Inspired by this contest to come up with ideas to increase the low amount of patient accrual for cancer clinical trials, I decided to look more into the data. Bold, by the way, is one of my all time favorite books, and was co-authored by the creator of the herox.com website, the xprize Foundation, and co-founder of Planetary Resources: Peter Diamandis. Truly someone to look up to.
Anyways, the premise of the contest is that over 20% of cancer clinical trials don’t complete, so the time and effort spent is wasted. The most common reason for this termination is the clinical trial not being able to recruit enough patients. Just how common is the low accrual reason though? And are there obvious characteristics of clinical trials that can help us better predict which ones will complete successfully, and what does that suggest about building better clinical trial protocols? I saw this as an opportunity to explore an interesting topic, while playing around with the trove of data at clinicaltrials.gov and various data analysis python libraries: seaborn for graphing, scikit-learn for machine learning, and the trusty pandas for data wrangling.
Basic data characteristics
I pulled the trials for a handful of the cancers with the most clinical trials (completed, terminated, and in progress), got around 27,000 trials, and observed the following:
- close to 60% of the studies are based in the US*
- almost 25% of all US based trials ever (finished and in progress) are still recruiting patients
- of those trials that are finished and have results, close to 20% terminated early, and 80% completed successfully (which matches the numbers the contest cited)
- almost 50% of all US based trials are in Phase II, almost 25% are in Phase I
- and interestingly, the termination rate does not differ very significantly across studies in different phases
Next, I was interested in finding out just how common insufficient patient accrual was as a trial termination reason vs. others reasons. This was a little tricky, as clinicaltrials.gov gives principal investigators a free-form text field to enter their termination reason. So “insufficient patient accrual” could be described as “Study closed by PI due to lower than expected accrual” or “The study was stopped due to lack of enrollment”. So I used k-means clustering (after term frequency-inverse document frequency feature extraction) of the termination reasons to find groups of reasons that meant similar things, and then manually de-duped the groups (e.g. combining the “lack of enrollment” and “low accrual” groups into the same group because they meant the same thing).
I found that about 52% of terminated clinical trials end because of insufficient patient accrual. This implies that about 10% of clinical trials that end (either successfully, or because they’re terminated early) do so because they can’t recruit enough patients for the study.
Predicting clinical trial termination?
Clinicaltrials.gov provides a bunch of information on each clinical trial–trial description, recruitment locations, eligibility criteria, phase, sponsor type (industry, institutional, other) to name a few–which begs the question: can this information be used to predict whether a trial will terminate early, specifically because of low patient? Are there visible aspects of a clinical trial that are related to a higher or lower probability that it fails to recruit enough patients? One might think that the complexity of trial eligibility criteria and the number of hospitals from which the trial can recruit from could be related to sufficient patient accrual.
Here was my attempt to get at a solution to this question analytically: fitting/training a logit regression multi class classifier–whether a trial would be “completed”, “terminated because of insufficient accrual”, or “terminated for other reasons”–on a random partition of clinical trial data, and measuring its accuracy at classifying out-of-sample clinical trials. The predictors were of two types: characteristic (e.g. phase, number of locations, sponsor type, etc.) and “textual”, or features extracted from text based data like the study’s description and eligibility criteria. Some of these features came from a similar tf-idf vectorization process as described in the k-means section above, other features were the simple character lengths of these text blocks. Below is a plot showing the relationship between two of these features: length of the eligibility criteria block of text, and length of the study’s title, two metrics that perhaps get at the complexity of a clinical trial.
The result: the logit model could only predict correctly whether trials would complete successfully, terminate because of low accrual, or terminate for other reasons 83.6% of the time. This is a pretty small improvement over saying “I think this trial will complete successfully” to every trial you come across, in which case you would be correct 80.6% of the time (see the Completed vs. Terminated pie chart above). Cancer clinical trials are very diverse, so it makes sense that there don’t seem to be any apparent one-size-fits-all solutions to improving patient accrual.
Nice, you made it this far! Thanks for reading. FYI: I submitted an entry to the contest I mention at the top of this post–if you want to be notified of when voting starts so you can vote for my entry (or not), you can fill this out.