Revolutionizing Clinical Trials using Machine Learning

Clinical trials today: Expensive, difficult, and ripe for disruption

Since their initial use in the 1940s, randomized controlled trials (RCTs) have become the gold-standard supporting the practice of evidence-based medicine [1]. However, increasing complexity of regulations and protocols mean they are both expensive and difficult to run: they cost upwards of $33M and take years to produce results [2,3]. Restrictive inclusion criteria also mean that half of clinical trials exclude more than 75% of patients they aim to treat [4]. Yet RCTs remain the foundation of modern medicine and more than 1,800 trials are commenced every year.

Although novel approaches to clinical trial design have emerged [5]—like decentralized trials, e-consent, and various flavors of adaptive designs—conventional RCTs have remained the dominant approach despite their acknowledged flaws. This situation presents a huge opportunity for innovation. Given the scale at which clinical trials are operated, even small improvements to how clinical trials are run could have tremendous impact on healthcare.

Challenges felt throughout the clinical development journey

The challenges of conducting an RCT may be considered in four stages : (Stage 1) planning of a clinical trial that targets the new treatment, (Stage 2) conduct of the planned trial, (Stage 3) analysis of the results obtained from the trial, and (Stage 4) clinical-use of the treatment if the trial has been successful. These four stages are not to be confused with the conventional phase (Phase I–IV) of drug development [6]. All four stages apply to clinical studies undertaken during each Phase: planning, analysis, and conduct apply mainly to Phase I–III while clinical-use mostly involves Phase III and beyond.

In order to better define the problem, we have explored the challenges involved in each stage with clinicians during our Revolutionizing Healthcare sessions (available here for the first session and the second session). This has identified unique challenges relevant to each stage, summarized below. Broadly speaking, planning requires synthesizing information from a diverse range of sources such as observational or pre-clinical data; conduct entails making decisions about who to recruit or at what dosage to administer a drug; analysis deals with complex inference problems related to risks and outcomes; and commercialization relies on intelligent modeling of various interdependent processes, from how diseases progress to how clinicians behave when prescribing treatments. An important—and continually overlooked—challenge is how the result of one trial may inform planning, conduct and analysis of the next, for example by suggesting broader applications of a successful treatment, or indicating how recruitment for a group of conditions might be optimized or stratified.

Machine learning can already help …but there is also work to be done

Machine learning forms a strong foundation for one to start tackling these challenges; it provides the fundamental tools and techniques that are necessary to precisely formulate what is needed and reason about potential solutions. As we mention later, there are already methods that can help improve trials, mainly by enabling better ways of harnessing available information to make data-driven decisions that make successful studies more likely. But of course, tools and techniques available now are not always able to fully capture the unique nature of each challenge, hence they need to continue to evolve. This need presents an opportunity for machine learning to grow further as well, together with the next-generation trials it enables.

An example: “Identifying good subpopulations fast with confidence”

Consider again the stage of clinical trial conduct. Adaptive clinical trials aim to make dynamic adjustments to how a trial is conducted—for instance, regarding the targeted population or the dosage of the treatment—according to the data collected during the trial itself [7]. As such, an adaptive trial deals predominantly with making on-the-go decisions based on limited information available at the time.

Reinforcement learning and multi-armed bandit literature lay the foundations of online decision making in uncertain environments, as in conducting adaptive trials. While many methodologies developed are applicable to clinical trials, decisions made during an adaptive trial also bring additional constraints to this setting that are not previously considered: for example, clinical trials are often limited both by budget as well as requirements for confidence in inferred results. Although decision making either with a fixed budget or a fixed confidence requirement is very well understood, the case where both constraints are present at the same time and what is achievable then remains largely unexplored (see our recent paper on the subject).

To move towards potential solutions, we have identified the subfield of machine learning whose concepts are best suited to address each challenge. This categorization forms an initial road map for machine learning to start revolutionizing clinical trials.

Our contributions so far

We have already proposed initial solutions for many of these challenges (highlighted in black as opposed to light gray) as summarized in the figure below. For a more detailed discussion of these works, please refer to our research pillars on adaptive clinical trials and individualized treatment effect inference.