¿Why this one Course?

  • Build practical expertise: Develop the skills to design robust and impactful RCTs.
  • Learn the full process: From randomisation to interpretation, gain a clear and structured understanding of how RCTs work in practice.
  • Learn from real-world data: Learn from worked examples based on real RCT datasets, including guided interpretation of Stata syntax and output.
  • Strengthen critical thinking:Improve your ability to identify bias, address baseline imbalances, and evaluate ethical considerations in trial design.

Program objectives

Randomised controlled trials (RCTs) are the gold standard in clinical and experimental research – and the ability to design and interpret them is an essential skill for today’s health professionals and researchers.

This intensive 1-week course provides a practical and in-depth exploration of the design, analysis and interpretation of RCTs, with a particular focus on parallel-group designs. Through real-world examples and expert guidance, you will gain the confidence to critically assess trial evidence and contribute to high-quality research.

Learning path

1

Foundations

2

Basic design and interpretation

3

Design and analysis of RCT data: core issues

4

Advanced RCT design and analysis

5

Synthesis and applications

Meta Final

Randomised controlled trials: design, analysis and interpretation

40 Horas
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Program content

1

Foundations

    Morning session (9:00 AM - 12:00 PM)

    • Welcome and introductions
    • Lecture: Introduction to RCTs
      • History and evolution of RCTs
      • Why RCTs? Pitfalls of observational studies
      • Overview of RCT design principles: randomisation, control groups, blinding, and ethics
    • Discussion: Ethical considerations in RCTs
      • Ethical frameworks and guidelines (e.g., Declaration of Helsinki)
      • Informed consent, patient safety, and minimising harm
      • Balancing scientific rigor with ethical concerns
      • GCP training: websites

    Afternoon session (2:00 PM - 4:30 PM)

    • Lecture: Key components of RCT design
      • Randomisation techniques (simple, block, stratified, cluster)
      • Types of control groups (placebo, active control, no-treatment)
      • Blinding (single, double, triple) and its importance in reducing bias
    • Interactive activity: Analysing case studies
      • Break into small groups to analyse real-world case studies of RCTs: key design issues (identifying randomisation techniques, control groups, and blinding methods)
    • Q&A and wrap-up
2

Basic design and interpretation

    Morning session (9:00 AM - 12:00 PM)

    • Lecture: RCT justification and trial objectives
      • What is your trial trying to show? (Efficacy versus effectiveness [incl. adherence], explanatory versus pragmatic trials, equipoise and the uncertainty principle)
      • Formulation of trial objectives
      • Intercurrent events and estimands
    • Practical exercise: randomisation
      • Simple randomisation
      • Stratified and block design
      • Minimisation
      • Cluster randomisation

    Afternoon session (2:00 PM - 4:30 PM)

    • Lecture: Interpreting evidence from RCTs
      • P-values and confidence intervals
      • Sample size and power
      • Pitfalls of p-values
      • Sequential learning from trials
    • Interactive activity: Analysing case studies
      • Break into small groups to analyse real-world case studies of RCTs: the Introduction section of published RCT papers
    • Q&A and wrap-up
3

Design and analysis of RCT data: core issues

    Morning session (9:00 AM - 12:00 PM)

    • Lecture: Trial population, control group and baseline imbalances
      • ‘Representativeness’ of the trial population
      • Internal vs external validity
      • Baseline characteristics (rationale, variable selection, interpretation)
    • Interactive activity: Analysing case studies
      • Break into small groups to analyse real-world case studies of RCTs, focused on interpretation of effects

    Afternoon session (2:00 PM - 4:30 PM)

    • Lecture: Basic analysis of intervention effects *
      • Statistical analysis plan
      • Group descriptives
      • The intervention effect: a conceptual definition (counterfactual argument, endpoint versus change scores)
      • Simple analysis of intervention effects
        • Summary measures (mean, geometric mean, median, prevalence, odds, etc.)
        • Summary measures of effect (mean difference, risk ratios, odds ratios, etc.)
        • Interpretation of intervention effects (absolute vs relative effects; standardised effects; NNT/NNH)
        • Analysis of log transformed outcomes
    • Interactive activity: Analysing case studies
      • Break into small groups to analyse real-world case studies of RCTs: analysis of baseline and simple intervention effects
4

Advanced RCT design and analysis

    Morning session (9:00 AM - 12:00 PM)

