Accelerate the speed and cut the costs of Clinical Trials using SAS® Clinical Enrollment Simulation

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Is that a good site?

This is a question most frequently asked when designing an Enrollment plan for clinical trials, and determining the answer without analytics to help may be a daunting task for sure.

Before we dive too deeply into the solution, lets figure out what we are trying to solve for.

Clinical trials ultimately aim to answer two simple questions:

Is the drug or device safe? and
Does it do what it’s supposed to do?

This can take decades and cost astronomical amounts…take the case of Tamoxifen for Breast Cancer

A clinical trial started in January of 1994 and is projected to complete only in September of 2024 being conducted by the UK’s Queen Mary University of London.

The trial enrolled an astonishing 7,154 women aged 35-70 years across nine countries to investigate if tamoxifen, a hormonal drug, can prevent the development of breast cancer in high-risk women.

And herein lies the opportunity.

Question: How can I accelerate the speed and cut the cost of Clinical Trials.

Answer: Simply by using the analytics that is embedded in the SAS® Clinical Enrollment Simulation product to create your Enrollment Plans for your clinical trial.

But it will be daunting and complex to build an executable enrollment strategy. The factors used as input are myriad and complex.

The graphic below details the factors that is typically considered when building a representative and executable Enrollment Plan.

Select any image to see a larger version.
Mobile users: To view the images, select the "Full" version at the bottom of the page.

These factors are the things that, when manipulated and fine tuned just the right amount, ensure that your Enrollment Plan is on budget and in time.

Why is manipulation of the input variables important?

Some estimations from Statista.com put the cost per clinical trial participant at upwards of $30,000. Dependent on the clinical trial that is. The graphic below shows some clinical trial costs running into the multiple hundreds of thousands dollars. Phew!.

That means that if we take the lowest cost of $30,000 per patient, the Tamoxifen Breast Cancer study mentioned in the opening has cost 7,154 times $30,000, so $215 odd hundred million so far. That is not a number, it is a SOUND... something like aaaaaaaghhhhhh!.

Two approaches to manipulating the input variables

There are two approaches ways to manipulate the factors that drives the success of a Enrollment Plan. Hint: only one of them is flexible and simple.

Projecting Enrollment approach (a.k.a - Guesswork based on previous data)
Simulate Enrollment approach (a.k.a – Get statistics to optimize the best potential success)

Projecting Enrollment Approach

Key challenges from the traditional approach:

1. 1. Working backwards form the goal is really trying to make a scenario that can justify the goal we are trying to hit. It does not always have a chance of actual execution and success.
  2. It uses actual value inputs. No room for variability. Let’s say for instance group A will be ready in 5 months but really, we know it will be sometime between 4.5 and 7. That variation is not able to be considered.
  3. Enrollment decay is not considered. What typically happens: Dr tells you she can plan on enrolling 15 patients per month. You know that is optimistic, so you enter an actual value i.e., 12 to be safe. Again, there is not accounting for actual decay as the low figure is hard coded for the entire time.
  4. Never get anyone enrolled. This is probably an underestimate, and closer to 30% never enroll anyone. The coordinator leaves the clinic, so no one runs the trial. A competing trial starts up and they send their patients to that one. The site just has delays in paperwork and just gives up
  5. It can take a long time to create and compare multiple scenarios to determine the impact of changing country and/or site profiles using Excel, so Cross regional data and costs typically are siloed. This approach provides no measure of risk – for completing the trial on time or on budget.
  6. What-if scenarios. No insight or corrective measures regarding the actions needed to keep a trial on track.
Simulate Enrollment Approach

SAS® Clinical Enrollment Simulation can accurately simulate and model the complex variabilities and probabilities associated with enrolling subjects in clinical trials, including country and site selection using analytics.

The three main benefits of using the SAS solution for running your Clinical Enrollment are detailed in the three graphics below.

The SAS Enrollment Simulator allow you to calculate the chance of success - what I really want to know is “am I going to hit my targets.”

Simulation allows you specify what targets matter, then we can look at what we know about the sites and see how likely we are to achieve the study goals.

The SAS Enrollment Simulator allow you to compare various scenarios with the Milestone achievement capability.

This allows us to see ‘what happens if’. We can play with different scenarios to give us the biggest likelihood of achieving success in our clinical trial.

The SAS Enrollment Simulator allow you to intervene at any time and play out scenarios for intervention when you need to ‘rescue’ a clinical trial site.

For instance, in this scenario we can see that by adding two extra ‘rescue sites’ bumps my chances of getting back on target to 75% and adding three to 95%.

This allow me to make a business decision to see whether spending the money will help me be successful or not, and by what percentage point.