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Sample Size Calculations Course & Certification
Intermediate
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Sample Size Calculations
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About
Biostatistics - Sample Size Calculations training provides the knowledge and practical skills required to determine appropriate sample sizes for clinical trials and research studies, ensuring that studies are adequately powered to detect clinically meaningful treatment effects while maintaining statistical validity and regulatory compliance. It equips learners with a strong foundation in statistical power, Type I and Type II errors, effect size assumptions, variability estimation, event-driven designs, dropout adjustments, and practical sample size planning considerations used in pharmaceutical, biotechnology, and clinical research environments.
This Biostatistics - Sample Size Calculations Course & Certification covers principles of statistical power and hypothesis testing, sample size determination for continuous, binary, and time-to-event endpoints, superiority, non-inferiority, and equivalence trial designs, effect size selection, variance estimation, allocation ratios, dropout inflation, blinded sample size re-estimation, event-driven trial planning, feasibility considerations, and interpretation of sample size assumptions within clinical study protocols. Upon successful completion, learners receive a certification demonstrating their understanding of sample size methodology and its application in clinical trial design, biostatistics, and regulatory-compliant research planning.
Who Should Enrol?
- Biostatisticians and Statistical Programmers
- Clinical Trial Statisticians and Quantitative Scientists
- Clinical Research Associates (CRAs) and Clinical Research Professionals
- Clinical Data Managers and Clinical Data Analysts
- Medical Researchers and Clinical Investigators
- Pharmaceutical, Biotechnology, and CRO Personnel
- Regulatory Affairs and Clinical Development Professionals
- Quality Assurance and Compliance Professionals involved in clinical research
- Students, Graduates, and Professionals seeking a career in Clinical Research, Biostatistics, and Drug Development
What you will learn
Understand the principles of statistical power, hypothesis testing, and sample size determination in clinical research and clinical trials.
Learn how to calculate and justify sample sizes for continuous, binary, and time-to-event endpoints while balancing scientific validity, ethical considerations, feasibility, and regulatory expectations.
Develop knowledge of Type I and Type II errors, effect size assumptions, variability estimation, event-driven trial design, allocation ratios, dropout adjustments, and sample size re-estimation methods.
Gain understanding of superiority, non-inferiority, and equivalence study designs, power analysis, protocol sample size justification, and best practices for designing adequately powered, efficient, and regulatory-compliant clinical trials.
Course Syllabus
- What sample-size justification is and why a whole course exists for it
- Who this course is for — protocol writers, trial designers, biostatisticians, grant applicants
- The four inputs preview — alpha, power, effect size, variability
- Meet HORIZON-2 and its three endpoint types
- The six-module roadmap and the applied M6 capstone
- How the course is assessed
- Scope and boundary note
- What 'powering a study' actually means
- The ethics of an under-powered trial
- The ethics of an over-powered trial
- Power as a feasibility and cost driver — sites, timelines, budget, drug supply
- Where the justification lives — protocol, SAP, grant, ethics submission, CONSORT
- Consequences of getting it wrong — inconclusive trials, failed submissions
- The post-hoc (observed) power fallacy
- Reverse the lever — what effect can a fixed feasible N detect?
- The hypothesis-test frame — H0/H1 and the 2x2 truth table
- Type I error (alpha) and what 'two-sided 0.05' buys you
- Type II error (beta) and power = 1-beta
- Effect size — raw (delta, difference in proportions, HR) versus standardised (Cohen's d, h, log HR)
- Size for the MCID, not the hoped-for effect
- Variance and SD assumptions and their sources — the danger of an optimistic sigma
- One- versus two-sided tests and when each is defensible
- How each lever moves N — the n proportional to sigma-squared relationship
- The two-means formula in plain English, applied to HORIZON-2 6MWD
- The standardised-effect shortcut and paired versus two-sample tests
- Equal versus unequal allocation — the (1+k)^2/(4k) inflation
- The two-proportions formula, applied to HORIZON-2 responder rate
- Pooled versus unpooled variance
- Continuity correction — what it is and when it matters
- Arcsine / Cohen's-h versus the standard formula — the pwr.2p.test caveat
- Risk-difference, relative-risk and odds-ratio framings
- Why time-to-event studies are events-driven, not subjects-driven
- The Schoenfeld events formula applied to HORIZON-2 (HR=0.75)
- Converting required events into subjects via the overall event probability
- Accrual, follow-up, censoring and the proportional-hazards assumption
- Superiority versus non-inferiority versus equivalence — the CI-vs-margin pictures
- Choosing the NI margin and one-sided alpha; why NI often needs more subjects
- Equivalence as two one-sided tests (TOST)
- Assay sensitivity and the constancy assumption
- The computed N is an evaluable-subject count, not an enrolment target
- The dropout inflation formula N_adjusted = N/(1-d)
- Estimating the dropout rate and being conservative
- Non-evaluable subjects versus dropout versus missing data
- Blinded sample-size re-estimation — re-estimating nuisance parameters
- Unblinded / interim-based re-estimation and the alpha control it demands
- Group-sequential designs — spending functions and early stopping
- Adaptive designs and the operational cost of over- or under-recruiting
- Two-sample means — HORIZON-2 6MWD: inputs, formula, arithmetic, dropout (190 to 224/arm)
- Two proportions — HORIZON-2 responder: 0.30 vs 0.45 (163 to 192/arm) and the pwr.2p.test caveat
- Survival / events — HORIZON-2 primary: 508 events to 1694 subjects to 1883 with 10% LTFU
- A non-inferiority contrast (M=25 to 273/arm)
- Writing the sample-size justification sentence for the protocol
- Naming the tools — R (pwr, gsDesign, survival), PASS, nQuery
- How to defend it in review and common mistakes to avoid
- Your turn — a re-sizing variation
- Synthesis of the four levers and the three formula families
- The one-page sample-size justification checklist
- The mindset — size for the MCID, be honest about sigma, always adjust for dropout
- Where the standards and tools live
- Scope and boundary note
- 📘 Bonus: Sample Size Calculations eBook (Free with purchase)
Course Benefits
Free eBook
Get our exclusive eBook with every purchase - a complete companion guide to the course, yours to keep forever
CPD Points
Gain Continuing Professional Development (CPD) Points, accredited by The Faculty of Pharmaceutical Medicine of the Royal College of Physicians of the United Kingdom. These can be used to count towards the distance learning element of any scheme that comes under the umbrella of The Academy of Medical Royal Colleges or any other scheme for which there is mutual recognition.
Certification
Receive a personal certificate to show your subject knowledge on course completion.
Affordable
You get excellent value through our cost-effective prices. We can also offer you group discounts on larger purchases.
Flexibility
The course saves you time through the convenience of online availability. This lets you complete the interactive course at your own comfort.
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 E10 (choice of control group and non-inferiority) and CONSORT 2010 (item 7a on sample size), as our training courses are constantly monitored, reviewed and updated.
Learn from Industry Experts
The course content has been developed by practitioners in clinical-trial biostatistics to ensure that learners can select the correct formula, compute a sample size and write a justification that will withstand regulatory and peer review.
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