that it is difficult to get people to take part in surveys, but certainly in this case people can see the need for it and have come forward in their thousands. It's possibly worth pointing out though that you do have to be selected to take part, that's very important isn't it, that we've never looked for volunteers. We've selected households randomly and that approach, that's very important to make this a really, really reliable survey isn't it?  JE  Yes, and as soon as there was information about the study in the newspapers earlier this year, we had people ringing up and asking to take part and we've had to explain to them that we want a nice random sample so that we can have a fully representative study.  MF  So ONS will be producing the figures then it's over to our colleagues in the UK Health Security Agency to interpret what that means from a public health point of view, and what response might be necessary. Absolutely.  JE  Absolutely. And they'll be producing some statistics as well. Looking particularly, as I said, at that infection hospitalisation rate.  MF  So are we expecting the virus to take off again, or is it just a just a precaution to be monitoring things in this way?  JE  When we started this, it was more about understanding if there would be that impact on the NHS over the winter. But then we did see back in September, a new variant, particularly in the US, and as you know, from looking at COVID over the past few years, when you see a new variant coming, sort of appearing in one country, you know that it will come here eventually. So, it's about keeping track of that really, although because we are doing lateral flow tests, we won't actually have information about what kind of variant people have, but it will just be to look to see whether we're seeing an increase in positivity in the community.  MF  Okay, so all eyes on the first result, and we wish you, and the team getting the survey together once again, every success on what is a highly valuable and important exercise.  So we've heard how the new winter surveillance study is helping us track ongoing COVID infection here at home. But we're also using the experience the ONS gained during the last pandemic to prepare not only ourselves, but other countries around the world for another one, Josie, with that global perspective in mind, my first question to you just to get us started is what have been the biggest learnings, the biggest take homes if you like, for Wellcome from the pandemic. And what's your priority now as an organisation considering how best to respond to others?  JOSIE GOLDING  Thank you for having me today, I think this is good to be reflecting on COVID in the future. So the biggest take home message is, probably I can look at the positives and the negatives, so I'll go on the negatives first.   So I think we had a lot of the tools for responding to outbreaks and bigger events but I think we weren't prepared to deal with such a massive pandemic that we saw at SARS-CoV-2, we had expected to prepare for something like influenza and of course we probably didn't use our imagination of how the impact would be so great, affecting people in so many different ways. I think we need to really use that imagination going forward, it's about thinking through the variety of different impacts we could see across different populations. I think we've learned a lot on how we communicate with the public, with the key people who are involved, and take those lessons because I think we did struggle. I think globally, not just Wellcome as one of the actors on communicating the importance, and the push to be better prepared to respond to these pandemics.   One of the successes, and I'll put this up from a Wellcome point of view, really was the true integration of research into the response. And you know, this has been building up for many years from the Ebola West Africa outbreak in 2014. And tested again, and tested and tested and refined, on how we do this across the small research community who are engaged in those relatively smaller outbreaks to now a complete game change on how people expect research to be integrated into outbreak responses through pandemics.   So I think that's now set the new status quo, and before I had to convince people of the importance, I think the importance now speaks for itself.   MF  Yes, it was notable in the early stages of the pandemic, those countries, notably in East Asia that have had experience of major respiratory viruses, and dealing with those on a public health point of view, didn't seem to be much better prepared than us in the West, who perhaps have underestimated the risks?  JG  It is absolutely true. You know, it is testing the system over and over and over again. So you know who your stakeholders were, you knew how to get things done quickly and at speed. And I think that's the one piece we have to keep remembering that we can keep preparing, but you still need to keep testing the system to ensure that it works in practice. But through it all I would say, you know, one of the things that Wellcome is taken away from SARS-CoV-2 is really the belief that we can't predict exactly what's going to come next when it comes to emerging infectious diseases. We have to keep that in mind, but actually the way to test the system time and time again, is dealing with the health priorities right now. So things like antimicrobial resistance. We know this has been a growing threat for many years. It's had some setbacks through SARS-CoV-2 and the pandemic, you know, we need to really re-energise the community to really take this seriously and to finance and to conduct the research that's required. But there are other threats that are, you know, common health issues, common infectious diseases that countries are dealing with, and we should be integrating the readiness, haemorrhagic fevers, viral fevers, other viruses, whatever it may be, into how we deal with those everyday infectious diseases.  MF  And what's the legacy been from an analytical point of view of the first few years for the period that is now known as peak COVID? Have we got that to draw on now because we're seeing the virus continuing to emerge? We're seeing potential threats from new variants and possibly other viruses.  JG  I don't think it's evenly applied across the globe on taking advantage of the systems. The approaches that were built up during SARS-CoV-2, some countries are able to maintain some of the resources that have been built or pivot into other health priorities. But that is a bit of a gap that we are seeing. I'll give an example of what I think is a great statistic, you know, for pathogen genomic sequencing and how that was used to track variants and making that as close to real time as you could find through the accelerator and diagnostics working group that mapped out the capacity in countries to be able to conduct pathogen genetic sequencing. And I think at the time, this is going back to 2022, that 77% of the world's countries were able to conduct sequencing when that's a massive game change for a tool that really wasn't a, partly an add on, into how you would do some of the epidemiological research at the beginning of outbreaks. So I think being able to pivot that tool and make sure that these types of facilities and the training and the expertise that people have built up over time can be sustained, working with those communities to be able to identify what are the real use cases for pathogens. And so I think, yes, some of it has probably not been evenly distributed, but we could always be doing more to be able to ensure we can better understand the variants as they come about, but also, what does it mean for a variant you know, how, what changes will that make, what impact will that have on our health?  MF  Hearing the UK with our partners, the UK health security agency, we are preparing, as you well know I'm sure, to run a further study going into the winter. What is the role of studies all like that? Are they uniform now across the world or this is not as similar surveillance programmes going on? Or do we remain a bit of a one off in doing this in the UK?  