Description:
This short course will bring to life the foundational concepts for becoming the ideal supervisor.
Attendees will gain a deeper understanding of the essential leadership competencies that will empower
them to grow a mentee or direct report, thus enabling them, in turn, to reach their full potential as well.
The rewards of this development will cascade through the organization. Participants will learn and
understand the expectations and behaviors necessary for becoming a supervisor for whom employees
will want to work, increasing their team productivity through an elevated level of engagement.
Engagement and fulfillment of employees is achievable when they feel motivated, are challenged to be
the best they can be and are able to accomplish more than they thought they could. This course will
consist of lecture, videos, and interactive panel discussions where participants will hear from seasoned
and successful leaders about how they have learned from their experiences and developed tips and
tricks for growing their supervisory skill set. Finally, participants will learn how to measure the right
outcomes for enabling sustained growth in this dimension. It is said that employees do not leave
companies, they leave supervisors. While many other leadership courses provide advice to statisticians,
statistical analysts, and data scientists on how to be effective leaders, this course focuses on the critical
role supervisors/professors/advisors play in their employees’ journeys to becoming strong leaders as
well as individuals who propose and drive innovative ideas/solutions and effectively implement them.
Strong supervisors, model desired employee behaviors, act as sponsors as well as mentors, contribute
to their employees’ career satisfaction, support their employees’ work/life balance and generally retain
good employees. If you are currently leading a team, managing a group, or considering a supervisory
role, this course will help you be more effective.
This short course is being offered in collaboration with the Leadership in Practice Committee (LiPCom)
of the Biopharmaceutical section of the ASA.
| Chapter | Topic |
|---|---|
| Preface | Real-world evidence and digital innovation to combat noncommunicable diseases |
| 1 | Real World Evidence Generation |
| 2 | Applications of RWE for Regulatory Uses |
| 3 | Ethics & Bioethics |
| 3 | Real- World Data, Big Data and Artificial Intelligence: Recent Development and Emerging Trends in the European Union |
| 4 | Patient centricity and Precision Medicine |
| 5 | Health Information Technology |
| 6 | Digital Health Technologies and Innovations |
| 7 | Economic Analysis and Outcome Assessment |
| 8 | Partnerships and Collaborations |
| 9 | Global Perspective: China Big Data Collaboration to Improve Patient Care |
| 10 | The Future of Patient-Centric Data-Driven Healthcare |
Abstract:
Incorporating real-world data (RWD) in regulatory decision-making demands much more than "mixing" RWD with investigational clinical trial data. The RWD has to undergo appropriate analysis for deriving the right real-world evidence (RWE). Moreover, such analysis has to be integrated with the design and analysis of the investigational study for regulatory decision-making. The standard clinical trial toolbox does not offer ready solutions for incorporating RWD. Therefore, there is an unmet need for sound clinical trial design and analysis for leveraging RWE in clinical evaluations.
In this course, the instructor will cover a series of methods they have developed for leveraging real-world data in clinical trial design and analysis. Noteworthy, these work has been recognized by the FDA and received The FDA CDRH Excellence in Scientific Research Award-EXTERNAL EVIDENCE METHODS RESEARCH (GROUP) and The FDA Scientific Achievement Award-EXCELLENCE IN ANALYTICAL SCIENCE (GROUP) for extraordinary achievements in the timely development and active promotion of novel statistical methods for leveraging real-world evidence to support regulatory decision-making.
In Part I of the course, the instructor will introduce a method for proposing performance goals—numerical target values pertaining to effectiveness or safety endpoints in single-arm medical product clinical studies—by leveraging RWE. The method applies entropy balancing to address possible patient dissimilarities between the study’s target patient population and existing real-world patients and can take into account operation differences between clinical studies and real-world clinical practice.
In Part II of the course, the instructor will introduce a method that extends the Bayesian power prior approach for a single-arm study to leverage external RWD. The method uses propensity score methodology to pre-select a subset of RWD patients that are similar to those in the current study in terms of covariates, and to stratify the selected patients together with those in the current study into more homogeneous strata. The power prior approach is then applied in each stratum to obtain stratum-specific posterior distributions, which are combined to complete the Bayesian inference for the parameters of interest.
In Part III of the course, the instructor will describe an R package, psrwe, that implements a PS-integrated power prior (PSPP) method, a PS-integrated composite likelihood (PSCL) method, and a PS-integrated weighted Kaplan-Meier estimation (PSKM) method for the methods in Part II. Illustrative examples are provided to demonstrate each of the approaches.