The Effective Statistician - in association with PSI

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!

Click this link to go to the homepage of the post. We specifically address the following points: Reasons for IC The classical Bucher approach vs matching adjusted indirect comparisons (MAIC)How to incorporated meta-analysesDifferent network-meta-analyses approaches (NMA): Bayes vs Frequentistsystematic literature reviews (SLR)Cochrane handbookTools VisualizationsBias Precision vs biasPre-specified vs post-hocSecondary vs primary endpointsPower of ICPublish detailed analysesFurther references:PRISMA http://prisma-statement.org/PRISMAStatement/Earlier podcast episode:Network meta-analyses: why, what, and how Listen to this episode and know more about Indirect Comparison now!

Click this link to go to the homepage of this post! During this episode, we will review some of the highlights from this year. It’ll help you to remember a couple of lessons learned or inspire you to listen to some episodes (again). We will talk about the following: LeadershipThe look beyond pharmaData scienceBenefit-RiskCareerNonparametricVisualizationProductivityAnd an outlook into 2020 Otherwise, enjoy your Christmas break and we’re taking a week off on New year's eve and start again on the seventh of January. Listen to this episode and become an effective statistician! Merry Christmas!

Application process and interview tips with Rhian Jacob and Rachael Loftus Click here to get to the homepage of episode 5

A widespread but difficult to treat disease Click this link to go to the homepage of the post. I once reviewed tables for a randomized study and noticed several comments about testing the baseline characteristics. The commenters were arguing which test would be best to test for the differences between the 2 randomized groups at baseline. This made my first angry about the wasted time and then curious about the reasons, statisticians still do this. In today's episode, Benjamin and I discuss some backgrounds for baseline testing in randomized studies. Listen to this episode, share it with others who might learn from it, and be an effective statistician!