How does patient adherence affect the results of clinical trials?
Are there ways to improve patient adherence to medication and treatments not only in clinical trials but also in real-life situations?
What is the future of the value of clinical trials in terms of inclusive technological innovations?

Clinical trials are designed to test whether a medication would be best for the target population. While these approaches are considered critical to healthcare development strategies, their value for applicability still remains dependent on how the participants cooperate with the process of treatment which involves their attitude towards adherence to the trial.

In this podcast, join me and Lina as we talk about the most effective ways to get patients to adhere to treatments they are offered in clinical trials. With her background in psychology, Lena’s insight is on point especially when it comes to determining the role of human behavior in terms of determining the applicability of the results garnered from clinical trials.

While there are several problems that arise from the lack of patient adherence, this podcast highlights some of the most effective ways that are available today for researchers and statisticians to consider when handling patient-centric clinical trials-therefore ensuring a high rate of patient adherence.

Here are some of the important learnings you can get from this episode:

Why would a person working under the rules and purpose of statistics for 30 years want something more than what she has been an expert with for many years?
Do you want something more out of your statistician background?
What could statisticians offer outside of the field of statistics in healthcare?
How does leadership in statistics bring you to embrace more exciting journey outside of statistics?
What does passion have to do with making statistics work for statisticians trying out new avenues of opportunities?

No matter how boring it may seem to others, statistics is certainly an exciting field for those who enjoy numbers and the analysis of the data collected from samples that are worked on. From the point of collecting data to studying what they mean, statisticians are certainly considered leaders who are able to utilize data to drive change for the benefit of the patients and the general public. But there is more to statistics than simply dealing with the numbers. It gives statisticians a direct connection with the issues giving them a chance to see things more clearly. In this podcast episode on steeping outside of statistics, Alex sits down to connect with Liz Thompson who has a strong 30-year background in statistics to understand why statisticians embrace the option of stepping outside of their expertise in the hope of bringing more value to what they do.

In this discussion, Liz presents some of the most distinct reasons as to why a statistician may want to embrace other options of growth in the healthcare industry as an advocate for distinct purposes.

Here are the bullets of lessons you will get from this TES podcast episode:

Why should statisticians aim for becoming good leaders?
How do you invest in yourself to become a good leader?
How does perseverance change the path taken by a leader in the field of statistics?

Gary Sullivan and I team up together to discuss the importance of leadership among statisticians. Understanding the basic factors of leadership and how it makes an effective statistician is important for every professional in the field of statistics to master.

Statisticians do more than just collect data and analyze the information they have collected. In many ways, statisticians, being the first-hand handlers of the data, also find solutions to problems at a much earlier time compared to other specialists in specific fields. Hence, beyond just relaying the facts, statisticians are also expected to provide first-hand solutions that they think may be capable of resolving the issues they find critical as they go through the data they have collected.

This is where leadership comes in. In our discussion, Gary and I exchange thoughts, and experiences and learned lessons through the years and how they can be applied in actual situations that statisticians face at work.

Here are some of the important realizations that we gathered during our conversation:

Publications are part of the bigger communication plan which also covers PR, scientific dialog, promotion, training, and internal communications. Writing publications should follow an overall strategy for the compound or therapeutic area. They are necessary for further steps like having promotional material, HTA submissions, scientific/medical dialog, etc.
They should not be an afterthought or seen as a pure incentive.

In this episode, Jenny Devenport and I talk about these publications as critical part of the scientific process We also cover how creating these helps establishing credibility with various stakeholders.

We also discuss the following points:

What is a secondary paper?
What is its typical content?
Where is the content coming from?
A secondary paper is a paper you do once you are done with your primary paper. I've been working on this for a long time and always have fun doing it.

Together with Benjamin, we discuss the following points:

Feeling comfortable does not lead to growth.

But why do most people stay in their comfort zone?
Are your own expectations holding you back?

In this episode, I am talking about the reasons why people stay in their comfort zones and always choose the “safe side”. And why choosing this hinders you from your personal and career growth.

I am really passionate about leadership and Claude Petit is also passionate about building leaders and being an effective leader. Together, we will share with you how to become a statistical leader and how to build such an organization.

So stay tuned!

What is a pharmacometrician?
What does the training of a pharmacometrician look like?

In my career, my experience of working with pharmacometricians was very limited because I spent most of my time working more on the later phases; and the contact with people in that space of the industry is not that big, that's why I found this episode really helpful. If you don't have a clue what they do then stay tuned for this episode.

What can go wrong with network meta-analyses?

You often have multiple endpoints but these are not collected consistently across all the studies included in the NMA. How do you deal with this?

Even with the same variables assessed over studies, they might be collected at different time points. This often has an effect on both efficacy and safety results. What do you do?

Your placebo treatment might change over time and consequently your placebo response. Often placebo plays a crucial part in your network, but can you really pool all your placebo arms? The patient population differs between studies and hence between treatments. How do you control this bias?

The pool of relevant treatments differs across countries when using network meta-analyses for HTA submissions. Should you adapt your NMA for each country's submission?

You’re not submitting all your HTA dossiers at once and thus the literature search needs to be updated repeatedly hence you input data into the NMA. How big are the differences and how long will it take to update everything (and what will it cost?)?

In today's episode, I am the interviewee, and Jenny is the one asking the questions. We are talking about how training in launch and commercialization can be a super weapon, what its role is, and how can this be developed and administered for non-statisticians.

We also talk about the following interesting points: