From study design to screening tests — the statistics behind cancer evidence, in three focused sessions.
This first run distils the foundations of Good Statistical Practice in Oncology into three focused Saturday sessions. Practising and trainee oncologists learn to classify study designs and spot the biases that mislead cancer research, turn messy clinical and registry data into an analysis-ready "Table 1", and evaluate diagnostic and screening tests properly. Every session ends with a hands-on task, so the methods are practised, not just heard — and the same course runs again for future cohorts.
The trustworthiness of a cancer study rests as much on how its statistics are planned, run and reported as on the clinical question itself — the right design and endpoint, an honest reckoning with bias, confounding and missing data, and transparent reporting. Good Statistical Practice (GSP) is the set of habits that get this right. This course teaches those habits in oncology terms, so you can both run the analyses yourself and judge whether a published trial did them well — not just which test to click.
If you read oncology trials, contribute to them, or run your own analyses and want to do it well, this course is pitched at you.
A compact foundations course delivered across three Saturdays. Take all three at the course rate, or pick the sessions you need. Each session ends with a short MCQ quiz — pass all three to earn your certificate.
| Session | Date | Topic | Tasks |
|---|---|---|---|
| 01 | Sat 1 Aug 2026 | Biostatistics in Oncology & Study Designs | 2 |
| 02 | Sat 5 Sep 2026 | Cancer Data & Exploratory Analysis | 2 |
| 03 | Sat 3 Oct 2026 | Diagnostic & Screening Test Evaluation | 2 |
The groundwork, framed in oncology terms.
The oncology evidence hierarchy; observational versus experimental designs; and how bias, confounding and immortal-time bias arise in cancer research.
Clinical, pathology, registry and genomic data types; building an analysis-ready dataset; and constructing a clear baseline "Table 1".
Sensitivity, specificity, PPV/NPV and likelihood ratios; ROC curves for tumour markers; and lead-time and length-time bias in screening.
Take all three sessions at the course rate and save.
Register for the full course — ₹600Run entirely online over live video and recorded for catch-up — no travel, with a Saturday-afternoon IST slot that suits most time zones.
Six guided tasks — two per session — each built on real or realistic oncology data.
Each session is anchored in familiar oncology examples, keeping the statistics concrete and clinically motivated.
A single oncology dataset runs across the three sessions, so you see the same patients analysed several different ways.
Demonstrations are in R; the statistical content maps directly onto SPSS or Stata for those who prefer them.
Each session closes with a short multiple-choice quiz. Attend all three sessions and pass the quizzes to receive your StatsCure certificate of completion.
This is the first run of a course we'll repeat — future cohorts will move on to further blocks of the wider oncology syllabus.
Yes. Every session is delivered live and recorded, so you can catch up or revisit it later. You get access to the recording for the session(s) you register for.
Yes. You can still register for a past session and work through its recording, tasks and MCQ — so it can still count toward your certificate.
Take all three sessions and pass the short MCQ quiz at the end of each. The certificate of completion is issued by StatsCure Network.
No. R is demonstrated step by step, and the statistical ideas map onto SPSS or Stata if you prefer them. No prior programming is assumed.
Register, then pay online in one step via Razorpay, or by UPI QR. Take the whole course for ₹600, or pay ₹250 for individual sessions.
Email admin@statscure.co.in before your session date and we'll do our best to arrange a transfer or refund.
Reserve your seat for all three sessions, or start with the one that interests you most. Registration takes a minute; pay online in one step via Razorpay, or by UPI QR.