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Overview of Real-World Evidence and Cross-Sectional Studies

Educators


  • Daniel Abazia, PharmD, BCPS, CPPSClinical Associate Professor - dabazia@pharmacy.rutgers.edu



  • Aneesha Santhosh, PharmDClinical Assistant Professor - aneesha.bhatia@pharmacy.rutgers.eduDate: November 8, 2024

Topic

  • Cross-Sectional Studies & Real-World Evidence

Learning Objectives

  • Define Real-World Evidence (RWE) vs. Real-World Data (RWD)

  • Explain Impact of RWE in clinical practice

  • Develop Clinical Decisions based on RWE

  • Compare Roles of observational vs experimental research

  • Identify Framework of a cross-sectional study

  • Analyze Results of a cross-sectional study

Definitions

  • Real-World Data (RWD):

    • Data relating to patient health status and healthcare delivery collected from various sources.

  • Real-World Evidence (RWE):

    • Clinical evidence derived from analysis of RWD regarding the benefits or risks of medical products.

  • Non-interventional (Observational) Study:

    • Patients receive marketed drugs in routine practice without assigned protocols.

FDA Initiatives

  • Workshop on RWE:

    • A public workshop focused on optimizing RWE in drug development scheduled for December 12, 2024.

    • Discussions on future applications and initiatives for RWD in drug labeling and development.

RWE vs. RCT (Randomized Controlled Trials)

  • RCTs focus on efficacy/safety in a research setting, typically homogeneous populations with high costs and fixed treatments.

  • RWE provides effectiveness safety insights from heterogeneous populations with variable treatments and lower costs.

Evolution of RWE

  • Significant historical developments:

    • 2006: IOM report on drug safety.

    • 2007: FDA Amendments Act (post-market risk analysis).

    • 2010: Patient-Centered Outcomes Research Institute (PCORI).

    • 2016: 21st Century Cures Act emphasized the use of RWE.

21st Century Cures Act - RWE Provision

  • Defines RWE and mandates the FDA to leverage it for drug approvals and modifications post-market.

  • Requires frameworks and guidance on RWE analysis and standards.

Emerging Trends in RWE and Data Usage

  • Data Sources:

    • Claims, electronic health records, registries, databases.

  • Framework for Regulatory Decisions:

    • Evaluating data reliability, adherence to regulatory standards, and significance to clinical questions.

Cross-Sectional Studies

  • Overview:

    • Observational designs providing a snapshot of diseases or health characteristics at a point in time.

    • Types: Descriptive vs Analytical studies.

  • Descriptive Studies:

    • Focus on prevalence metrics without inferring causality.

    • Example: Measuring asthma prevalence among university students.

  • Analytical Studies:

    • Explore associations or potential causal relationships within a population.

    • Example: Correlating smoking with lung cancer diagnoses.

Prevalence vs. Incidence

  • Prevalence:

    • Proportion of a population with a disease at a specific time.

  • Incidence:

    • Number of new cases developing in a defined time period.

Statistical Measures in Studies

  • Odds Ratio (OR):

    • Measure of association between exposure and outcome; calculated from case data.

  • T-Tests:

    • Used to compare means between two groups ensuring appropriateness of assumptions.

  • Multiple Regression Models:

    • Estimate relationships between variables while controlling for confounders.

Strengths and Weaknesses of Cross-Sectional Studies

  • Strengths:

    • Cost-effective, can examine multiple outcomes, ethical ease in design.

  • Weaknesses:

    • Cannot determine causality or incidence; requires adequate sample sizes for precision.

Example Case Study

  • Pandemic Readiness in NYC:

    • Cross-sectional study assessing preparedness during the COVID-19 outbreak.

    • Findings indicated high levels of unpreparedness (81.6% not ready) based on surveyed data.

Conclusion

  • Both RWE and cross-sectional studies are instrumental in understanding real-world issues and informing clinical practice.

  • Their ability to analyze large populations and real-world conditions aids in decision-making and safety monitoring post-drug approval.