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Clinical Research & Real-World Evidence (RWE) Professional
Rating: 2.8 out of 5(4 ratings)
26 students

Clinical Research & Real-World Evidence (RWE) Professional

Clinical Research & Real-World Evidence (RWE) Training with SAS, R, CDISC, EDC Systems, and Regulatory Guidelines
Created byJeet Ghosh (BRIT,DRD,CMS,DITA)
Last updated 7/2025
English

What you'll learn

  • Distinguish the objectives, advantages, and limitations of Phase I–IV clinical trials, pragmatic trials, and observational studies.
  • Configure and utilize leading electronic data capture platforms and apply CDISC standards to structure, clean, and audit clinical datasets.
  • Identify, curate, and critically evaluate real-world data from electronic medical records, insurance claims, patient registries, and wearables.
  • Develop and execute retrospective and prospective RWE study protocols, including control cohort definition and endpoint selection.
  • Apply advanced methods such as propensity score matching, inverse probability weighting, and sensitivity analyses to minimize confounding.
  • Conduct univariate, multivariate, and survival analyses in SAS and R, and interpret hazard ratios, odds ratios, and confidence intervals.
  • Build interactive dashboards in Power BI or Tableau and craft clear reports tailored to regulatory agencies, payers, and academia.
  • Prepare RWE packages for label expansions, post-marketing safety updates, and health-economic value dossiers under FDA, EMA, and payer frameworks.
  • Implement HIPAA, GDPR, and global data-privacy best practices, including informed consent management and data de-identification.
  • Integrate pharmacovigilance processes with RWE by designing signal-detection algorithms and risk-minimization plans using tools like Argus Safety.
  • Coordinate cross-functional RWE projects by developing project plans, timelines, budgets, and deliverables across clinical, biostatistics, HEOR, regulatory .
  • Execute an end-to-end capstone RWE study—from protocol drafting and data extraction to analysis, visualization, and a defendable regulatory brief.

Course content

2 sections • 29 lectures • 3h 17m total length

  • Lesson 1: Foundations of Clinical Research & Evidence Hierarchy11:24

    In this lesson you will learn :


    1. Overview of drug and device Development

    2. Hierarchy of evidence: RCTs vs. observational studies vs. RWE

    3. Key stakeholders and roles in clinical research

  • Lesson 2: Clinical Trial Phases, Designs & Protocol Essentials10:05

    In this lesson you will learn :


    1. Phase I–IV objectives, endpoints, and sample-size considerations

    2. Pragmatic and adaptive trial designs

    3. Writing robust clinical trial protocols

  • Lesson 3: Global Regulatory Frameworks & Ethical Oversight11:05

    In this lesson you will learn :


    1. ICH-GCP, FDA, EMA, and Schedule Y guidelines

    2. Institutional Review Boards/Ethics Committees process

    3. Informed consent, risk–benefit assessment, and auditing

  • Lesson 4: Clinical Data Management & CDISC Standards11:04

    In this lesson you will learn :


    1. CDASH, SDTM, ADaM models for data structuring

    2. Data cleaning plans, query management, and audit trails

    3. Version control and metadata documentation

  • Lesson 5: Electronic Data Capture (EDC) Systems10:16

    In this lesson you will learn :


    1. Configuration and validation of REDCap, OpenClinica, Medrio

    2. User access, role-based permissions, and system testing

    3. Real-time data entry, discrepancy resolution, and exports

  • Lesson 6: Real-World Data (RWD) Sources & Quality Assurance9:30

    In this lesson you will learn :


    1. EMRs/EHRs, claims databases, patient registries, and wearables

    2. Data provenance, completeness, and representativeness checks

    3. Techniques for cleaning and standardizing heterogeneous RWD

  • Lesson 7: RWE Study Design & Protocol Development10:13

    In this lesson you will learn :


    1. Retrospective cohort, case–control, and prospective registry studies

    2. Endpoint selection, comparator/control definitions, and feasibility

    3. Study protocol components and statistical analysis plans

  • Lesson 8: Advanced Methods for Confounding Control9:54

    In this lesson you will learn :


    1. Propensity score matching, weighting, and stratification

    2. Instrumental variables, marginal structural models

    3. Sensitivity and subgroup analyses to test robustness

  • Lesson 9: Biostatistics for RWE: SAS & R Practical Applications12:25

    In this lesson you will learn :


    1. Importing and managing datasets in SAS and R

    2. Performing univariate, multivariate, survival, and time-to-event analyses

    3. Interpreting hazard ratios, odds ratios, confidence intervals

  • Lesson 10: Data Visualization & Communication9:31

    In this lesson you will learn :


