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Data Sciences in Pharma - Patient Centered Outcomes Research

Susanne Clinch

4.5

Based on 21 ratings

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Susanne Clinch

The course is targeted toward people who are interested in how patient experience data and clinical outcome assessment (COA) data can be used as evidence across drug development, in the pharmaceutical industry. By the end of the course you will better understand how this data is collected and analysed to evidence how patients feel, function or survive in the context of a clinical trial. More specifically, the course will cover: i) a background to COAs; ii) a background to patient experience data; iii) how to select, develop/modify and validate COAs using qualitative data (a) and psychometrics (b); iv) interpreting data on a COA; v) measuring treatment related tolerability via patient reported outcomes; vi) Common COA data outputs.

No experience in the pharmaceutical industry is needed for this course, but it is beneficial. This is an introductory course so an interest in qualitative and quantitative data and some basic knowledge in data analytics and statistics will be helpful for some lessons but is not required.

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What's inside

Syllabus

An introduction to clinical outcome assessments and patient experience data

This module will cover a background of clinical outcome assessments (COAs) and patient experience data, what they are and what they consist of, how this data fits into drug development and the importance of this data as evidence to the external environement (such as health authorities) across the drug lifecycle

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Read about what's good

what should give you pause

and possible dealbreakers

The course is tailored towards informing how health authorities evaluate a COA

Taught by instructors who are recognized for their work in this field

Provides a foundation on patient experience data and clinical outcome assessments

Covers qualitative and quantitative methods used in COA selections

Explores the use of patient reported outcomes in measuring treatment related tolerability

Requires some basic knowledge in data analytics, but no experience in the pharmaceutical industry

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Reviews summary

Essential introduction to pharma patient outcomes

According to students, this course offers a largely positive and well-structured introduction to patient-centered outcomes research and COA data in the pharmaceutical industry. Learners highlight the instructor's clear explanations and the relevance of the content to real-world drug development. Many found the modules on qualitative and quantitative research, as well as the PRO-CTCAE section, particularly insightful. While it serves as an excellent foundational course for those new to the field, some reviewers with stronger data science or statistical backgrounds felt that the quantitative sections were too basic and desired more advanced case studies or hands-on activities.

Perfectly suited for newcomers seeking an accessible introduction.

"It's a great foundational course for anyone new to this specific area..."

"The course does a good job of explaining complex topics in an accessible manner."

"It's more suited for complete beginners to data science in pharma."

Instructors explain complex topics with clarity and expertise.

"The instructor's explanations are clear and engaging."

"Highly recommend this course! It demystifies COAs and patient experience data beautifully. The instructor is very knowledgeable and communicates clearly."

"This course clarified so many concepts around COAs that I've encountered in my work... The instructor's expertise shines through."

A comprehensive and well-structured introduction to COAs.

"This course is incredibly well-structured and provides a comprehensive overview of COAs and patient experience data in pharma."

"Excellent course! As someone transitioning into pharma, this course provided me with the essential understanding of COAs and their importance in drug development."

"A well-organized course that introduces the crucial role of COAs. The content is relevant and timely."

Too basic in statistics and lacks advanced hands-on application.

"I felt some parts, especially on psychometrics, could have been more in-depth for those with a stronger data science background."

"For someone with a strong background in statistics, the quantitative section was very basic. I hoped for more advanced case studies."

"I came to this course expecting to learn more about advanced data analysis techniques specific to pharma outcomes... The statistical parts were too basic for me."

"I was hoping for more hands-on examples of data analysis, but it focuses more on the theoretical and conceptual aspects..."

"I think it needs more practical exercises. It's very theory-heavy."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Data Sciences in Pharma - Patient Centered Outcomes Research with these activities:

Review Key Concepts and Definitions

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This review will help you build a solid foundation for understanding the concepts covered in the course.

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Review the course syllabus and identify key concepts and definitions.
Create a glossary of terms and definitions.
Read relevant sections of the textbook or other introductory materials.

Refresher on Statistical Methods

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Brush up on statistical methods to strengthen your foundation knowledge in this course. Topics like data analysis, hypothesis testing, and regression models can be found in most introductory statistics textbooks.

Browse courses on Statistical Methods

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Review fundamental statistical concepts: data types, sampling, probability distributions, and descriptive statistics.
Study the principles of hypothesis testing: null and alternative hypotheses, errors, type I and type II, and significance levels.
Practice data analysis techniques: summarizing, organizing, and interpreting data.

Read 'Fundamentals of Clinical Trials' by Friedman, Furberg, DeMets

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Gain a comprehensive understanding of clinical trial design and conduct.

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Read the chapters relevant to the course material.
Summarize key concepts and methodologies.
Apply the knowledge gained to the context of clinical outcome assessments.

12 other activities

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Background research on Patient Reported Outcomes (PROs)

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Review foundational concepts to ensure a strong base before embarking on the course.

