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About

Clinical trial endpoints are fundamental to determining whether an investigational treatment provides meaningful evidence of efficacy and safety. Well-defined endpoints translate clinical objectives into measurable outcomes and influence study design, sample size, statistical analysis, regulatory assessment, and interpretation of trial results.
This Pharmaceutical Medicine – Endpoints in Clinical Trials Training Course & Certification provides comprehensive knowledge of valid endpoints, primary, secondary, and exploratory endpoints, hard and surrogate endpoints, composite endpoints, patient-reported outcomes, endpoint selection, clinical relevance, estimands, sample size considerations, and the interpretation of endpoint results. The course also explores how endpoint choices affect trial design and regulatory decision-making. Upon successful completion, learners receive a certification demonstrating their understanding of clinical trial endpoints and best practices for endpoint selection and evaluation.

Who Should Enrol?

  • Clinical Research and Clinical Operations Professionals
  • Clinical Development and Pharmaceutical Medicine Professionals
  • Clinical Trial Managers and Clinical Research Associates
  • Biostatisticians and Statistical Programmers
  • Regulatory Affairs and Medical Affairs Professionals
  • Clinical Data Management and Outcomes Research Professionals
  • Pharmacovigilance and Drug Safety Professionals
  • Anyone involved in designing, conducting, analysing, or interpreting clinical trials
📢 Every purchase also includes our FREE companion Endpoints in Clinical Trials eBook, designed to help you apply principles in real-world pharmaceutical medicine settings.

What you will learn

Understand the role of endpoints in clinical trials, including how endpoints translate study objectives into measurable outcomes for evaluating treatment effects.

Learn the differences between primary, secondary, and exploratory endpoints, and understand how endpoint selection influences trial design, interpretation, and regulatory decision-making.

Develop knowledge of hard and surrogate endpoints, composite endpoints, patient-reported outcomes, and the principles used to assess endpoint validity and clinical relevance.

Gain practical understanding of endpoint selection, estimands, sample size considerations, statistical analysis, and common challenges in defining and interpreting clinical trial endpoints.

Course Syllabus

  1. A. The endpoint as the trial's question made measurable
  2. What is an endpoint?
  3. Why endpoint validity matters in your work
  4. The three-pillar framework, previewed
  5. B. Pillar 1 — Relevance
  6. What relevance means
  7. Relevant versus less relevant — an example
  8. Who counts as a 'decision-maker'?
  9. The relevance pitfall: measurable but not relevant
  10. C. Pillar 2 — Measurability
  11. Measurability — the four components
  12. Reliability — the same result on repeat measurement
  13. Validity — measuring what it claims to measure
  14. Responsiveness — sensitive enough to detect real change
  15. Putting measurability together
  16. D. Pillar 3 — Clinical meaningfulness
  17. What 'clinically meaningful' means
  18. The minimal clinically important difference (MCID)
  19. Statistical significance vs clinical meaningfulness
  20. Is veltarib's HbA1c effect clinically meaningful?
  21. E. The operational definition
  22. The operational definition — the five elements
  23. Who measures it, and with what instrument
  24. When — the timepoint(s)
  25. How — the standardised procedure
  26. The operational definition, applied to NW-DIAB-301
  27. F. Objective vs subjective measures; endpoint forms
  28. Objective measures
  29. Subjective measures
  30. Objective vs subjective — the trade-off
  31. Endpoint forms — three ways to express a result
  32. Continuous endpoints
  33. Binary and responder endpoints
  34. Time-to-event endpoints
  35. Why endpoint form affects trial size and duration
  36. G. Ascertainment, adjudication and pre-specification
  37. Ascertainment — how the outcome is captured
  38. Adjudication — independent, blinded confirmation
  39. Pre-specification — defined before the data are seen
  40. Common pre-specification pitfalls
  41. Putting the checklist together
  42. H. Worked example — is HbA1c at Week 26 valid for veltarib?
  43. The case, set up
  44. Worked example — judging Relevance
  45. Worked example — judging Measurability
  46. Worked example — judging Clinical meaningfulness
  47. Worked example — bringing the verdict together
  48. Worked example — a contrast worth naming

