Real-World Data and Real-World Evidence Analytics Training Course

Course Introduction

The Real-World Data and Real-World Evidence Analytics Training Course provides a comprehensive foundation for understanding, managing, analyzing, and interpreting data generated from routine practice, healthcare delivery, patient experiences, observational studies, registries, electronic health records, claims databases, and other real-world sources. The course examines how diverse data can be transformed into credible evidence that supports research, policy, clinical decision-making, health technology assessment, and organizational strategy.

Participants will explore the characteristics, structures, strengths, limitations, and quality considerations associated with major real-world data sources. The training addresses electronic health records, administrative and claims data, disease registries, laboratory databases, pharmacy data, patient-generated information, digital health platforms, wearable technologies, surveys, and observational datasets. Emphasis is placed on understanding data provenance, completeness, representativeness, interoperability, coding systems, missingness, and other factors that influence the reliability of real-world evidence.

The course develops practical analytical capabilities for transforming raw real-world data into meaningful evidence. Participants will learn about data preparation, linkage, cleaning, validation, descriptive analysis, cohort construction, outcome definition, exposure assessment, longitudinal analysis, and appropriate statistical methods for observational data. Attention is given to confounding, selection bias, information bias, missing data, time-related biases, and other methodological challenges that can affect conclusions derived from non-randomized data.

A major component of the training focuses on study design and causal inference using real-world data. Participants will examine retrospective and prospective observational designs, comparative effectiveness research, cohort and case-control studies, target trial emulation, propensity score methods, matching, weighting, stratification, sensitivity analysis, and causal diagrams. The course also considers how analysts can strengthen study validity through transparent protocols, reproducible workflows, appropriate comparators, robust outcome definitions, and clearly documented analytical assumptions.

Participants will also investigate emerging applications of artificial intelligence, machine learning, automation, natural language processing, digital health data, and advanced analytics in real-world evidence generation. The training considers opportunities and limitations associated with algorithmic approaches, including model validation, explainability, bias, data drift, privacy, governance, and responsible use. Practical attention is given to integrating multiple data sources while maintaining analytical rigor, regulatory awareness, ethical standards, and reproducibility.

By the end of the course, participants will be better prepared to design and evaluate real-world evidence studies, assess the quality and suitability of real-world data, select appropriate analytical approaches, communicate findings to technical and non-technical audiences, and translate evidence into actionable insights. The course is particularly valuable for professionals seeking to strengthen evidence-based decision-making across healthcare, pharmaceutical research, public health, health economics, policy, research organizations, and data-driven health systems.

Duration

10 days

Who Should Attend

• Healthcare researchers involved in observational studies, clinical research, outcomes research, or evidence generation.

• Epidemiologists seeking advanced skills in real-world data analysis and comparative effectiveness research.

• Biostatisticians and data analysts working with healthcare, clinical, administrative, or longitudinal datasets.

• Pharmaceutical and biotechnology professionals involved in real-world evidence and post-market research.

• Public health professionals using routine health information and population-level data for decision-making.

• Health economists and health technology assessment professionals evaluating interventions using real-world evidence.

• Clinical professionals interested in understanding how routine-care data can support evidence generation.

• Research and policy professionals responsible for translating healthcare data into evidence for programs and interventions.

• Data scientists developing analytical models and machine learning applications for real-world healthcare data.

• Health information management and informatics professionals working with electronic and administrative health records.

• Monitoring and evaluation specialists using routine data to assess healthcare programs and outcomes.

• Government and regulatory professionals involved in evidence assessment, health policy, and healthcare decision-making.

Course Objectives

• Explain the concepts, characteristics, applications, and distinctions between real-world data and real-world evidence across healthcare and research environments.

• Identify and assess major real-world data sources according to their relevance, completeness, quality, representativeness, accessibility, and analytical suitability.

• Develop practical workflows for acquiring, integrating, cleaning, documenting, validating, and preparing heterogeneous real-world datasets for analysis.

• Design observational studies that appropriately address research questions involving exposures, interventions, outcomes, populations, and longitudinal healthcare experiences.

