Those who have worked on clinical trials for many years probably remember the rigidity of the clinical protocols and how often clinicians complained that the study design is out of touch with reality. The new tendency of using innovative clinical trial designs shows that this message was heard. Clinical research has also changed during the years and now we know a lot more about the complexity of diseases like cancer.
What are the limitations of the standard randomized clinical trials?
In a standard study there will be 2-3 options to choose from, it will randomize 1-5% of the eligible patients who consent and in the next 5 to 10 years it will try to catch up with the recruitment that is falling behind and reduce the amount of protocol violations. Data and Safety Monitoring Committee could potentially request early termination. The data will be analysis according to Intent-to-Treat (ITT) principle – counting all outcomes according to randomization, regardless of changes in adherence. This, of course, will trigger long arguments around the data generated from the study its validity, if the correct group was selected, etc.
What innovative designs could be used in cancer clinical trials?
- Single-Arm Dose-Finding Studies
The purpose of these studies which are common in phase 1 clinical trials is to find the balance between determining the maximum toxicity dose (MTD) and safe treatment of patients where the dose will be close to the unknown MTD so they can have better chance to benefit from the treatment. Normally phase 1 oncology clinical trials involve small number of patients (20-30) and a model-based approach could be used to determine the most appropriate dose. In this case when the drug reaches phase 3 the researchers can used all data from phase 1 and phase 2 – response and toxicity data – to design phase 3 study. The standard settings do not use such approach.
2. Biomarker-Based Personalized Therapies: Development and Testing
This approach involves identifying and using relevant biomarkers (for example, tissue samples from tumor – fixed, fresh or circulating tumor cells). The second step is to identify reliable method to assess these markers and the third set is to design clinical trials that support development and verification of personalized therapies.
3. Seamless Phase 2-3 Randomized Clinical Trials
Small single-arm phase 2 cancer clinical trials cannot use big resources until there is more reliable data that the treatment has potential to be successful. In such cases it may be useful to include phase 2 study as an internal pilot of the confirmatory phase 3 trial.
4. Comparative effectiveness research studies: Equipoise-Stratified Randomization
The authors of the paper discuss STAR*D study, which offers different treatment options for patients with depression. The study offered 7 possible treatment options and for those patients who did not achieve satisfactory results there were 4 switch options and 3 augment options which allowed clinicians to select the best option for their patients.
5. Comparative effectiveness research studies: Sequential Multiple-Assignment Randomization (SMAR)
Using the same study as above for example the authors present another option for adaptive study design but in this case the treatment is adaptive. In order to improve the outcome of the treatment patients were randomized on different treatment options based on their medical history and response to previous treatments.
6. Comparative effectiveness research studies: Embedded Experiments to Close the Knowledge-Action Gap
This approach supports the idea that patients are randomized on the treatment which is superior to the standard of care. One of the challenges of new treatments is that they are implemented in clinical practice very slow and the purpose of this approach is to provide clinicians with more reassurance that the treatment will offer better outcome for this patients.
There is a clear need for changes in protocol designs to align them with clinical practice and provide more flexibility for patients and clinicians.
Author: Olga Peycheva
Olga is a clinical research professional who has been working in clinical research since 2005. She has extensive experience in clinical research in Eastern and Western Europe.
Originally published on 9 Jan 2019