Some issues in clinical trials design

A clinical trial is an experiment performed on human beings to measure the efficacy of a new treatment under study. The treatment could be a new drug under study, a new therapy, a surgical procedure, or any new clinical procedure that needs to be approved. Then, clinical trials play a very important role in drugs development and pharmaceutical research, because any new drug or procedure has to pass a thorough examination, often very regulated by the national regulatory drug administration of each country. Like any experiment, it has a strong statistical background in all the design, the recruiting and follow-up of patients, and the analysis of the results.

Conventionally, drug trials are classified into four phases, with each phase having a different purpose:

  •  Phase 1: Determine the potential toxicity.
  •  Phase 2: Preliminary study of efficacy and toxicity.
  •  Phase 3: Final test comparing the drug with a commonly used treatment or a placebo.
  •  Phase 4: Post approval follow-up of patient status.

Usually, the different phases are considered like separate clinical trials. Each phase of the drug approval process can be considered as a separate clinical trial and it requires different statistical analysis.

Phase 1 and 2 cover small to moderate size experiments (20-50 patients) and they are centered in the determination of toxicity and efficacy, so the final aim is to get an estimation of the toxicity-efficacy curve. Usually, different doses are tested in the patients and the measurement of the responses gives an estimation of the optimal dose to ensure the maximum efficiency without producing toxicity. There are many designs for these phases, based in optimality criteria, the use of Markov Chains…
Phase 3 is the longest one in the trial; it can have thousands of patients involved, and is also the most complex. As we stated before, the new treatment is compared against commonly used treatments or a placebo, so we have to assign the different treatments to the patients that start the trial. There is a wide catalogue of phase 3 designs in the literature; an exhaustive review is given in Rosenberger and Lachin (2002). If the drug successfully passes through Phases 1, 2, and 3, it is approved by the regulatory agency. Finally, Phase 4 involves delineating additional information, including monitoring the treatment’s risks, late-developing side-effects, benefits, and optimal use.

In the process of designing a clinical trial we have to deal with different issues. For example, in phase 3, the principal objective is to provide an unbiased comparison of the difference between treatments.  We have to avoid the different biases that appear in the study. These biases can come from patients, physicians or some unknown covariates among other factors. A powerful tool to avoid this problem is the random assignment of patients. This kind of trials are called randomized clinical trials and they use different probability rules in the assignment of treatments to patients. However, randomization alone does not avoid all biases, for example, wherever possible, clinical trials should be double-masked, i.e.,  neither the patient nor the physician should know the treatment that has been allocated to the patient.

Finally, although it is well known the importance of the use of statistical tools to carry out any experiment, in these cases, due to their complicated structure and strict regulation they become essential in order to make rigorous and efficient clinical trials.


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