Download A simple method to estimate survival trajectories across time in

A simple method to estimate
survival trajectories
Dr. Matt Williams
ICHNT & IC
E-Oncology
Feb 2015
[email protected]
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)
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
Looking at survival
Williams et al., Clin Onc, 2013
How can we characterise survival?
Stockler et al., BJC, 2006
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
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
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
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
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
Thanks
• Anna Lerner & Ramsay Singer
• Martin Utley (UCL) for discussion
• ICHNT & ICRUK centre supporting my work