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Review journal articles on the development and use of PROs.
Explore the FDA website for guidance documents on PROs.
Attend a webinar or online presentation on the use of PROs in clinical trials.

Understand Psychometric Properties

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These tutorials will provide you with the necessary background in psychometrics to evaluate the validity of COAs.

Browse courses on Psychometrics

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Find online tutorials or courses on psychometrics.
Complete the tutorials and practice exercises.
Apply psychometric principles to evaluate COAs in the context of clinical trials.

Form a study group

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Foster collaboration, active recall, and deeper understanding of course material with peers.

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Find classmates with complementary skills and interests.
Establish a regular meeting schedule and study plan.
Take turns leading discussions and sharing insights.
Collaborate on practice problems and assignments.
Evaluate each other's understanding through quizzes or mock presentations.

Develop a COA Selection Plan

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This project will give you hands-on experience in selecting and justifying COAs for a specific clinical trial.

Browse courses on Clinical Trial Design

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Identify a clinical trial scenario and define the research question.
Review available COAs and select the most appropriate ones.
Develop a plan for collecting and analyzing COA data.
Create a presentation or report that outlines your plan.

Explore online tutorials on psychometrics

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Enhance knowledge of psychometric principles for evaluating clinical outcome assessments.

Browse courses on Psychometrics

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Identify reputable platforms or organizations offering psychometrics tutorials.
Select tutorials that align with the course content and your skill level.
Complete the tutorials at your own pace, taking notes and practicing examples.
Seek clarification from the course instructor or online forums if needed.

Practice Interpreting COA Data

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These drills will help you develop the skills needed to interpret and analyze COA data.

Browse courses on Statistical Analysis

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Find practice datasets with COA data.
Use statistical software to analyze the data and interpret the results.
Compare your results with those of experts in the field.

Practice interpreting clinical outcome assessment data

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Develop proficiency in interpreting and analyzing clinical outcome assessment data by engaging in targeted practice exercises.

Browse courses on Data Interpretation

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Find practice datasets or case studies related to clinical trials.
Apply the concepts and methods learned in the course to analyze and interpret the data.
Compare your interpretations with expert opinions or published literature.
Repeat the process with different datasets to improve your skills.

Participate in a COA Design Challenge

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This competition will challenge you to design and validate a COA that meets specific criteria.

Browse courses on Clinical Research

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Find a COA design challenge or competition.
Form a team and develop a COA design proposal.
Collect and analyze data to validate your COA design.
Submit your proposal and compete against other teams.

Develop a patient experience data collection plan

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Apply course concepts to create a practical plan for gathering patient experience data.

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Identify the specific patient population of interest.
Determine the objectives of the data collection.
Select appropriate data collection methods (e.g., surveys, interviews, focus groups).
Develop a detailed plan for implementing the data collection.
Pilot test the data collection plan and make necessary adjustments.

Mentor Junior Researchers

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Mentoring others can help you solidify your understanding of the course material and develop your leadership skills.

Browse courses on Mentoring

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Identify junior researchers who are interested in learning about COAs.
Provide guidance and support on COA selection, validation, and analysis.
Encourage them to participate in research projects and conferences.

Attend a workshop on clinical outcome assessments

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Engage with experts and practitioners in the field to enhance understanding and gain practical insights.

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Identify industry-recognized workshops aligned with the course content.
Register for the workshop and prepare for active participation.
Attend the workshop, take notes, and engage in discussions.
Follow up with experts or attendees to clarify concepts or explore further opportunities.

Contribute to an open-source COA software project

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Gain practical experience in working with COA data and contribute to the development of open-source tools.

Browse courses on Open-Source Software

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Identify open-source projects related to COAs and patient experience data.
Review the project documentation and codebase.
Identify a specific area where you can contribute your skills.
Communicate with the project maintainers and propose your contribution.
Implement your contribution and submit a pull request.

Career center

Learners who complete Data Sciences in Pharma - Patient Centered Outcomes Research will develop knowledge and skills that may be useful to these careers:

Reading list

We've selected 43 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Data Sciences in Pharma - Patient Centered Outcomes Research.

Handbook of Multicultural Assessment

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This comprehensive handbook covers all aspects of psychological assessment, including psychometrics, test construction, administration, and interpretation. It valuable resource for researchers and practitioners who use psychological tests in their work.

Handbook of Multicultural Assessment: Clinical,...

Kindle Edition

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Basics of Qualitative Research

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Classic text on grounded theory, a qualitative research method that is used to develop theory from data. It must-read for anyone interested in learning more about grounded theory.

Data Sciences in Pharma - Patient Centered Outcomes Research

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What's inside

Syllabus

Traffic lights

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Reviews summary

Essential introduction to pharma patient outcomes

Activities

Career center

Reading list

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