JG  I don't believe that this is evenly spread around the world. We ourselves at Wellcome had made a decision to continue funding our SARS-CoV-2 work on the genomics as well as the characterization of these variants as they come out. And what difference does it make in people who've been vaccinated or with other health conditions? We know when we've engaged with the research community across a variety of countries around the world, it ends up being very novel that this research has continued to happen. So I do think there is a gap, and it is becoming more challenging for public health institutes, WHO and others, to gather this information to understand are the vaccines still effective when we have these new variants, are they more transmissible, and other impacts that we would assess for those new variants. So I do think it's becoming more limited, and so of course, we need to make sure that the data we do generate is of high quality.  MF  The focus has been very much on COVID, but of course as we've seen historically and in other countries, other viruses have emerged and have serious public health consequences in those countries globally. What other emerging diseases do we need to have our eye on at the moment?  JG  Since SARS-CoV-2 really picked up we've had a global impacts event that affected you know, very select communities around the world, and is still ongoing, but not to the same level. We have the ongoing threat of avian influenza, we have El Nino upon us, which is likely to further impact the rates of cholera that we're going to see as well as impacting temperatures, so mosquito borne viruses and other types of arboviruses, potentially broader than that, so it is happening right now. I don't think we even need to sit back and think what it could be. And there are many events that we need to be preparing for. And particularly with something like El Nino as a particular weather event but thinking about the climate crisis. This is only going to grow we need to really collect the evidence now to understand what difference will it make what risks will it pose by experience, and geographical distribution further afield.  MF  Yes, can you unpack that a bit for us, because most people will be aware of El Nino as a meteorological phenomenon. How does that translate into public health impacts?  JG  The whole background and where we've been watching and waiting for more certainty and whether this was actually going to happen this year, but it's a very high, I think it's now greater than 90% certainty it's going to happen from this part of the year onwards, and and it will vary depending on where in the region it will impact you for droughts or flooding. And of course we need to better understand well, what impact would that have on cholera? Cholera is a prime example. While it's not directly linked to El Nino as it stands right now, we have seen such a change in the cholera distribution in Malawi being a great example where it's seeing rises in cases outside of the expected weather event. So you'd expect it in flooding season but you're seeing it more in dry seasons. So El Nino will make this worse potentially. It's being able to track it and understand the issue we have with events like El Nino is that we don't have enough information on it. We need to be better from a researcher's perspective, we need to just understand the researchers in those countries that are likely to be affected, their opportunity to gather as much evidence about the impact of El Nino so that when it comes around again, we'll be better able to apply what we've learned now.  MF  Without wishing to sensationalise, what do you think the risks are of another big global pandemic of the sought we saw with COVID-19?  JG I'm a virologist by training so I'm always thinking that viruses hold the opportunity for some of the greatest opportunity for change. I'm always hopeful that you know these risks like the SARS-CoV-2 are actually quite rare events, to see something take off and to be able to transmit that successfully to humans. We see there are many events where we have what people refer to as spillover events between animals to humans, but it takes quite a change and we don't fully understand what the change might be or why it adapts for people to be more susceptible. So I think it is a risk, it's a known risk even for SARS-CoV-2 which could change drastically. It's a very early stage in understanding this virus and how it operates. So I think we just have to be prepared, to be continually preparing, for the event that it could happen. I think influenza is the greatest one that it would be surprising if we didn't have a global event for influenza of some kind in the next few years. We've been preparing for this for quite some time.  MF  Is that the one that was anticipated then? Because if you look back historically, and this was the big comparison of course that was made with the COVID pandemic, it was so called Spanish flu wasn't it after the First World War, which was a huge global pandemic.  JG  Yes, it has been the one that we've always focused on. And if we look at the way that we've managed to monitor the change in the evolution of the virus, and to build the infrastructure globally, to be able to do the research and track that within laboratories and share information on that to help inform the vaccine production, which is very seasonal, influenza has changed every year. This is decades in the making. It's been ongoing for decades, and still, you know, we still have problems with making sure we have enough vaccine at the right time in the right supply to be accessible to all. So even for something that we know is likely to come we still struggle to get to that level of preparedness and it takes a lot of effort and time and it will continue, hopefully SARS-CoV-2 will help evolve those structures and I know a lot of the interest has been to combine, where we can, with coronaviruses respiratory like illnesses in the future to make it more efficient, but it's a big undertaking to really map out what you can do for a single pathogen. So, we have to work to see where we can build in those efficiencies across multi pathogen approaches.  MF  So one response and this is a project that you're working on with ONS and we're going to talk about now, and bring Joy in to explain to us, and that's the pandemic preparedness toolkit. The ONS is developing alongside Wellcome as I say, Joy, you're part of the team at ONS creating the toolkit just to take you from the very beginning, how did the ONS get involved in this and why is ONS well placed to facilitate this work?  JOY PREECE  Well, this was a proposal that we put together for Wellcome in the aftermath of, I think some of the early years of the pandemic response, and the of course well-known Coronavirus Infection Study (CIS) which I was part of, and I think what we really learned during COVID, the during big years, 2020 and 2021 in the UK, was how important really active data monitoring was when a disease is multiplying exponentially. That's really frightening stuff. You can't afford to just sit back and wait and see. So the key to any successful response has to be figuring things out like the reproduction number really, really quickly. You know, you needed to know how many people were affected, how fast infections were increasing, how the numbers of infections related to numbers of deaths, and we saw during COVID that it wasn't enough to just wait until people were already so ill that they were turning up in a healthcare setting in a hospital or you know, even worse kept to have systems that could be producing those kinds of statistical insights early from a community setting. And so that's the unique approach that ONS really took here. Linking up our statistical offices with the public health agencies and the decision makers. They're using our experience with surveys, with administrative data, with data modelling and data science, drawing on connections that we had with academics, with expert epidemiologists to try and get answers to those important questions as quickly as we could. And I think the unique thing that the ONS and other statistical offices around the world can bring to this is the very fact that we aren't part of the public health systems. So we bring here in ONS that expertise in a social research settings or community settings here, and you know, even apart from numbers of infections, there's topics like employment patterns, travel and tourism opinions and lifestyle habits, which tells you really important things about how people are interacting and behaving which gives you the ability to do some really, really clever modelling or things like disease communicability as that's kind of the background ONS brought to this from our experience during COVID. But, of course, we have experience as well supporting capacity building with overseas national statistical institutes.  