Designing a study for an investigator-initiated trial (IIT) can be a daunting task. Aside from satisfying the scientific and technical fundamentals, you also have to consider other factors (which may not be obvious) that can impact your study. A proposal with a weak study design will eventually lead to wasted time, effort, and money. This article will guide you through some of the critical steps to help maximize your IIT’s chances of success.
The first and most important piece of your study design is defining the purpose. What is your reason or rationale for proposing this study? What is your hypothesis? Is it a testable hypothesis? Your hypothesis should have strong supporting evidence from previous work or published literature. Make an effort to clearly define your purpose as it will help structure and guide the rest of your study design.
Types of studies
The second step is to identify the type of study that will help answer your hypothesis. Studies can range from as simple as in vitro experiments to more complex clinical trials. The choice entirely depends on your purpose and also the number of available resources at hand. We’ve outlined below some of the most common types of studies for an IIT:
- In vitro studies (lab-based studies) measure the effects of a variable in a highly controlled environment.
- Animal studies, often conducted after successful in vitro studies, test the effects of a variable in a controlled in vivo environment
- Observational studies attempt to observe an independent variable, which is not manipulated by the researcher, in a sample population.
- Surveys collect information from a sample population through questionnaires.
- Clinical studies test the efficacy and safety of a product or procedure in a sample population.
In certain types of study designs, especially in clinical studies, it may be necessary to include a “blinding” criteria to eliminate or reduce experimental biases. Blinding occurs when certain information, which may influence the outcome, is hidden from the researchers, participants, or a third party involved in the study. This can include single-blind (usually the participants are blinded of their treatment group), double-blind (both participants and researchers are blinded), and triple-blind (participants, researchers, and a third party are blinded).
Most scientific studies follow a cause-and-effect design, in which an independent variable X is expected to cause a change in the dependent variable Y. For instance, a researcher proposes to test whether increasing the dosing frequency (independent variable) of an eye drop can reduce dry eye symptoms (dependent variable). In the ideal situation, only one independent variable is manipulated at a time, while other independent variables are kept constant, and the resulting effects are observed. In this case, the observed outcome can be attributed entirely to the independent variable. However, this type of scenario is not realistic and only possible in a highly controlled in vitro environment.
For more complex studies, such as clinical trials, multiple independent variables can vary simultaneously to affect the outcome. In the above example, aside from dosing frequency, other independent factors that could influence the results are the dosing volume, time of dosing, temperature, humidity, and the participants’ age, gender, and ethnicity. As you might imagine, the more complex the study, the more variables you have to account for, and the higher the variability in the results. In some cases, there may be even confounding variables, which are factors that affect the results but were not identified initially. Therefore, most clinical study designs will include a target inclusion/exclusion criteria for participants to help reduce some of the variability.
Finally, one of the most overlooked design elements relating to variables is setting the right negative and positive controls. The results of a study always need to be compared to their respective controls in order to provide useful conclusions. The negative control is the group that receives no treatment, whereas the positive control gets a treatment that is known to produce an effect.
Unless you have access to have unlimited resources, you will need to set specific endpoints for your study. The endpoints act as checkpoints to limit wasted resources and to steer your study in the right direction. To this end, it is always a good idea to break up a study into multiple phases, in which a subsequent phase does not start until certain endpoints or milestones have been reached. For example, an endpoint could be that a treatment does not show any negative side effects in a small group of participants. The following phase of the study could then test the treatment with a larger sample.
In the perfect scenario, the results of your study were obvious enough that we could confidently conclude that a particular variable caused an observed effect. In reality, however, the outcomes for the various test groups may only be marginally different from each other and the controls. For this reason, statistics is a quintessential part of every study design. Choosing the right statistical analysis will help identify whether the observed effects are actually real or just simply due to chance. Unfortunately, the know-how behind statistical testing and analysis can be quite complicated, especially for clinical trials. If you’re not familiar with statistics, we highly recommend you consult a statistician to ensure that you’ve chosen the most optimal statistical tests for your study. We recommend software such as GraphPad for simple statistical analysis and R for more complex analysis. For more information on calculating sample size for a study, please refer to our next article.
By this point in the study design, we’ve covered all of the scientific basis for a well-designed proposal. The next step is to ensure that the logistics of the study are also in order:
- Will your study require regulatory approvals such as ethical approvals?
- Does your study need to follow GLP (Good Lab Practices) and GCP (Good Clinical Practices)?
- For clinical studies, what is the target enrollment? When will it start and end? What do you expect the dropout rate to be?
- What are your plans for publication and disclosure of results?
- Do you have access to the necessary instrumentation and facilities?
- Can your budget support your study design?
Last, but not Least
More often than not, studies will stray from their original plan due to unforeseen circumstances (like a pandemic). Keep this in mind, and always give a generous buffer in your timeline and cost estimates. Two things that sponsors hate to see are a surprise increase in expenses and missed deadlines. A good study plan should therefore always account for potential setbacks and delays. We know that designing a study is no easy task, so reach out for help if you need it. Sengi provides services in drafting, designing, and reviewing your IIT proposals.