This work is designed to support trial statisticians in making informed, transparent choices in their analysis plans.

We hope it helps improve clarity and impact in trial reporting.

#ClinicalTrials #Estimands #StatisticalMethods #RCTs #QualityOfLife #CancerResearch

Using real cancer trial data, we show how aligning estimators with estimands leads to more meaningful insights for patients, clinicians, and researchers.

This study explores:
✅ How different statistical methods (estimators) can be used to estimate different treatment effects
✅ Practical guidance on assumptions and implementation
✅ Why choosing the right estimator for your estimand matters: for clarity, transparency, and relevance

In trials with high mortality—like many cancer studies—outcomes such as quality of life can become undefined after death.

This creates complex challenges for designing, analysing, and interpreting results.