MF  Now regular listeners to these podcasts will know we recently spoke to our colleagues in Ghana, about everything they're doing in partnership with our own, so there's countries like Ghana, who are very much part of this pandemic preparedness project as well.   JP  That's right. So what we are looking to do from the back of everything we learned in the UK is to go out and work with, initially we've identified that we want to be working with three different partner countries to co-create a toolkit that can be generalizable, and that can be accessed globally. And what's really important here is that it isn't about, oh, well here's a model that the UK used and therefore it's applicable to everybody because yes, we just heard from Josie that's not the case. Different countries have different contexts, different experiences of different diseases and have built different infrastructures and skills as a result. So what we really want to do is generate that pool of knowledge internationally and co-create a toolkit that allows countries, based on their unique context, to draw from it and we're talking here of practical guidance. Statistical Methods, knowledge products, case studies, training materials, really this is about capacity building to support that kind of infectious disease surveillance, but in a way that may look very different depending on the country's context. So it's about an international community of practice here.  MF  Well, that's why it's a toolkit. Not a template for how to deal with a pandemic.  JP  That's absolutely right. So we're thinking about this under I think, three headings. So data collection on one hand, statistics and modelling on another but crucially, also the relationship building between statistical producers and public health professionals, and that's really essential, because you want to help toolkit users build those relationships of trust between analysts and the public health decision makers that needs to be already there. It needs to be there as a fundamental before a crisis hits because otherwise the opportunities for that kind of productive collaboration that ONS was able to do during the COVID pandemic, just become so so limited.  MF  And it's about sharing of learnings as well, sharing of intelligence...  JP  No, absolutely, absolutely right. This is why I talk about it as a co-created toolkit. This is something that will be kind of jointly delivered in collaboration between ourselves and the three countries that we end up working with. We're going through a process right now to kind of identify volunteers and select countries that we'll work with, but also our first stage of that we are launching with a couple of kind of lessons learned workshops are where we're inviting statistical Institute's from around the world and a number of large international NGOs and experts in the field to come and talk about their experience of you know, disease surveillance of COVID and pandemic response and of other disease response. I start drawing out you know, what is there that we can find in common, what are there that are common challenges that have been common enablers to make your situation better, what is there that collectively we identify as the key criteria for a toolkit that will have the most value to the most countries.  MF  And Josie, from Wellcomes point of view, what are what are Wellcome's ambitions for this piece of work?  JG  We're very keen to understand how that toolkit can be of value to others, but also think through what is that epidemic preparedness model? So how would you apply it in the future to whatever that disease may be? So we never know maybe during the lifespan of this project, there will be opportunities for the countries to be able to test it out to see what works for them in in real time. I mean, I hope there's not another pandemic, but we have to just work on that assumption that as we go along, during this particular project, there could be something that might have to test it. But we'll see.  MF  There was of course much soul searching about the effectiveness of particularly early response to COVID in certainly this country, in the UK and in other Western countries. But, of course, looking back, and now we have the data, it was the global south that was disproportionately affected.  JG  It's fair to say, and I think there's still an unanswered question why some countries were affected more and some were less, and I think Joy has be very clear about the different contexts that countries operate in, but that includes also the populations as well and the other diseases they might have seen, what other health issues. I know, there's been less cases observed on the African continent and of course, that is down to the ability to test, but to a degree it is a different population structure that we're seeing. So yes, we want to make sure that these types of tools are equitably shared, and applied to whatever the health requirements are for their systems. And I think this is the exciting part of this project. And I guess my main kind of message that I repeat to everybody is the only way to prepare for a future pandemic or a future epidemic is simply to deal with the health issues that we have right now. So make sure that we're thinking about things like MMR, and thinking about the impact of climate and understanding better how it's changing the dynamics of infectious diseases such as mosquito borne, or other viruses or malaria, you know, the common issues that many countries are facing and just act now rather than just planning for something. I want to see some real tangible research and systems being built. I think that's why the ONS approach for this is important because it's about just getting on with it. And not just, you know, coming up with a theoretical model, it is actually working with the countries to see how you're going to apply it now. So we have to just keep focusing on that it could be tomorrow. So just get going.  JP  Well, and I would just say aye because I completely agree Josie because it's very easy to get caught up in talking about a pandemic response. But of course, a pandemic response, you can only draw on the resources that are already there. There is no time in a crisis situation to be developing things that are substantially new. So what we're really talking about when we talk about a pandemic preparation is about supporting improved health statistics for all sorts of purposes. You know, data and modelling and communicating and understanding the statistical insight and actually having that really good disease, that has a multiplier benefit for a whole range of health outcomes. Whether or not we see a pandemic tomorrow, we should be planning, even if it doesn't happen tomorrow. And I think that's the critical thing in this, this isn't a once in 100 years. This is an event that is happening on a daily basis, when people are catching diseases and communicating diseases on a daily basis, and providing improved tools to support that has a benefit even in the absence of a large-scale event.  MF  And that's it for this episode of Statistically Speaking, next time, as the end of the year approaches, we'll be joined once again by the UK's National Statistician. If you've got a question for us then please ask us via @ONSfocus on the ‘X' social media platform, or Twitter for us traditionalists.  Thanks to all of our guests today and our producer Julia Short. You can of course subscribe to new episodes of Statistically Speaking on Spotify, Apple podcasts and all other major platforms.  ENDS