• Apply appropriate statistical and epidemiological techniques to analyze real-world data while accounting for the complexities of non-randomized evidence.

• Analyze major sources of bias, confounding, missingness, measurement error, selection effects, and time-related distortions in real-world evidence studies.

• Apply propensity scores, matching, weighting, stratification, regression, and other methods used to improve comparability between study populations.

• Explore causal inference frameworks and target trial emulation approaches for strengthening conclusions derived from observational healthcare data.

• Evaluate the quality, validity, reliability, transparency, reproducibility, and generalizability of real-world evidence generated from diverse data environments.

• Integrate emerging artificial intelligence, machine learning, natural language processing, and digital health technologies into responsible evidence-generation workflows.

• Interpret and communicate analytical findings effectively to researchers, clinicians, policymakers, regulators, executives, and other evidence users.

• Design practical real-world evidence strategies that support research, healthcare improvement, policy development, regulatory decision-making, and evidence-based resource allocation.

Comprehensive Course Outline

Module 1: Foundations Of Real-World Data And Real-World Evidence

• Definitions, terminology, evolution, and practical distinctions between real-world data and real-world evidence.

• Major applications of real-world evidence across healthcare, research, policy, regulation, and decision-making.

• Characteristics of observational evidence compared with evidence generated through randomized controlled studies.

• Opportunities, limitations, misconceptions, and emerging expectations surrounding real-world evidence generation.

Module 2: Real-World Data Sources And Data Ecosystems

• Electronic health records, electronic medical records, clinical databases, and routine health information systems.

• Claims, billing, administrative, pharmacy, laboratory, and healthcare utilization datasets.

• Disease registries, patient-generated data, surveys, wearables, mobile applications, and digital health platforms.

• Data provenance, source characteristics, interoperability, accessibility, ownership, and suitability for evidence generation.

Module 3: Data Quality Assessment And Validation

• Dimensions of real-world data quality including completeness, accuracy, consistency, timeliness, and validity.

• Data profiling, anomaly detection, duplicate identification, coding errors, and structural quality assessment.

• Assessing missingness, measurement errors, inconsistent definitions, and changes in data collection practices.

• Validation frameworks, quality assurance procedures, data fitness-for-purpose assessment, and documentation.

Module 4: Data Preparation, Cleaning And Transformation

• Data extraction, transformation, standardization, normalization, and preparation for analytical workflows.

• Managing missing values, outliers, duplicates, inconsistent variables, and erroneous observations.

• Developing reproducible data-cleaning pipelines and maintaining transparent data-processing documentation.

• Creating analysis-ready datasets while preserving data lineage, provenance, and methodological traceability.

Module 5: Data Linkage, Integration And Interoperability

• Principles and applications of deterministic, probabilistic, and privacy-preserving record linkage.

• Integrating clinical, administrative, laboratory, pharmacy, registry, claims, and patient-generated datasets.

• Common interoperability challenges involving terminology, coding systems, data formats, and inconsistent identifiers.

• Data integration governance, linkage validation, error assessment, and responsible management of linked datasets.

Module 6: Observational Study Design And Cohort Development

• Cohort, case-control, cross-sectional, longitudinal, retrospective, and prospective real-world study designs.

• Defining study populations, inclusion and exclusion criteria, index dates, exposures, comparators, and follow-up periods.

• Developing study protocols, analytical populations, eligibility algorithms, and reproducible cohort definitions.

• Addressing immortal time, selection, surveillance, and other biases arising from study design and cohort construction.

Module 7: Outcome, Exposure And Treatment Measurement

• Defining clinical outcomes, patient-reported outcomes, utilization measures, safety events, and economic endpoints.

• Identifying exposures, treatments, interventions, treatment switching, adherence, persistence, and discontinuation.

• Developing operational definitions and algorithms for variables derived from routine healthcare records.

• Validating outcome and exposure definitions and assessing misclassification within real-world evidence studies.

Module 8: Statistical Analysis Of Real-World Data

• Descriptive statistics, incidence and prevalence measures, survival analysis, regression, and longitudinal methods.