    1. Dashboard creation with Power BI and Tableau

    2. Best practices for visualizing time-series, survival curves, and forest plots

    3. Crafting executive summaries, slide decks, and journal-style reports

  • Lesson 11: RWE in Regulatory Submission & Market Access13:05

    In this lesson you will learn :


    1. Preparing RWE packages for label expansion and safety updates

    2. Health-economic modeling and value dossiers for payers

    3. FDA’s RWE framework, EMA’s adaptive pathways, HTA body requirements

  • Lesson 12: Ethics, Privacy & Compliance in RWE14:42

    In this lesson you will learn :


    1. HIPAA, GDPR, and global data-privacy regulations

    2. De-identification, pseudonymization, and informed consent for RWD

    3. Data governance, security audits, and breach response

  • Lesson 13: Pharmacovigilance & Safety Surveillance Integration13:21

    In this lesson you will learn :


    1. Signal detection methods: disproportionality analysis, machine learning

    2. Using Argus Safety/Oracle for adverse event reporting

    3. Risk-minimization plans and safety communication strategies

  • Lesson 14: Cross-Functional Project Management & Leadership13:59

    In this lesson you will learn :


    1. Agile and Waterfall approaches for RWE studies

    2. Budgeting, timelines, resource allocation, and vendor management

    3. Stakeholder communication: clinical, biostatistics, HEOR, regulatory, IT

Requirements

  • A bachelor’s degree in life sciences, pharmacy, medicine, public health, or a related field.
  • Basic understanding of clinical research principles and terminology.
  • Introductory knowledge of biostatistics and epidemiology.
  • Familiarity with spreadsheet software (e.g., Excel) and willingness to learn SAS or R.
  • Comfortable using web-based platforms and electronic data capture systems.
  • Proficiency in English for reading scientific literature and writing reports.
  • Reliable internet access and a computer capable of running statistical software.
  • Commitment to dedicating 5–7 hours per week over an 8-week blended schedule.

Description

Clinical Research & Real-World Evidence (RWE) Professional


Unlock the power of real-world data and revolutionize evidence-based healthcare decisions. This internationally benchmarked course blends the rigor of clinical research with the innovation of real-world evidence, offering in-depth mastery from study design to regulatory-grade analysis.

Whether you’re a healthcare professional, researcher, or data scientist, this course empowers you to navigate the complexities of global clinical trials and RWE generation with hands-on experience in SAS, R, CDISC standards, EDC systems, Power BI, and more.


What You’ll Learn inside This Course:


  • Fundamentals of clinical research and the hierarchy of evidence

  • Comprehensive understanding of clinical trial phases and adaptive designs

  • Global regulatory frameworks: ICH-GCP, FDA, EMA, and Indian Schedule Y

  • Clinical data management using CDASH, SDTM, and ADaM standards

  • Configuring leading Electronic Data Capture (EDC) systems

  • Real-world data sourcing from EMRs, claims, registries, and wearables

  • Designing robust RWE protocols with advanced confounding control methods

  • Biostatistical analysis using SAS and R (multivariate, survival, time-to-event)

  • Communicating insights via professional dashboards and journal-style reports

  • Preparing RWE for regulatory submission, label expansion, and market access

  • Ensuring ethics, data privacy, and global compliance in RWE practices

  • Integrating pharmacovigilance and signal detection into real-world studies

  • Completing a full-scale, peer-reviewed capstone project from protocol to presentation


This course offers applied theoretical real-world training aligned with global industry standards and regulatory expectations — ensuring you're equipped to deliver evidence that influences clinical, regulatory, and payer decision-making in today's dynamic healthcare environment.


Who this course is for:

  • Life-sciences, pharmacy, medicine, and public-health graduates aiming to build specialized skills in RWE design, analysis, and regulatory applications.
  • Clinical research associates, coordinators, and project managers seeking to deepen their expertise in both traditional trials and real-world data studies.
  • Data analysts and biostatisticians who want to apply their quantitative abilities to healthcare datasets—EMRs, claims, registries, and wearables.
  • Pharmacovigilance and safety-monitoring professionals looking to integrate RWE methods into signal detection and post-marketing surveillance.
  • Market-access, HEOR, and health-economics specialists requiring hands-on experience with RWE for value dossiers and reimbursement strategies.
  • Regulatory-affairs and quality-assurance staff responsible for preparing real-world evidence packages for label expansions and safety updates.
  • Healthcare-IT and informatics professionals aiming to implement EDC systems, CDISC standards, and data-privacy protocols in clinical and RWE projects.
  • Early-career researchers, graduate students, and career changers who aspire to enter the rapidly growing field of real-world evidence and data-driven healthcare research.

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