Bloomberg Radio host Barry Ritholtz speaks with Jon McAuliffe, who is co-founder and chief investment officer at the Voleon Group, heading the firm's investment-strategy research and development. McAuliffe has a substantial track record of successful innovation in applying statistical methods to real-life prediction problems, particularly in the financial markets. McAuliffe is also an adjunct professor of statistics at the University of California, Berkeley, where he earned his Ph.D.See omnystudio.com/listener for privacy information.

The statistical methods portion of the DNP project can be challenging for students. A school of nursing, in collaboration with the University's Department of Research, created a statistical methods algorithm for students to use while developing their DNP project. Drs. Johnston, Astrella, and Grimm explain the algorithm and its use.

In this episode, Early Career Editor Kevin C. Klatt, PhD, RD speaks with Briana Stephenson, PhD (she/her/hers), an Assistant Professor in the Departments of Biostatistics and the School of Publication about her recent publication, “Racial and ethnic heterogeneity in diets of low-income adult females in the United States: results from National Health and Nutrition Examination Surveys from 2011 to 2018” in the American Journal of Clinical Nutrition. Dr Stephenson discusses her research on robust profile clustering as an extension of latent class models to define dietary patterns in population subgroups, focusing in this analysis on low-income female adults to identify racial and ethnic differences in dietary patterns. As Dr Stephenson noted (as of March 2022), she is currently recruiting a postdoc in Statistical Methods in Population Health Disparities research.Be sure to connect with us! Dr Stephenson, @BJKstephenson; AJCN: @AJCNutrition; Dr. Klatt: @kcklatt. Find all of the publications from the American Society for Nutrition (@nutritionorg; @jnutritionorg) at our website: https://nutrition.org/publications/.

RLS Pitfalls In Study Design And Statistical Methods by SAEM

Andy and Dave discuss the latest in AI news and research, including the introduction of a lawsuit against Microsoft, GitHub and OpenAI for allegedly violating copyright law by reproducing open-source code using AI. The Texas Attorney General files a lawsuit against Google alleging unlawful capture and use of biometric data of Texans without their consent. DARPA flies its final flight of ALIAS, an autonomous system outfitted on a UH-60 Black Hawk. And Rafael's DRONE DOME counter-UAS system wins Pentagon certification. In research, Meta publishes work on Cicero, an AI agent that combines Large Language Models with strategic reasoning to achieve human-level performance in Diplomacy. Meta researchers also publish work on ESMFold, an AI algorithm that predicts structures from some 600 million proteins, “mostly unknown.” And Meta also releases (then takes down due to misuse) Galactica, a 120B parameter language model for scientific papers. In a similar, but less turbulent vein, Explainpaper provides the ability to upload a paper, highlight confusing text, and ask queries to get explanations.  CRC Press publishes online for free Data Science and Machine Learning: Mathematical and Statistical Methods, a thorough text for upper-class college or grad-school level. And finally, the video of the week features Andrew Pickering, Professor Emeritus of sociology and philosophy at the University of Exeter, UK, with a video on the Cybernetic Brain, and the book of the same name, published in 2011. https://www.cna.org/our-media/podcasts/ai-with-ai  

 Sameer Maskey is the Founder and CEO of Fusemachines Inc, a company that makes Artificial Intelligence accessible to everyone through education, software and services. Dr. Maskey has more than 18 years of experience in artificial intelligence, natural language processing, machine learning, and data science. After completing his PhD in Computer Science from Columbia University, he joined IBM Watson Research Center where he invented various statistical algorithms to improve speech-to-speech translation and question answering systems. Sameer currently serves as an Adjunct Assistant Professor at Columbia University where he teaches several courses including “Statistical Methods for Natural Language Processing” and “Programming for Entrepreneurs”. He has published more than 20 peer-reviewed articles and served as Session Chair, Program Committee member, and Review Committee member at ACL, HLT, ICASSP, NAACL and COLING. Sameer is an inventor of 15 United States patents and has authored over 20 publications. https://fusemachines.com/https://www.linkedin.com/in/sameer-maskey-92680232/https://twitter.com/sameermaskeyRebel HR is a podcast for HR professionals and leaders of people who are ready to make some disruption in the world of work.We'll be discussing topics that are disruptive to the world of work and talk about new and different ways to approach solving those problems.Follow Rebel HR Podcast at:www.rebelhumanresources.comhttps://twitter.com/rebelhrguyhttps://www.facebook.com/rebelhrpodcastwww.kyleroed.comhttps://www.linkedin.com/in/kyle-roed/ Managing Projects Doesn't Have to Be a MessBasecamp helps teams organize projects in a way that makes sense for all. Try Basecamp.BrandBuzzsprout - Let's get your podcast launched! Start for FREEDisclaimer: This post contains affiliate links. If you make a purchase, I may receive a commission at no extra cost to you.Support the showRebel HR is a podcast for HR professionals and leaders of people who are ready to make some disruption in the world of work. Please connect to continue the conversation! https://twitter.com/rebelhrguyhttps://www.facebook.com/rebelhrpodcasthttp://www.kyleroed.comhttps://www.linkedin.com/in/kyle-roed/

In today's episode, my guest Riley King and I explore the application of Bayesian statistical methods in the medical device industry. Riley is a thought leader in medical device design and development and has worked with class III medical devices for over 14 years. Riley shares with us · His experience in medical device R&D and working in a quality leadership position in medical device · The use of conventional, frequentist statistical methods in medical device development and some of the challenges he has faced when trying to apply conventional statistical methods · His explanation of Bayesian statistics, the difference between frequentist and Bayesian methods, and why use Bayesian methods · Real-life medical device use cases where Bayesian methods can be useful · His tools of choice, which includes the programming language R and specific packages for Bayesian methods · His learning journey and book/resource recommendations · Tips for those in a similar line of work interested in learning and applying these techniques · Trends he believes will shape the future of medical device technology and development Riley has a blog called [R]eliability – A Random Walk in the Medical Device Space (https://rileyking.netlify.app/) where he talks in detail about his experiences. Make sure to check it out; it is awesome!

