Predicting population-level rwTTD for lung cancer and advanced
head and neck cancer treatment using pembrolizumab
We tested the above algorithm in the context of lung cancer treatment
and head and neck cancer treatment using pembrolizumab (for cohort
selection please see Methods ). rwTTD, the duration between the
first dosing to the last administration are defined by the following
three criteria: a. switch to a different treatment: This is an event
point, and rwTTD is defined between the first dosing to the last
available administration. b. death: This is also an event point, and
rwTTD is defined between the first dosing to the death date. c. With a
gap >= 120 days between last known administration and last
known activity: This is an event point, and rwTTD is defined between the
first dose to the last known available administration. If none of the
above happens, the data point is considered as censored (no data after
last administration date or the gap is < 120 days).
We carried out three evaluation experiments (Fig. S14 ). The
first two experiments used advanced lung cancer data and examined the
performance of prediction rwTTD in this homogeneous population. In the
first experiment, we randomly selected the cutoff time between the first
dose time and the last contact time point (let it be censoring time or
termination time), and uniformly and randomly selected a time in between
as the cutoff time. All information prior to the cutoff date
(observation window) is used to extract feature data (seeMethods ). The time between the cutoff time and the last contact
time point is the time used to calculate the rwTTD curve. Here we are
evaluating the ability of predicting rwTTD given a random length of
observations. In the second experiment, the cutoff date is consistently
30 days after the first dose. Thus, we are evaluating how well we can
predict given 30 days of observation data. The third experiment was
trained with lung cancer data with a random cutoff and tested with head
and neck cancer. Under these three sceneria, we evaluated the
performance of predicting the rwTTD curve.
Overall, we found strong performance for rwTTD in both homogeneous
population and cross-disease prediction tasks (Fig. 5a-c, Fig.
S15-17 ). We observed an average 14.12% 13.15%, 31.59% percent
absolute error rate for random cutoff cross-validation, 30 day cutoff
cross-validation, and cross-disease prediction, respectively. The
cumulative error rates are 23.78%, 18.43%, 34.15% respectively
(Fig. 5d ). Of note, cross-disease errors are expected to be
higher as the patient populations are distinct and can respond to the
drug differently. We further examined the performance at 6, 12, 18, and
24 months, and error rates remained stable within this range
(Fig. 5e ). In Particular, we observed a very low average 50%
terminated ratio date prediction, for only 82.90, 105.33, 81.90 for
random cutoff cross-validation, 30 day cutoff cross-validation, and
cross-disease respectively (Fig. 5f ). These results support
strong performance in real world data even when the model is delivered
to data derived from a different population but share certain
similarities in the EMR data that was collected.