In an interesting review recently published on Contemporary Clinical Trials Communications, Fogel DB analyses several factors associated with clinical trial failure by reviewing the relevant literature from the past 30 years, and offers insights on opportunities for improving the likelihood of success.
Clinical trials are challenging, expensive, time-consuming and could represent an important burden for patients. Of note, the costs of failure for a late phase clinical trial correspond not only to the cost associated with the trial itself but also to the cost of all studies previously performed, as well as the cost of lost time pursuing a potentially viable alternative.
The major reason of trial failure has been and remains an inability to demonstrate the efficacy of a new product. As reported by the author, a previous study showed that 54% of 640 phase 3 clinical trials with novel therapeutics failed during clinical development, with 57% of those failing due to inadequate efficacy. Trials on potentially efficacious products can still fail to prove their efficacy due to several reasons, such as a flawed experimental design, an inappropriate statistical endpoint, or simply being underpowered, which may be a consequence of patient dropouts and inadequate enrolment.
The choice of the most appropriate primary objective/endpoint and the definition of a clinically significant result, the method to be used to handle missing data, the adequate length of follow-up and the minimization of protocol deviations are some of the crucial factors that need to be taken into account upfront in a clinical trial design to improve the likelihood of its success.
Eligibility criteria are also a fundamental element of a clinical trial design. The choice of the appropriate inclusion and exclusion criteria to define the study population can affect the likelihood of the trial to recruit, enrol and retain enough subjects to have a chance to meet its primary endpoint (i.e. to be successful), as well as its duration and cost. The study population should be relevant to the real-world population that is intended to benefit from the study product. As highlighted by the author, too specific inclusion criteria can lead to difficulties in finding suitable participants, and may result in a longer enrolment period and higher costs, and eventually in protocol amendments. As a matter of fact, a study measuring the incidence, causing and repercussions of study protocol amendments showed that 16% of protocol amendments are due to changes in eligibility criteria.
As stated by the author, “the potential of protocol amendments can be reduced with better planning and anticipation of the consequences from design choices”.
Artificial Intelligence (AI) techniques, such as Natural Language Processing (NLP), could further help future study protocol development by extracting relevant information in the available literature and presenting systematically-organized data to the study designer for consideration.
Of note, a proper study design (with the most appropriate endpoints, eligibility criteria, statistical methodology and sample size) is not the only factor improving the likelihood of success of a clinical trial.
Read more about this topic in Part II
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