  1. A. Endpoint Roles — Primary, Secondary, Exploratory
  2. One trial, many endpoints, different jobs
  3. The endpoint hierarchy as a pyramid
  4. Same word, different weight
  5. Why roles must be pre-specified, not decided after the fact
  6. A quick self-check for role
  7. B. The Primary Endpoint — Powered and Claim-Bearing
  8. What makes an endpoint 'primary'
  9. From trial objective to primary endpoint to sample size
  10. veltarib's primary endpoint in NW-DIAB-301
  11. The primary carries the main claim — meet it or miss it
  12. Guardrails that keep a primary primary
  13. C. Secondary Endpoints — Support, Context and Key Secondaries
  14. veltarib's secondary endpoints in NW-DIAB-301
  15. Key secondary endpoints can earn their own label claim
  16. Secondary endpoints still need discipline
  17. Reading secondary results honestly
  18. D. Exploratory Endpoints — Hypothesis-Generating, No Claim
  19. veltarib's exploratory endpoints in NW-DIAB-301
  20. No claim rests on an exploratory endpoint
  21. The hypothesis-generating cycle
  22. E. Why You Cannot Have Many 'Primaries'
  23. More 'primaries' tested, more false-positive risk
  24. Co-primary endpoints — the narrow, deliberate exception
  25. Is this a valid co-primary design?
  26. F. Multiplicity, the Testing Hierarchy and the Alpha Budget
  27. Multiplicity — why testing many things inflates false-positive risk
  28. The pre-specified testing hierarchy (gatekeeping)
  29. Alpha as a budget
  30. Gatekeeping in practice — an illustrative veltarib order
  31. What breaks if you skip the hierarchy
  32. Three outcomes of a gatekept sequence
  33. G. From Primary Endpoint to Sample Size — and the Estimand Link
  34. The primary endpoint drives the sample size
  35. A preview: endpoint choice changes the whole trial
  36. The endpoint is one attribute of the estimand
  37. Why the estimand link matters for endpoint choice
  38. Section G recap
  39. The hierarchy revisited
  40. H. Worked Example — Classify the veltarib NW-DIAB-301 Endpoints
  41. The full NW-DIAB-301 protocol endpoint list
  42. Reading the protocol like a reviewer
  43. Step 1 — Classify the primary endpoint
  44. Step 2 — Classify the secondary endpoints
  45. Step 3 — Classify the exploratory endpoints
  46. Putting it together — the full classification
  47. What would change this classification?

  1. A. Hard endpoints and surrogate endpoints, defined
  2. What is a hard (clinical) endpoint?
  3. What is a surrogate endpoint?
  4. Hard versus surrogate — side by side
  5. The logic chain a surrogate relies on
  6. Sorting endpoints: hard or surrogate?
  7. Why this distinction matters so much
  8. B. Why surrogates are used
  9. Why not just always use hard endpoints?
  10. Faster: a marker moves before a hard event accrues
  11. Smaller: far fewer patients needed
  12. Earlier: usable for exploratory, earlier-phase decisions
  13. Sometimes more ethical: avoiding needless exposure
  14. The trade-off: speed and size against certainty
  15. C. The validation ladder
  16. The validation ladder — three rungs
  17. Biomarker vs surrogate endpoint — not the same thing
  18. Rung 1 — Validated surrogate
  19. Rung 2 — Reasonably likely to predict clinical benefit
  20. Rung 3 — Candidate surrogate
  21. Prentice criteria — the conceptual test
  22. The FDA surrogate-endpoint table
  23. Where HbA1c and PFS sit on the ladder
  24. D. Regulatory scrutiny and accelerated approval
  25. Why regulators scrutinise surrogates so closely
  26. The core risk, illustrated
  27. Accelerated approval on a promising surrogate
  28. The confirmatory-trial requirement
  29. The full accelerated-approval sequence
  30. What happens if the confirmatory trial fails to confirm
  31. onvatinib's accelerated-approval pathway, recapped
  32. E. Cautionary tales — when a surrogate misled
  33. Why cautionary tales matter here
  34. CAST — the Cardiac Arrhythmia Suppression Trial
  35. Torcetrapib — raised HDL, increased events and death
  36. Bevacizumab in metastatic breast cancer
  37. What the three cautionary tales have in common
  38. F. HbA1c/PFS vs MACE/OS — bringing it together
  39. Our two surrogates and two hard endpoints, side by side
  40. Diabetes: HbA1c versus MACE, side by side
  41. Oncology: PFS versus OS, side by side
  42. Endpoint choice: what it is really deciding
  43. A practical checklist: judging a candidate surrogate
  44. Setting up the worked example
  45. G. Worked example — critiquing a surrogate
  46. Step 1 — where does HbA1c sit on the ladder?
  47. Step 2 — what does that validation cover, and what doesn't it?
  48. Step 3 — why NW-DIAB-CVOT is still required
  49. Step 4 — the NW-DIAB-CVOT result and what it proves

