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The Effective Statistician - in association with PSI, 485 episodes. Page 1 of 5.

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Title
1

Move Beyond Excel with R for Health Technology Assessment

2

Turn Data into Decisions by Putting People First

3

Better Dose Decisions and a Culture of Statistical Innovation

4

Five Lessons for a Meaningful Career in Statistics

5

Integrated Evidence Planning That Connects Drug Development to Patient Needs

6

How to Make Statistics Click with Schools Outreach and Interactive Data Visualization

7

Will AI Replace Statisticians? The Future of Statistics in Pharma

8

How to Build an Effective Patient-Reported Outcome Strategy for Clinical Trials

9

How the R Consortium Is Transforming Regulatory Submissions and AI in Clinical Trials

10

Project Optimus and what you need to know about it

11

Unlocking Growth: The Power of Coaching and Mentoring for Statisticians

12

Understanding and Mitigating Endpoint Bias in External Control Arms

13

The Future of Statistical Methodology in Drug Development

14

Rethinking Programming Validation and Traceability in Clinical Trials

15

How Applied Improvisation Develops and Reinforces Interpersonal Skills

16

What does a clinical lead expect from a statistician?

17

Measuring Trust – A Key to Effective Leadership

18

Choosing and Interpreting PROs and COAs – A Guide for Clinical Trial Statisticians

19

How Statisticians Can Build Trust and Lead with Emotional Intelligence

20

The Evolving Role of Generative AI in Pharma

21

Kicking Off 2026: Gratitude and What’s Ahead

22

Why to present better and how as a statistician

23

External control arms - how to get to a good one

24

Top 9: Non-parametric analyses - much more than just the Wilcoxon test!

25

How to communicate results from adaptive studies simple, but still correct

26

Introduction to adaptive designs and ICH E20

27

Leadership, Influence & Presenting: Human Skills That Make Statisticians Effective

28

Top 8: The Single Arm Studies and What are the Alternatives?

29

Top 7: How to work with a physician within Pharma to become a valuable partner

30

Top 6: What is EU HTA and why should statisticians care?

31

Top 5: The analysis of adverse events done right

32

Replay: R vs SAS - which is the better tool in pharmaceutical research

33

Replay: The Chimp Paradox

34

Replay: Is data science something for you?

35

Replay: Things we would like to have known before we started with RWE

36

AI and Statistics Start-ups: Opportunities and Challenges

37

Combining RCT and RWD – applications in rare diseases and practical recommendations

38

Real-World Evidence vs. Randomized Trials: Can We Emulate Accuracy?

39

The Power of Networking

40

Inclusion for All: Rethinking Success, Neurodiversity, and Accessibility in the Workplace

41

Daily updated systematic literature reviews - the future of oncology

42

A novel approach for finding predictive biomarkers

43

A Webinar Recording: Redefining the Role of Statisticians in a World of Real-World Data and AI

44

RWE and JCA - how do they go together

45

Why and how you can promote more students become statisticians

46

Clarifying confusions around interim, primary, final, and other analyses in clinical trial

47

Reimagining Clinical Trials with Synthetic Data and Digital Twins

48

Working in an english work environment as a non-native speaker

49

R-shiny - how to set it up effectively and avoid common mistakes

50

3 personal stories of how soft skills have helped me as a statistician

51

R-packages - best practices and useful tools

52

Delegating programming tasks - how SOPs help and hinder

53

Statistics and Market access - from foes to friends

54

Beyond logic - how to convince others of your ideas

55

P-value and confidence intervals - the good, the bad, and the ugly

56

Building the Influence as a Statistician in a Clinical Trial Team

57

Effective self-management and taking care of your mental health

58

Taming AI for Biostatistics: Darko Medin on Bio AI Works & Reliable AI Models

59

Slow Productivity

60

Behind the Scenes of Writing a Book About Casual Inference

61

What Is The MDLC and How It Will Help You?

62

Endless To-Do Lists

63

Struggling With A Full Calendar?

64

Breaking Free from the Beliefs Keeping You Stuck in Long Hours

65

What Are GAMS and How Can You Leverage Them?

66

Why Becoming a Long-Haul Leader Matters

67

Negotiation for Statisticians: Advocating for Yourself and Your Work

68

Trust

69

Behind the Pages: How I Wrote 'How to Be an Effective Statistician'

70

Imposter Syndrome

71

Learn, Do, Reflect, and Start Again! How to Become a Better Leader

72

2025 Lifestyle Planning

73

Early Development and The Dose Selection in the Immune-oncology

74

Sharpen The Saw

75

Why is it important to measure exposure to digital health interventions and how to tackle the challenge

76

Why Do Clinical Trials Impact the Climate Heavily and What We Can Do About It?

77

Introduction to Dealing with Non-proportional Hazards

78

How Real Leaders Help Their People Grow Professionally and As a Person

79

Clinical Development of Gene Therapies

80

Statistical Topics in Gene Therapy

81

Clinical Development Plans

82

The Most Important Things You Need to Know About AI And ML

83

Can Statisticians Contribute to Moving Out of The Statistics Function?

84

How Notion Can Help You With Productivity

85

Statistical Software Engineering

86

Prediction Modeling

87

How AI Can Help Us with Our Day-to-day Work! Experiences with Copilot

88

Better Delivery of Presentations

89

The Best Learnings From One of The Most Influential Statisticians

90

Crafting Better Slides

91

The Role of AI in Clinical Development

92

Three Areas You Must Address in Your Presentation

93

Bridging IT and Statistics: A Guide for Statisticians

94

5 Questions to Ask Yourself Before Opening PowerPoint

95

Overcoming Challenges of Working Effectively Together: Pharma And Small Providers

96

Digital Transformation And Innovation (Part 2)

97

Digital Transformation And Innovation (Part 1)

98

The Role of Coaching For Your Career And Success

99

Benefit-Risk

100

How And When To Leverage Integrated Evidence Plans (Part 2)