New
SM
Clara Fargo
EBMT Clinical Research Quality Assurance Coordinator
I have finalised the demo for the ICH-GCP E6 R3 refresher course. Overall, I liked the content and the interface. I also want to thank Whitehall Train...
Survival Analysis Course & Certification
Intermediate
Watch Preview
4.7
★★★★★
97% Recommended
3 hours
178 active users
3 points
Watch Preview
Your Study Basket
Survival Analysis
| Total | |
🎁 FREE Bonus Included
eBookWorth £29— Yours FREE when you purchase today.
VAT is levied as per your country’s applicable rules and rates at checkout
Save more with a monthly or annual plan that includes this course
Secure Payment Service
Rest assured we use secure processes to protect your payment.
Secured by:
About
Biostatistics Survival Analysis training provides a practical introduction to the statistical principles and methods used to analyse and interpret time-to-event data in clinical research and clinical trials. It helps learners understand fundamental survival analysis concepts, including survival endpoints, censoring, risk sets, survival and hazard functions, Kaplan-Meier estimation, and interpretation of survival curves.
This Biostatistics Survival Analysis Course & Certification provides essential knowledge of Kaplan-Meier methods, median survival, confidence intervals, number-at-risk tables, log-rank tests, hazard ratios, Cox proportional hazards regression, adjusted and unadjusted treatment effects, subgroup and interaction analysis, proportional-hazards assumptions, time-varying effects, competing risks, and cumulative incidence functions. Learners will develop practical skills to select appropriate statistical methods, compare treatment groups, assess model assumptions, interpret clinical trial survival results, and communicate time-to-event findings accurately and effectively.
Who Should Enrol?
- Biostatisticians and Statistical Analysts
- Clinical Trial Programmers and Statistical Programmers
- Clinical Research and Clinical Development Professionals
- Clinical Data Scientists and Data Analysts
- Medical and Scientific Researchers
- Regulatory Affairs and Medical Writing Professionals
- Pharmacovigilance and Drug Safety Professionals
- Anyone involved in analysing, interpreting, or reporting survival and time-to-event outcomes in clinical research
What you will learn
Understand the fundamentals of biostatistics and survival analysis, including time-to-event data, survival endpoints, censoring, risk sets, survival functions, and hazard functions used in clinical research.
Learn Kaplan-Meier estimation, survival curves, median survival, confidence intervals, number-at-risk tables, log-rank tests, and methods for comparing survival outcomes between treatment groups.
Develop knowledge of hazard ratios, Cox proportional hazards regression, adjusted and unadjusted treatment effects, subgroup and interaction analysis, proportional-hazards assumptions, and time-varying treatment effects.
Gain practical understanding of competing risks, cumulative incidence functions, interpretation of survival analysis results, and best practices for reporting accurate, reproducible, and clinically meaningful time-to-event findings.
Course Syllabus
- What a time-to-event endpoint is
- Survival endpoints — OS, PFS, DFS, EFS and TTP and their event definitions
- The (time, status) pair and why ordinary methods fail
- Right-censoring and its types — administrative, lost-to-follow-up and withdrawal
- Independent (non-informative) censoring and why informative censoring biases everything
- The risk set and the one-row-per-subject data layout
- ATLAS-Lung framing and a worked (time, status) example
- The survival function S(t) and the hazard
- Why we need a non-parametric estimator with censoring
- The product-limit (Kaplan-Meier) intuition
- Building a KM step curve by hand
- Reading a Kaplan-Meier curve — steps, censoring ticks and plateaus
- Median survival and landmark survival probabilities
- Confidence bands and the number-at-risk table
- KM in perspective — pitfalls and limits
- The comparison question and the null hypothesis of whole-curve equality
- Observed vs expected events — the engine
- The log-rank statistic and its chi-square
- The Cox score-test link and the stratified log-rank
- Weighted variants — Gehan-Wilcoxon and the family
- Limitations, proportional hazards and the crossing-curves problem
- Reporting the test and wrap-up
- The hazard function and cumulative hazard
- The Cox proportional-hazards model in plain English
- The hazard ratio and how to interpret it
- Confidence intervals, p-values and reporting the HR
- Adjusting for covariates — adjusted vs unadjusted HR
- HR versus risk ratio versus odds ratio, and the average-HR caveat
- Worked example — an ATLAS-Lung Cox output read line-by-line
- What proportional hazards means
- Why PH matters
- Graphical check 1 — the log(-log) plot
- Graphical check 2 — Schoenfeld residuals
- Time-varying effects and the interaction test
- Common PH-violation patterns — converging, diverging, crossing and delayed effects
- Worked example — decide PH for ATLAS-Lung OS
- What to do when PH fails — stratify, time-varying covariate, RMST
- What a competing risk is
- Why naive 1-KM over-estimates cumulative incidence
- The cumulative incidence function (CIF) via Aalen-Johansen
- Cause-specific vs subdistribution (Fine-Gray) hazards
- Choosing a method, and Gray's test
- When it matters, and why OS is competing-risk-free
- Pitfalls, reporting and wrap-up
- Reading a KM figure as a non-programmer
- Read-along — the ATLAS-Lung OS Kaplan-Meier curve
- Reading a forest plot
- Forest read-along and subgroup discipline
- What to report for a CSR or publication — median, HR and 95% CI, p-value, events, number-at-risk
- Common misreadings to avoid
- Reporting checklists and the clinician script
- How survival threads the whole Biostatistics category
- Events drive power, not subjects
- Sizing with the Schoenfeld events formula
- From events to subjects — accrual, follow-up and dropout
- Interim looks and group-sequential design
- Specifying the estimand in a statistical analysis plan
- Endpoint and censoring rules
- Primary model, PH handling, sensitivity and multiplicity
- The journey, synthesised — describe, compare, model, check, competing risks, report, size and specify
- The one-page how to read and report a survival analysis checklist
- The mindset — events drive power; a single HR needs PH; censoring must be non-informative; always read the number-at-risk
- Where the standards and tools live
- How this connects to Statistics for Clinical Research, Sample Size Calculations and SAPs
- 📘 Bonus: Survival Analysis 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 E3 (clinical study reports) and CONSORT 2010 reporting standards for time-to-event endpoints, as our training courses are constantly monitored, reviewed and updated.
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.
Our Certified Customers
Learner Rating & Reviews
4.7
★★★★★★
Average Rating
536 global ratings
★★★★★
87.0%
★★★★★
5.0%
★★★★★
3.0%
★★★★★
3.0%
★★★★★
2.0%
