What feeling do you have, when you start a new clinical trial? Do you hope that all problems remain in the past? Everything must be better from now? You take a box of tablets not destroyed after last year’s terminated study, containing placebo… you think…
Why Clinical Trials Fail? To answer this question the author has conducted surveys among clinical research specialists and accomplished analysis of publications and best practices of EMA and FDA. So keeping the long story short, let us start with a question: Actually what means “failure in a clinical trial”? A failure in clinical research usually occurs in two dimensions:
- Failing in a project, e.g. the budget has been overspent, targets have not been achieved, and deadlines have not been met.
- Failing in a research project, e.g. not reaching statistical significance in a research area and thus failed to prove the efficacy of a medication or to obtain controversial results.
It is obvious that these two dimensions are two very large domains. Therefore, let us agree on that, regarding the “research” part we have the following initial conditions:
- A candidate drug is safe and efficacious;
- The study design is adequate;
- The study is conducted according to Good Clinical Practice;
And let us focus more on the project management issues. According to experts’ feedback, the most common issue during a clinical trial is a project manager’s skill set.
1. Unskilled Project Manager
Among the most popular “sins” of a project manager are lack of risk-management forecasting and poor motivation of a team. The background of this problem is that often project managers move into a project management role from a CRA role with little to no experience in scoping out and costing clinical trials or, in fact, any larger project. This leads to weak planning and failure in the implementation. Timelines are defined unrealistically, and key persons are not involved in decision-making. All this turns into absenteeism of team members, and the group becomes unproductive.
2. Unproductive Team
A project manager alone cannot achieve anything without a team. Quite a few factors lead towards to team inefficiency. Among them are: low motivation, lack of experience in cooperation and personnel turnover. Employment turnover is one of the major contributors. Nowadays, in the pharmaceutical industry, there is a dangerous trend: switching from one company to another within a short period. If a team, which launches a study is not the team, who planned it – the problems in a study are already preprogrammed. In addition, to lose a team member like an investigator or CRA during a trial is as if a pilot jumps from an airplane and lets the passengers continue flying. The expertise from the company leaks with a person who leaves.
Additionally, weakness in team skills disturbs productive mood. E.g. if the opinion of a Leading Investigator has too much weight during the study setup, then the team’s motivation drops dramatically. The outcome of this is – low motivation, no desire to continue participation in a trial, or questionable data quality. A weak team can hardly deal with a protocol complexity.
3. Complexity of Protocol
Trying to ‘answer’ too many questions in one single trial is a usual problem, which leads to a study failure. A usual indicator of over-complexity is if a large number of protocol amendments follow to remove half of the initial endpoints.
4. Dilemma ‘Project completion targets’ vs. ‘Eligibility of the volunteers’
The situation arises, where the project management meets the restrictions of research. On the one hand, sites receive recruiting targets; on the other hand, they face limitations of eligibility of volunteers. Often it is a field full of conflicts among CROs and sites. The outcome of this is – low motivation, no desire to continue participation in a trial, no or questionable data quality.
5. Poor training & poor verification
Another reason for failure is usually the lack of well-structured training of study sites. Only a competence-based training and competence-based verification guarantees well-designed standards and reduces study variability. Variability in measuring procedures may be critical for clinical studies. It increases the confidence intervals of key indicators and introduces risks of obtaining results of statistical insignificance. Poor training results in ethical issues and poor data quality.
6. Ethical Issues
Not everybody in a trial sometimes understands the “value of honesty”. From time to time, it happens consciously, sometimes subconsciously. In the first case – it is fraud, in the second case – sloppiness. Ethical issues introduce a huge risk of trial failure, danger to the reputation of pharmaceutical companies, CRO´s and pharmaceutical physicians. A gain in the short run may be a loss in a long project.
7. Data Quality
Clinical data is the key evidence and main result of any clinical trial. Therefore, the quality of data is the key element of any clinical trial. The absence of data quality of monitoring during a clinical study may have very regrettable consequences. Par example, the introduction of noise into data entries by miscalibration of measurement devices or misunderstanding of measurement methods negatively influence on the resulting confidence intervals and therefore on trial outcomes.
Some companies offer a regular manual statistical quality check of clinical raw data; others offer more statistical data-surveillance service during a clinical trial. This is like an “early smoke detector” in the study before the real fire breaks out. One of these systems, which recently appeared new to the market is an RBM solution EarlyBird. Nevertheless, even if all negative factors are eliminated, for a successful study a bit of luck is necessary as well. Some project managers call it “The Luck Factor”. Often it comes with experience, with the corporate culture and frequently without an explanation at all.
Independently from the complexity of a project, the author wishes you a serenity to accept the things, which you cannot change, the courage to change the things you can, and the wisdom to know where the difference is.