Avishek Nag is an Analytics Practitioner & Data Scientist by profession specializing in Statistical Methods, Machine Learning and NLP. He has worked with companies like VMware, Cisco, MobileIron etc. He has also Authored two books on Machine Learning & one of the topics on Applied Probability.

WHY RECYCLE CULTURE? ©2021 VOL.1.   ISBN 978-976-96650-7-1 PODCASTPlausibly, as an Author, Cinematographer, Media Arts Specialist, License Cultural Practitioner and Publisher I have a passion which engages my analytical and intuitive cognition in all academic fora.  However, on this occasion as I was theorizing the “unresolvable philosophical thought question Why Recycle Culture? © 2021 ISBN 978-976-96650-7-1. I became au fait with several ethos. In this space and in my academic kit were several varied lens from which I have used to compose and frame the argument Why Recycle Culture? © 2021. A close up view provided detail of this theoretical abstract  (a) it seemingly comports itself as a nuance (b) which seems very complex and(c) is manipulated by human behaviour. Simply put Why Recycle Culture? © 2021 if it treats; or it is a process and it is what people do. William  Anderson GittensAuthor, Cinematographer Dip.Com., Arts. B.A. Media Arts Specialists’ License Cultural  Practitioner, Publisher,CEO Devgro Media Arts Services®2015,Editor in Chief of Devgro Media Arts Services Publishing®2015WORKS CITED Anholt, Robert R. H., and Trudy Mackay. 2010. Principles of behavioral genetics. Academic Press. ISBN 978-0-12-372575-2. Lay summary.    Aronin, Larissa; Hornsby, Michael; Kiliańska-Przybyło, Grażyna (2018). The Material Culture of Multilingualism. Cham, Switzerland: Springer. p. 25. ISBN 9783319911038. Black Dog Publishing (2006). Recycle : a source book. London, UK: Black Dog Publishing. ISBN 978-1-904772-36-1.  Buchli, Victor (2004). Material Culture: Critical Concepts in the Social Sci-ences, Volume 1, Issue 1. London: Routledge. p. 241. ISBN 978-0415267199.  Burn, Shawn (2006). "Social Psychology and the Stimulation of Recycling Behaviors: The Block Leader Approach". Journal of Applied Social Psychology. 21 (8): 611–629. CiteSeerX 10.1.1.462.1934. doi:10.1111/j.1559-1816.1991.tb00539.x  Carl A. Zimring (2005). Cash for Your Trash: Scrap Recycling in America. New Brunswick, NJ: Rutgers University Press. ISBN 978-0-8135-4694-0.  Cleveland, Cutler J.; Morris, Christopher G. (15 November 2013). Handbook of Energy: Chronologies, Top Ten Lists, and Word Clouds. Elsevier. p. 461. ISBN 978-0-12-417019-3.  "Cultural anthropology". Encyclopedia Britannica. Retrieved 2020-02-24.Dabb, C (May 1997). The relationship between weather and children's behavior: a study of teacher percep-tions. USU Thesis.    Dadd-Redalia, Debra (1 January 1994). Sustaining the earth: choosing consumer products that are safe for you, your family, and the earth. New York: Hearst Books. p. 103. ISBN 978-0-688-12335-2. OCLC 29702410 Dictionary of the Social Sciences (2008) [2002]. Calhoun, Craig (ed.). "Sociology". New York: Oxford University Press – via American Sociological Association.Farnsworth, Bryn. 4 July 2019. "Human Behavior: The Complete Pocket Guide." iMotions. Copenhagen. So What Exactly is Behavior? Purcell, Shaun. 2012. "Statistical Methods in Behavioral Genetics" Appendix in Behavioral Genetics (6th ed.), edited by R. Plomin, J. C. DeFries, V. S. Knopik, and J. M. Neiderhiser. Worth Publishers. ISBN 978-1-4292-4215-8. Retrieved 5 June 2020. Lay summary.Gittens,William  Anderson, Author, Cinematographer Dip.Com., Arts. B.A. Media Arts Specialists’ License Cultural  Practitioner, Pub-lisher,CEO Devgro Media Arts Services®2015,Editor in Chief of Devgro Media Arts Services Publishing®2015    Gorvett, Zaria (2019). "The Norwegian art of the packed lunch". BBC News.     Greenliving.lovetoknow.com/What_Will_Happen_if_You_ Hemakumara, GPTS. and Rainis, R. 2018. Spatial behaviour modelling of unauthorised housing in Colombo, Sri Lanka. KEMANUSIAAN the Asian Journal of HumanitieSupport the show (http://www.buzzsprout.com/429292)

23. Name the statistical methods that are highly beneficial for data analysts? The statistical methods that are mostly used by data analysts are: · Bayesian method · Markov process · Simplex algorithm · Imputation · Spatial and cluster processes · Rank statistics, percentile, outliers detection · Mathematical optimization --- This episode is sponsored by · Anchor: The easiest way to make a podcast. https://anchor.fm/app