  1. A. Why combine components into one endpoint
  2. What is a composite endpoint?
  3. Reason one: more events, more statistical power
  4. Reason two: capturing a multi-faceted disease
  5. Reason three: handling competing risks
  6. Combining components: benefit and cost
  7. B. MACE - the archetype composite endpoint
  8. 3-point MACE - the fixed definition
  9. Why these three components, specifically?
  10. The three MACE components, at a glance
  11. Adjudication: who decides a component 'counts'
  12. Beyond 3-point: the 4-point and 5-point MACE variants
  13. More components: more power, or more dilution?
  14. MACE quick-reference checklist
  15. C. Time-to-first-event counting
  16. Time-to-first-event: the counting rule
  17. A worked mini-illustration of the counting rule
  18. What time-to-first-event leaves uncounted
  19. Time-to-first-event vs counting every event
  20. D. Interpretation pitfalls - reading a composite honestly
  21. Pitfall one: components can move in different directions
  22. An illustrative divergent-component result
  23. Pitfall two: driven by the least serious or most frequent component
  24. Pitfall three: components are not equally important to patients
  25. How to interpret a composite result, step by step
  26. Reading a composite honestly: the checklist
  27. The classic misreading, in practice
  28. E. Competing risks and the weighting problem
  29. What is a competing risk?
  30. Composite as a partial answer to competing risk
  31. The weighting problem, restated
  32. Conceptual approaches to the weighting problem
  33. Simplicity versus clinical faithfulness
  34. F. Beyond MACE - win-ratio and hierarchical composites
  35. The win-ratio concept
  36. Hierarchical composites: a pre-specified testing order
  37. Standard composite vs win-ratio/hierarchical approaches
  38. When you might see win-ratio or hierarchical methods
  39. G. Worked example - reading the veltarib MACE result honestly
  40. Setting up the worked example: NW-DIAB-CVOT
  41. The headline result: HR 0.87 (95% CI 0.78-0.97)
  42. An illustrative component breakdown
  43. Interpreting the breakdown: what moved, and what didn't
  44. What can, and cannot, be claimed
  45. Closing the loop: read a composite honestly

  1. A. What a PRO is, and the COA family
  2. What is a Patient-Reported Outcome?
  3. The COA family — four types
  4. ClinRO, ObsRO and PerfO — the other three, defined
  5. PRO vs the other COA types — the defining line
  6. Is this outcome measure a PRO? A quick test
  7. B. Why PROs matter, and their growing regulatory weight
  8. Why PROs matter
  9. PROs and patient-centred drug development
  10. PROs' growing regulatory weight — a short timeline
  11. What 'growing weight' means in practice
  12. C. Instruments and their validation
  13. Building and validating a PRO instrument — the lifecycle
  14. Concept of interest, instrument, and claim
  15. Content validity — does it cover what matters to patients?
  16. Reliability and construct validity
  17. Responsiveness and the recall period
  18. Putting the four measurement properties together
  19. Common validation pitfalls to watch for
  20. D. The FDA PRO guidance, PFDD/COA, and ePRO
  21. The FDA PRO guidance (2009), in outline
  22. Patient-Focused Drug Development and the COA programme
  23. ePRO — capturing PROs electronically
  24. ePRO — compliance, timestamps and 'parking-lot syndrome'
  25. ePRO — bring-your-own-device (BYOD) considerations
  26. E. Where PROs sit — primary vs secondary
  27. Where does a PRO sit in the endpoint hierarchy?
  28. PRO as PRIMARY — symptom-driven conditions
  29. PRO as SECONDARY/supportive — objective measure leads
  30. Deciding where a PRO sits — the practical test
  31. F. Labelling claims and common pitfalls
  32. Labelling claims — the evidentiary bar
  33. What a PRO-based label claim requires
  34. Pitfall — missing data
  35. Pitfall — open-label bias
  36. Pitfall — choosing an ill-fitting instrument
  37. G. Worked example — classifying veltarib's PRO
  38. The case, set up
  39. Worked example — is this concept genuinely symptom-driven?
  40. Worked example — primary, key-secondary, or exploratory?
  41. Worked example — checking fit-for-purpose and ePRO capture
  42. Worked example — what label claim could it support?
  43. Worked example — bringing the verdict together

