"Statistics in Clinical Trials Unravelled" — a Webinar Series by StatsCure in association with CRSF.
RegisterLearn survival analysis hacks to outsmart time in clinical trials.
Crack missing data challenges with powerful imputation strategies.
Explore health economics to balance cost and care in drug trials.
Decode messy trial data using mixed models like a pro.
From hazard ratios to trial design — statistics in the language of cancer medicine. Twenty-six fortnightly sessions on Saturdays (3:00–5:00 PM IST), 56 guided tasks, one capstone.
Or register per session (₹200) from inside the programme page.
| Session | Content |
|---|---|
| Updates | MMRM's kicking LOCF's butt for repeated measures. Cluster trials are blowing up. Takeaway: Mixed models decode data. |
| Platforms | R's lme4, nlme, brms handle fixed effects, random effects, clustering. Takeaway: R makes messy data easy. |
| Strategy | Lean into MMRM, nail clustering for clear insights from chaotic designs. Takeaway: Right model, right insights. |
| Latest Story | A mental health trial used lme4 to dig up treatment effects in clusters. Takeaway: Mixed models turn noise to signal. |
| Key Takeaway | Mixed Models for Repeated Measures (MMRM) outperform traditional methods like LOCF by handling missing and correlated data. |
| Session | Content |
|---|---|
| Foundations | Kaplan–Meier, log-rank, and reading survival curves the right way. Takeaway: See the data, not just p-values. |
| Cox Models | Hazard ratios explained without hand-waving; assumptions, checks, and pitfalls in oncology data. |
| Competing Risks | When death from another cause changes the picture — cumulative incidence, Fine-Gray. |
| Landmarking | Dynamic prediction: updating risk as the patient's story unfolds. |
| Key Takeaway | Survival analysis done right lets you defend every hazard ratio you report. |