1. A simple method to estimate survival trajectories Dr. Matt Williams ICHNT & IC E-Oncology Feb 2015 Matthew.williams2@imperial.nhs.uk

2. The need 331 000 people diagnosed a year with cancer (UK, 2011) 161 823 cancer deaths (UK, 2012) Lung, bowel, breast, prostate – 54% cases, 46% of deaths May be diagnosed with incurable disease (e.g. Lung) May initially be treated curatively, and then relapse (e.g Breast) Once diagnosed with “incurable” disease, may still be treated and live for many years CRUK Cancer Stats (2015)

3. Talking about survival Curable/ incurable “In the long run, we are all dead” Prognostic factors Bias survival one way or another Patient & Tumour factors (age, fitness; tumour grade, molecular aspects) Clinicians are bad at predicting prognosis Survival remains a process, of a population, over time JM Keynes, 1923

4. Looking at survival Williams et al., Clin Onc, 2013

5. How can we characterise survival? Stockler et al., BJC, 2006

6. If it is exponential Constant risk E.g. radioactive decay Multiples of the median estimate other points 75% ~ median/2 25% ~ median x 2 Assumes no cure No conditional survival

7. Metastatic Colorectal cancer Trials of patients receiving first-line palliative chemotherapy 2000 – 2011, phase III, 2+ regimens, 100+ pts per arm, 75% of pts had died 46 trials 96 curves for analysis 96 points at 90%, 75%, 25%; 54 points at 10% Obtained median survival Median/4; median/2; median * 2; median *3 Agreement defined calculated being 0.75 – 1.33 actual figure

9. Metastatic Colorectal cancer 46 trials; 29 011 patients Median OS 16.8 (IQR: 14.3 – 19.4) 342 data points 301 (88%) acceptable Worst agreement at 90% level (76% agreement) Tendency to underestimate time to 90% and 75%, over-estimate to 25% and 10% Williams et al., Ann Onc, 2014

10. Related work Breast, Lung and Prostate cancer We now have data on cancer accounting for ~ half all cancer deaths GBM in progress Clinicians aren’t accurate, but are good enough at estimating the median survival We are discussing collaboration with Sydney group Kiely et al., JCO 2013 Kiely et al., JCO, 2011 Kiely et al., Lung Cancer, 2012 West et al., EJC, 2014

11. Computational aspects Very simple computation ! (1/4, ½, *2, *3) Based on a mathematical understanding of an empirical observation Widely applicable Helps us think about clinical practice Orthogonal to other prognostic tools Better prognostic estimates improve estimates of the median

12. Thanks Anna Lerner & Ramsay Singer Martin Utley (UCL) for discussion ICHNT & ICRUK centre supporting my work