There are a lot of facts and figures to sift through when it comes to the COVID 19 pandemic – there are death rates and infection rates to consider, as well as the paths of infection in a particular community. Investigating the pandemic is the focus of this episode of Stats and Stories with guests Ron Fricker and Steve Rigdon. Dr. Ronald D. Fricker, Jr. is a Professor of Statistics and the Associate Dean for Faculty Affairs and Administration in the Virginia Tech College of Science. He holds a PhD and an MA in Statistics from Yale University, an MS in Operations Research from The George Washington University, and a bachelor’s degree from the United States Naval Academy.  He is the author of Introduction to Statistical Methods for Biosurveillance published by Cambridge University Press and co-author with Dr. Steve Rigdon of Monitoring the Health of Populations by Tracking Disease Outbreaks and Epidemics: Saving Humanity from the Next Plague published by the American Statistical Association and CRC Press. Dr. Fricker is a Fellow of the American Statistical Association, an Elected Member of the International Statistical Institute, and a Fellow of the American Association for the Advancement of Science. Steve Rigdon is a Professor of Epidemiology and Biostatistics at Saint Louis University College for Public Health and Social Justice where he teaches about Baysian statistical methods. His research interests include Biosurveillance; models for election prediction; quality; survival analysis.

Alzheimer's Speaks Radio - Shifting dementia care from crisis to comfort around the world one episode at a time by raising all voices and delivering sounds news, not just sound bites since 2011. Lori La Bey will talk with two of the three Authors who wrote the book Fundamental Statistical Methods for Analysis of Alzheimer's and Other Neurodegenerative Diseases.  Authors Dr. Brittany N. Dugger and Dr. Jeffrey R. Wilson will share why they felt it was important to write this book, who will benefit from reading it and what people can expect to find in it. Feel free to call in and ask questions or make a comment on the book at 323-870-4602 Contact Information For Our Guests Dr. Brittany N. Dugger           Email            Dr. Jeffrey R. Wilson             Email           Phone:  480.213.4460       Purchase Their Book   Contact Alzheimer's Speaks 

This week, our host Dr Rob Doubleday sits down with Prof Daniela De Angelis, Professor of Statistical Science for Health at the University of Cambridge to discuss applying statistical methods to epidemiology, disease transmission, and how we're using models to understand the burden on the NHS posed by covid-19. CSaP's Science and Policy Podcast is a production of the Centre for Science and Policy at the University of Cambridge. This series on science, policy and pandemics is produced in partnership with Cambridge Infectious Diseases and the Cambridge Immunology Network. Our guest this week: Professor De Angelis works on developing and apply statistical methods to characterise epidemics, exploiting the complex body of available information. She is Deputy Director of the MRC Biostatistics Unit at the University of Cambridge. Professor De Angelis has been working throughout the covid-19 response as part of an epidemiological modelling group advising the UK Government. -- This series is hosted by CSaP Executive Director Dr Rob Doubleday, and is edited and produced by CSaP Communications Coordinator Kate McNeil. If you have feedback about this episode, or questions you'd like us to address in a future week, please email enquiries@csap.cam.ac.uk .

Alison (radiomics skeptic) and Nick (radiomics hopeful) sit down to discuss the benefits, drawbacks, and potential of radiomics. A variety of papers were discussed and can be found below. We also briefly discussed (though we did try not to) deep learning and broader AI applications.Are you a radiomics optimist or pessimist? Tell us at https://www.reddit.com/r/HormesisPodcast/comments/ct6p1q/episode_4_radiomics_how_to_maybe_classify_your/.Listen and subscribe to our podcast at Apple Podcasts, Stitcher, Google Podcasts, or through the RSS Feed.References:[1] Philippe Lambin, Emmanuel Rios-Velazquez, Ralph Leijenaar, Sara Carvalho, Ruud G.P.M. van Stiphout, Patrick Granton, Catharina M.L. Zegers, Robert Gillies, Ronald Boellard, Andre ́ Dekker, and Hugo J.W.L. Aerts. “Radiomics: Extracting more information from medical images using advanced feature analysis.” European Journal of Cancer, vol. 48: 441-446. [DOI: 10.1016/j.ejca.2011.11.036].[2] Afsaneh Jalalian, Syamsiah Mashohor, Rozi Mahmud, Babak Karasfi, M. Iqbal B. Saripan, and Abdul Rahman B. Ramli. “Foundation and Methodologies in Computer-Aided Diagnosis Systems for Breast Cancer Diagnosis.” EXCLI Journal, vol. 16:113-137. [DOI: 10.17179/excli2016-701].[3] Virendra Kumar, Yuhua Gu, Satrajit Basu, Anders Berglund, Steven A. Eschrich, Matthew B. Schabath, Kenneth Forster, Hugo J.W.L. Aertsf, Andre Dekkerf, David Fenstermacher, Dmitry B. Goldgof, Lawrence O. Hall, Philippe Lambin, Yoganand Balagurunathan, Robert A. Gatenby, and Robert J. Gillies. “Radiomics: the process and the challenges.” Magnetic Resonance Imaging, vol. 30: 1234-1248. [DOI: 10.1016/j.mri.2012.06.010][4] Sunderland and Christian. “Quantitative PET/CT Scanner Performance Characterization Based Upon the Society of Nuclear Medicine and Molecular Imaging Clinical Trials Network Oncology Clinical Simulator Phantom.” Journal of Nuclear Medicine, vol. 56: 145-152. [DOI: 10.2967/jnumed.114.148056].[5] Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. “Why Should I Trust You?: Explaining the Predictions of Any Classifier.” Association for Computing Machinery. [DOI: 10.1145/2939672.2939778].[6] Brijesh Verma, Peter McLeod, and Alan Klevansky. “Classification of benign and malignant patterns in digital mammograms for the diagnosis of breast cancer.” International Journal of Computer Applications, vol. 37: 3344-3351. [DOI: 10.1016/j.eswa.2009.10.016].[7] David L Raunig, Lisa M McShane, Gene Pennello, Constantine Gatsonis, Paul L Carson, James T Voyvodic, Richard L Wahl, Brenda F Kurland, Adam J Schwarz, Mithat Gönen, Gudrun Zahlmann, Marina Kondratovich, Kevin O'Donnell, Nicholas Petrick, Patricia E Cole, Brian Garra, Daniel C Sullivan and QIBA Technical Performance Working Group. “Quantitative Imaging Biomarkers: A Review of Statistical Methods for Technical Performance Assessment.” Stat Methods Med Res, vol. 0, 1-41. [DOI: 10.1177/0962280214537344].[8] Christie Lin, Stephanie Harmon, Tyler Bradshaw, Jens Eickhoff, Scott Perlman, Glenn Liu, and Robert Jeraj. “Response-to-repeatability of quantitative imaging features for longitudinal response assessment.” Physics in Medicine & Biology, 64. [DOI: 10.1088/1361-6560/aafa0a].[9] D. Karunanithi, Omar Alheyasat, Divya Thomas, and G. Kavitha. “Attacks on Artificial Intelligence Applications through Adversarial Image.” International Journal of Pure and Applied Mathematics, vol. 118: 4491-4495.