  1. A. The Big Idea — Endpoint Choice Drives Everything Downstream
  2. The endpoint is a lever, not just a label
  3. A rare hard event needs a huge, long trial
  4. A validated surrogate needs a smaller, faster trial
  5. Continuous, binary and time-to-event endpoints differ in efficiency
  6. Time-to-event endpoints are event-driven, not calendar-driven
  7. Design and endpoint are chosen together
  8. The four levers, side by side
  9. Why this matters when you read a protocol
  10. B. A Light Bridge to Sample Size Calculations and Clinical Trial Design
  11. Sample Size Calculations — what feeds the number
  12. Clinical Trial Design — the estimand as the thread
  13. Why this is only a bridge
  14. Scope check: this course vs the two it points to
  15. C. Oncology Walk-Through — Same Tumour, Three Endpoints
  16. One tumour, three endpoints, three jobs
  17. ORR — the fast signal for accelerated approval
  18. PFS — the randomised, registrational endpoint
  19. OS — the confirmatory, gold-standard hard endpoint
  20. The GOAL and STAGE pick the endpoint
  21. onvatinib NSCLC — endpoint vs goal, stage and design
  22. Reading OS carefully — post-progression crossover
  23. ORR, PFS and OS — a hierarchy of evidence strength
  24. Worked example — mapping onvatinib's three endpoints
  25. D. Diabetes Walk-Through — Same Drug, Two Endpoints
  26. Same drug, same disease, two different jobs
  27. NW-DIAB-301 — the HbA1c trial
  28. NW-DIAB-CVOT — the MACE cardiovascular-outcomes trial
  29. Small-and-short versus huge-and-long, same drug
  30. veltarib — size and duration contrast, at a glance
  31. MACE — a composite, hard, adjudicated endpoint
  32. Worked example — which veltarib trial answers which question?
  33. E. The Mechanics — Event-Driven Design and Rare Events
  34. PFS vs OS — fewer events, faster answer
  35. Inside an event-driven trial's lifecycle
  36. What rarity costs a trial
  37. Endpoint type and data form, mapped with real examples
  38. Worked example — applying the framework to a new endpoint
  39. F. Synthesis — Reading Any Trial Through Its Endpoint
  40. The decision framework, one page
  41. Headline effect sizes, at a glance
  42. The master table — five trials, one pattern
  43. A checklist for reading any protocol
  44. The endpoint types that reveal size and design
  45. The endpoint as the connective thread

  1. 📘 Bonus: Endpoints in Clinical Trials eBook (Free with purchase)

Course Benefits

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Free eBook

Get our exclusive eBook with every purchase - a complete companion guide to the course, yours to keep forever

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CPD Points

Gain Continuing Professional Development points on completion of this course.

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Certification

Receive a personal certificate to show your subject knowledge on course completion.

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Affordable

You get excellent value through our cost-effective prices. We can also offer you group discounts on larger purchases.

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Flexibility

The course saves you time through the convenience of online availability. This lets you complete the interactive course at your own comfort.

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Keep Up to Date

You will stay up to date with any changes to ICH E9 (Statistical Principles for Clinical Trials), ICH E9(R1) (estimands and sensitivity analysis), ICH E3 (clinical study reports) and CONSORT 2010 reporting standards for time-to-event endpoints, as our training courses are constantly monitored, reviewed and updated.

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Learn from Industry Experts

The course content has been developed by practitioners in oncology and clinical-trial biostatistics to ensure that learners can read a Kaplan-Meier curve and a forest plot, interpret a hazard ratio, and report a survival analysis that will withstand regulatory and peer review.


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