    • Lecture: Advanced analysis of intervention effects *
      • Adjustment for outcome variable measured at baseline (interpretability of treatment effects, statistical precision)
      • Adjustment for group differences at baseline
      • Baseline modification of intervention effects (‘subgroup analysis’, forest plots)
      • Adjustment for study design
      • Evaluating time changes in intervention effects

    Afternoon session (2:00 PM - 4:30 PM)

    • Lecture: ITT analysis and estimands *
      • ITT and per protocol analysis
      • Handling missing values in ITT analysis
    • Interactive activity: Analysing case studies
      • Break into small groups to analyse real-world case studies of RCTs: handling of missing data / longitudinal data
5

Synthesis and applications

    Morning session (9:00 AM - 12:00 PM)

    • Lecture: Beyond linear and logistic regression: expanding the toolkit *
    • Advanced outcomes
      • Censored outcomes (tobit regression)
      • Time-to-event (survival) outcomes (Kaplan-Meier, Cox regression)
      • Count models (Poisson, negative binomial)
      • Ordinal outcomes (proportional odds logistic regression, ordinal probit models, non-parametric tests)
      • Semi-continuous outcomes (two-part models)
    • Alternative RCT designs
      • Cross-over trials
      • Factorial trials
      • Non-inferiority trials
    • Review session
    • Recap of RCT principles
      • Review of statistical methods: hypothesis testing, regression, ITT analysis
    • Common challenges and best practices in RCT design and analysis (missing values, multiplicity)
    • Group project: designing and analysing an RCT
      • Participants will design an RCT from scratch based on a provided research question, including:
        • Study objectives, hypothesis, and randomisation plan
        • Control group and blinding strategy
        • Statistical analysis plan (including regression models, ITT analysis)
      • Presentations of group designs and analysis plans
      • Instructor and peer feedback

     

    Afternoon session (2:00 PM - 4:30 PM)

    • Group Project: Designing and analysing an RCT (continued)
      • Participants will design an RCT from scratch based on a provided research question
      • Presentations of group designs and analysis plans
      • Instructor and peer feedback
    • Course wrap-up and Q&A
      • Final review of key takeaways
      • Guidance documents (CONSORT; SPIRIT; ICH E6, E8, E9, E10)
      • Feedback on the course and final questions

Learning methodology

Lectura de materiales bibliográficos

Se han seleccionado una serie de lecturas obligatorias y presentaciones, las cuales deberán ser revisadas por los participantes de manera previa a la realización de cada actividad con el propósito de fomentar la participación del estudiante y favorecer el desarrollo de discusiones sobre situaciones específicas.

Sesiones sincrónicas

Las sesiones virtuales se emplean para proporcionar retroalimentación inmediata entre participantes y estudiantes, manteniendo presentaciones en vivo.

Envío de actividades

El envío de actividades es un proceso fundamental para la interacción entre docentes y estudiantes, mediante la realización de tareas específicas.

Foro

Este análisis de las competencias adquiridas de los participantes permite evaluar evaluaciones entre sí, además de conocer otros puntos de vista.

Competencies to develop

Having completed this course, participants are expected to be able to:

  • Identify and distinguish count variables from other types of variables
  • Recall the basic function and assumptions of count models
  • Describe, explain and interpret offset variables
  • Construct, select, interpret and evaluate regression models for count data with Poisson and negative binomial distributions
  • Identify overdispersion, and describe reasons for and consequences of persistent overdispersion
  • Construct, interpret and evaluate zero-truncated count models, Hurdle models and zero-inflated models
  • Report and interpret margins and marginal effects as estimated by count models

Admission requirements

   
  • This course is offered in English.

This course is ideal for:

  • (Clinical) researchers seeking to strengthen their trial design and analytical skills.
  • Healthcare professionals aiming to apply evidence-based practices with greater confidence.
  • Data analysts and statisticians looking to deepen their expertise in clinical trial analysis.
  • Graduate students building a strong foundation for a career in health research.
  • Pre-requisite courses or knowledge:
    • Prior exposure to the following topics is recommended:
    • Introductory statistics (essential): Basic concepts such as t-tests, chi-squared tests, ANOVA, linear regression analysis, logistic regression analysis.
    • Introduction to epidemiology (desirable): Introductory knowledge of epidemiology or public health.
  • No prior experience with RCTs is required.