Хотите знать, кто виновен в том, что лента любимой соц. сети настолько релевантна вашим интересам, что вам приходится прибегать к ограничителям времени, лишь бы не залипать в неё вечно? Как всегда, информация для слушателей Подлодки доступна прямо из первых уст – к нам в гости пришёл Андрей Якушев, тимлид команды CoreML в ВК и рассказал все о том, как устроены рекомендательные системы. Мы прошлись по всему пайплайну создания и внедрения рекомендательных систем, уделив особое внимание части про машинное обучение, так что скучно точно не будет! Поддержи лучший подкаст про мобильную разработку:
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Dr. LaMotte and Dr. Wells discuss their NIJ funded research on PMI and how it can help crime scene investigators. This is their abstract submitted for the R&D Symposium: "To our knowledge an estimate of time since death is almost never accompanied by the kind of mathematically explicit probability statement that is the standard in most scientific disciplines. This has been a problem both for death investigation casework (and court testimony) and for research, because scientists have not known how to design decomposition experiments to provide adequate statistical power for postmortem interval (PMI) estimation. We have been developing methods for calculating statistical confidence limits about a PMI estimate based on either continuous quantitative or categorical data. The examples we present are from forensic entomology, but the approach is suitable for any postmortem variable. To do this we extended and adapted the time-tested statistical method of inverse prediction (IP, also called calibration) to the PMI estimation setting. Methods to produce valid p-values for this process are known for single, quantitative y and x that follow a linear regression relation and with y having constant variance. Some exist for multivariate y, but only for settings where y has constant variance. Many measurements used for PMI estimation do not fit these criteria. The current project builds on earlier work in which we developed IP methods for non-constant variance of a single, quantitative y (e.g. estimating carrion maggot age using a single size measurement, Wells and LaMotte 1995), and in which we developed the first ever method for IP based on categorical data (e.g. estimating PMI based on carrion insect succession, LaMotte and Wells 2000). One possible barrier to the adoption of these new inverse prediction methods by researchers and death investigators has been that they are not implemented in statistical software packages. In this presentation we will show how IP using categorical data can be done by simply reading a table. Concerning quantitative data we will show how inverse prediction of PMI can be performed using statistical analysis software already widely available for general linear mixed models, where the statistical theory and methodology are well-established. We will show how flexible models using polynomial splines can be fit for both the means and variance-covariance matrices, and how to use dummy variables over a grid of values of x to get the p-values required for confidence sets automatically. Attendees familiar with mixed models and their applications will be able to implement these methods in standard statistical packages. Statistical Methods for Combining Multivariate and Categorical Data in Postmortem Interval Estimation 2013‐DN‐BXK042 Lynn R. LaMotte,1 and Jeffrey D. Wells2 1Biostatistics Program, LSU School of Public Health To learn more visit www.ForensicCOE.org

Guests: Mark Glickman - Senior Lecturer on Statistics at Harvard University, Author of the Handbook of Statistical Methods and Analyses in Sports Sean Foreman - Founder of Baseball Reference See acast.com/privacy for privacy and opt-out information.

Show from 6/21/17 Guests: Mark Glickman - Senior Lecturer on Statistics at Harvard University, Author of the Handbook of Statistical Methods and Analyses in Sports Sean Foreman - Founder of Baseball Reference See acast.com/privacy for privacy and opt-out information.

Ewan Cameron and Sam Bhatt from the Nuffield Department of Population Health discuss statistical methods used to map malaria and other infectious diseases.

In this lecture, Stephen Furmanek discusses Quality Improvement projects.  This video examines different methods for displaying the results and how to determine which method is best.   Some items in this lecture may have come from the lecturer’s personal academic files or have been cited in-line or at the end of the lecture. For more information, see our citation page. Disclaimers   ©2015 LouisvilleLectures.org

Mon, 28 Jul 2014 12:00:00 +0100 https://edoc.ub.uni-muenchen.de/17562/ https://edoc.ub.uni-muenchen.de/17562/1/Duemcke_Sebastian.pdf Dümcke, Sebastian ddc:540, ddc:500, Fakultät für Chemie und Pharmazie

Cauchemez, S (Imperial College London) Monday 19 August 2013, 14:00-14:30

Tue, 1 Jan 2013 12:00:00 +0100 https://epub.ub.uni-muenchen.de/21738/1/MA_Manuilova.pdf Manuilova, Ekaterina ddc:500, Ausgewählte Abschlu

