We leverage Real World Evidence in many different areas and there's are many different things that can go wrong with this.

In today's episode, Rachel and I will discuss the things that we would like to have known before we started working with RWE so stay tuned to avoid mistakes we made in the past.

How do you start your week?
Do you just rely on yourself or do you use some tools to organise yourself?

learned a lot of productivity hacks from Michael Hyatt. I've listened a lot to his podcasts and I used some of his recommended tools to make my plans work such as the Full Focus Planner which is kind of a calendar and a to-do list together with goal setting tips and more.

There's a lot of community charity works we can all do but there is not a lot of work where we can maximise our impact by using our specific skills as programmers, as statisticians, and as data scientists.

What kind of leader do you want to be?
What are the leadership principles?

I learned about the concept of leadership principles from Gary Sullivan and it helped me to better understand what kind of leader I want to be.

I've been working on RWE most of my career and the clinical trial data for a while now. And 20 years ago, we would have never thought about merging them together but there's a lot of opportunities if we do this.

The recent advances in analytical methods for combining evidence from RCTs and non-RCTs, and the development of new frameworks for the inclusion of RWE in HTA have provided a greater insight on how issues around RWE uncertainty can be dealt with when estimating treatment effects for new technologies.

This year's PSI conference will be face to face again.

I'm really excited for this. This is the best conference for statisticians that you can attend.

If you are working in health care industry, there is no other conference like this. Attend this conference and meet friends and create new social connections! And you submitting a poster for the conference would be best!

During my early years, I was overwhelmed by so many trainings, SOPs, and processes. Only after my later years I learned what quality is really about. Gary fundamentally helped me and many others have a very different and more insightful/helpful/easy perspective about this topic.

What are typical data science methods that we are not yet using enough?
Which areas will become important in the future in terms of data science?

Data visualisation is one of my favorite topics and today, I'll be interviewing someone who has helped me understand about data visualisation for years.

Is it possible to align business focus and self development at the same time?
How can we create clear department or company goals or relate these to our goals?