The Effective Statistician - in association with PSI

Episode 1 In today's episode, I review the different things you need to consider in creating an effective data visualization when presenting, which is the following: Who is the audience?Level of understandingKOLResearcherStatisticiansWhat is the medium?PresentationEmailPaperHow much time do you have?Who is the presenter? Is there one at all?Understand how your visualization is used downstream References:Storytelling with data - cole nussbaumer knaflicTamara Munzners book Visualization, Analysis and DesignDatastori.esBig book of dashboardsMakeover MondayD3 gallery on githubSpecial interest group VISData visualization society Listen to this episode to learn from it, and share it with others!

Interview with Deborah Ashby Click this link to go to the homepage of this episode! Despite her having a weak position in society at the time - being a nurse and a women – she still was very influential and achieved a lot. Many statisticians feel like they are not part of key decisions and don’t have the impact, they could have. What can we learn from her in this regard? References:Link to the homepage of the conference keynote speakers Link to the home page of the conference Listen to this episode and learn more!

Interview with Stephen Pyke Click this link to go to the homepage of this episode! In today's episode, Stephen and I discuss the PSI strategy day in 2019 and focused on the following points: My own journey - statistics and beyondHoliday reading - people respond to incentivesClinical development - Our main job: minimize value destructionAddressing the challenges of low productivityProblem that needs more attentionTime - Study enrollment time is often an important source of uncertainty in project plansQuality - Detecting site quality issues quickly is criticalCost - Trial costs are more likely to increase than reduceStatistics at the interface - Partnering with other disciplines Listen and learn from this episode now!

Interview with Rachel Phillips and Mark Baillie Click this link to go to the homepage of this episode! n today's episode, we talk about the advantages of using visualizations when analyzing and communicating data. We also discuss the following points: In today's episode, we talk about the advantages of using visualizations when analyzing and communicating data. We also discuss the following points: How our organizations prioritize visualizations?What we love about visualizations?What are the principles of visualization statisticians should know about?How to create interesting, innovative, and purposeful visualizations? Listen to this episode to learn more on visualizations and share this with others who might learn from it! References: Visualization special interest group (VIS SIG)Wonderful Wednesday announcement and link to registrationGoogle team BigPictureGoogle tutorialDatastori.es episode on visualization of uncertaintyGithub D3 galleryNovartis visualization initiative and cheat-sheetStratos initiativedata visualization societyFundamentals of Data Visualization - online bookData Visualization - A practical introduction - online book

Imi Dean, a real-world data scientist at Roche with expertise in oncology and machine learning, shares insights on the impact of real-world evidence in healthcare. He discusses his journey from medical science to data science, highlighting the power of real-world data and its contrast to traditional trials. The importance of precise research questions and overcoming biases in data is emphasized, as well as the role of propensity scoring in treatment analysis. Ultimately, Imi reveals how real-world evidence can significantly enhance patient care and decision-making.

Interview with Liz Cole Click this link to go to the homepage of this episode! Listen to our conversation and understand: What is content marketing?Why is this relevant for statisticians working in CROs, pharma or as consultants? How can content help CROs or consultants win more business? How can content help you attract candidates to your team and stand out as an employer?How can content help to boost your personal profile? What are the barriers that stop people from implementing content marketing and how we overcome these barriers?  What actions do you recommend should statisticians start with?What resources do you recommend helping with content creation and content marketing?  Listen to this episode and learn from it!

Interview with Karim Malki Click this link to go to the homepage of this episode! Karim shares about his career, his roles, and different approaches and methods. We also discuss the following points: Machine learningAIPredictive analyticsData scienceDifferent statistical methods and limitationsDifferent tools and applications Statistical innovation Listen to this episode, learn from it, and share it with others!

Interview with Christopher Bergh Click this link to go to the homepage of this episode! The data ops manifesto can be found here and lists these 18 points - some of which are discussed in more detail in this episode. Data Ops Principles: Continually satisfy your customerValue working analyticsEmbrace changeIt's a team sportDaily interactionsSelf-organizeReduce heroismReflectAnalytics is codeOrchestrateMake it reproducibleDisposable environmentsSimplicityAnalytics is manufacturingQuality is paramountMonitor quality and performanceReuseImprove cycle times

Click this link to go to the homepage of the post. The 6 behaviours we’re speaking about are: Being confidently relaxedBeing decisiveBeing knowledgeableBeing friendlyBeing curiousBeing vulnerable Here’s the link to the TED talk by Brene Brown, which was watched already over 44 million times (status November 2019): https://www.ted.com/talks/brene_brown_on_vulnerability?language=en#t-68179 Listen to this episode and share it with others who might learn from it!

Interview with Andy Grieve Click this link to go to the homepage of this episode! Today's interview is surely beneficial to everybody. Andy Grieve has a lot of experiences across the industry as academia and other senior roles. We also talk about the following points: When did you realize for yourself, that statisticians should and can play a bigger role?How would the health sector look like, if statisticians would have considerably more influence - e.g. if all the pharma companies would have something like a chief statistical officer?What are the factors, that play in favor of statisticians gaining more influence?What do you see as the biggest barriers for statisticians gaining more influence?What would you recommend to other statisticians to achieve a bigger influence?Do you have different recommendations for statisticians earlier or later in the career? Listen to this interview and listen to others who can learn from it!