Alan Agresti discusses his experiences and views of what works well when teaching quantitative methods to undergraduate social science students. He covers what an introductory quantitative methods course should achieve, general concepts versus mathematical statistics, active learning, use of technology and what to emphasise and de-emphasise. The talk was given as part of a workshop in June 2012 at the Department of Sociology, University of Oxford, for the QMteachers project www.sociology.ox.ac.uk/qmteachers. Alan Agresti is Distinguished Professor Emeritus at the University of Florida. He has written more than 100 articles and six books, including Categorical Data Analysis, which has received more than 12,000 citations in journal articles, and Statistical Methods for the Social Sciences (with Barbara Finlay), an introductory textbook for undergraduate or graduate students

Alan Agresti discusses his experiences and views of what works well when teaching quantitative methods to undergraduate social science students. He covers what an introductory quantitative methods course should achieve, general concepts versus mathematical statistics, active learning, use of technology and what to emphasise and de-emphasise. The talk was given as part of a workshop in June 2012 at the Department of Sociology, University of Oxford, for the QMteachers project www.sociology.ox.ac.uk/qmteachers. Alan Agresti is Distinguished Professor Emeritus at the University of Florida. He has written more than 100 articles and six books, including Categorical Data Analysis, which has received more than 12,000 citations in journal articles, and Statistical Methods for the Social Sciences (with Barbara Finlay), an introductory textbook for undergraduate or graduate students

How Much Are They Holding You Back? The pervasive fantasy in business today is that you can tweak your way to success. Tweakers believe you need only “monitor your metrics” to ratchet your way to the top of the mountain. “Hold your position, then make a tiny change and click up to the next level.” Tweakers find comfort in numbers, decimal points, percentages and line graphs. Don't get me wrong, I do believe in monitoring. You cannot improve what you do not measure. But you won't see big differences in that line graph until you make some meaningful changes. Incremental change is the path to quiet evolution. Significant change unleashes noisy revolution. There are no quiet revolutions. AIn 1979, Sony put lightweight headphones on a tightly-compacted cassette tape player to create the ‘Walkman,' a worldwide hit that allowed you to take your music with you when you went walking, shopping or jogging. Sony retained a 50% market share in the U.S. for more than a decade even though their Walkman cost at least $20 more than its numerous rivals. Sony in 1990 was like Apple today; seemingly invincible. So why didn't Sony invent the iPod? Sony fell into the trap of scientific, incremental change; an eternal series of tiny improvements in the hope of making an increasingly better Walkman; a process known in Japan as “kaizen.”  30-SECOND HISTORY LESSON: To help restore Japan in the aftermath of WWII, America provided experts to assist the rebuilding of Japanese industry. A Management Training Program was developed and taught by Homer Sarasohn and Charles Protzman in 1949-50. Sarasohn later recommended W. Edwards Deming to provide further training in Statistical Methods. And thus, Japanese “kaizen” was born. Sony introduced a courageous product and it made them hugely successful. And then Sony began playing it safe. Your friends, family, staff, and customers – all the people who care about you – want you to be safe. And the safest thing you can do, they believe, is to conform to the accepted norm. This is why they will always “express their concern” when they see you stray from the straight and narrow path. But isn't “playing it safe” in business the least safe thing you can do? Sony methodically kept improving the Walkman long after they should have replaced it with an entirely new concept.  Big Success is rare because it requires audacity and courage. Or maybe I'm wrong. What do you think? Roy H. Williams

The fields of geographical epidemiology and public health surveillance have benefited from combined advances in hierarchical model building and in geographical information systems. Exploring and characterising a variety of spatial patterns of diseases at a fine geographical resolution has become possible (Banerjee, Carlin and Gelfand 2004). Insight into the sensitivity of the resulting inference to the choice of the structure of the different components of the hierarchical model has been gained through the use of simulation studies (Best, Richardson and Thomson, 2005) and numerous case studies. Baseline results on how to use the posterior distribution of relative risk estimates to detect areas of increased risks have been discussed (Richardson, Thomson, Best and Elliott, 2004) Extending hierarchical disease mapping models to models that simultaneously consider space and time and/or several diseases leads to a number of benefits in terms of interpretation and potential for detection of localised excesses. Such extension is accompanied by an increase of the complexity of the model structures that might be specified. The presentation will first outline classes of hierarchical space time models that can be used to characterise the patterns of chronic diseases. Space-time analysis of related diseases that that tease out common and specific space and time structures will be discussed next and illustrated on an example related to male and female lung cancer (Richardson, Abellan and Best, 2006). Finally, the use of space-time models to better characterise the stability of spatial patterns and to highlight atypical areas with unusual variability in their risk time pattern will be discussed and illustrated in a number of realistic scenarios. In particular, we will show how to model the space-time interactions and exploit their posterior distributions in order to classify the areas' risk pattern over time as 'predictable/ repeatable' or 'atypical/ highly variable'. References Banerjee S, Carlin B and Gelfand A (2004.) Hierarchical modelling and analysis of spatial data. Chapman and Hall, New York. Best N, Richardson S and Thompson A. (2005) A comparison of Bayesian spatial models for disease mapping. Statistical Methods in Medical Research. 14:35-59. Richardson S, Thomson A, Best N & Elliott P. Interpreting posterior relative risk estimates in disease mapping studies. Environmental Health Perspective , 2004, 112: 1016-25 S. Richardson, J-J. Abellan and N. Best. Bayesian spatio-temporal analysis of joint patterns of male and female lung cancer risks in Yorkshire (UK). Statistical Methods in Medical Research, 15: 385-407, (2006). Sylvia Richardson - Imperial College, London Bande son disponible au format mp3 Durée : 57 mn