• Comparing treatment groups and estimating associations while accounting for differences in baseline characteristics.

• Handling repeated measurements, clustered observations, time-to-event outcomes, and complex longitudinal structures.

• Selecting analytical methods according to research objectives, data structure, assumptions, and evidence requirements.

Module 9: Confounding, Bias And Causal Inference

• Understanding confounding, selection bias, information bias, measurement error, and other threats to validity.

• Directed acyclic graphs and conceptual frameworks for identifying relationships among exposures, outcomes, and covariates.

• Propensity scores, matching, stratification, inverse probability weighting, and related adjustment techniques.

• Sensitivity analyses, negative controls, quantitative bias assessment, and transparent interpretation of causal findings.

Module 10: Comparative Effectiveness And Safety Research

• Designing comparative effectiveness studies using routine clinical and healthcare utilization data.

• Assessing treatment effectiveness, safety outcomes, adverse events, healthcare utilization, and patient outcomes.

• Active comparator designs, new-user designs, treatment switching, and approaches to reducing indication-related bias.

• Interpreting comparative findings while clearly distinguishing association, causal inference, uncertainty, and evidence limitations.

Module 11: Target Trial Emulation And Advanced Evidence Generation

• Principles of target trial emulation and translating hypothetical randomized trials into observational study designs.

• Defining eligibility, treatment strategies, assignment procedures, follow-up, outcomes, and causal estimands.

• Addressing time zero, treatment initiation, censoring, adherence, switching, and competing events.

• Evaluating assumptions, limitations, sensitivity analyses, and reporting standards for emulated target trials.

Module 12: Real-World Evidence For Health Economics And Policy

• Applying real-world data to healthcare utilization, costs, resource consumption, and economic outcome analysis.

• Integrating effectiveness, safety, quality-of-life, and economic evidence for health technology assessment.

• Using real-world evidence to inform healthcare planning, service evaluation, reimbursement, and resource allocation.

• Translating analytical results into evidence packages for policymakers, healthcare organizations, and decision-makers.

Module 13: Artificial Intelligence And Machine Learning In Real-World Evidence

• Applications of machine learning for prediction, risk stratification, phenotyping, outcome detection, and patient segmentation.

• Natural language processing for extracting structured evidence from clinical notes and unstructured healthcare documentation.

• Model development, validation, performance assessment, explainability, fairness, bias detection, and model monitoring.

• Responsible integration of artificial intelligence into real-world evidence workflows without compromising analytical validity.

Module 14: Digital Health, Patient-Generated Data And Emerging Data Sources

• Using wearable devices, mobile applications, remote monitoring, sensors, and connected health technologies for evidence generation.

• Integrating patient-reported and patient-generated information with conventional healthcare datasets.

• Evaluating data reliability, engagement patterns, representativeness, interoperability, and measurement consistency.

• Emerging opportunities involving digital biomarkers, decentralized research, synthetic data, and continuously collected health information.

Module 15: Governance, Privacy, Ethics And Regulatory Considerations

• Data privacy, confidentiality, informed consent, ethical oversight, responsible data access, and secure analytical environments.

• Governance frameworks for secondary data use, data sharing, cross-institutional collaboration, and research accountability.

• Regulatory expectations, transparency, reproducibility, auditability, and evidence standards for real-world research.

• Addressing algorithmic bias, responsible AI, data security, patient rights, and ethical challenges in large-scale analytics.

Module 16: Advanced Real-World Evidence Strategy And Implementation

• Developing end-to-end real-world evidence strategies from research questions and data selection through analysis and reporting.

• Building reproducible evidence-generation pipelines, analytical governance structures, documentation systems, and quality controls.

• Communicating uncertainty, limitations, findings, and actionable insights to technical and non-technical stakeholders.

• Designing sustainable real-world evidence programs that support continuous learning, research innovation, and evidence-based decision-making.

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808

Training Venue 

The training will be held at our Upskill Training Centre. We also offer training for a group (at a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.

Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Upskill certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808

Terms of Payment:

Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.