Rationale and Design Section
Experimental studies are used to investigate the role of some agent or intervention in the causation, prevention, or treatment of a disease or outcome. The investigators assign patients to either receive (or not receive) the intervention, which is the most distinguishing differentiation between an experimental study and an observational study.
More can also be learned about experimental studies in both STAT 503 and STAT 509
Design considerations Section
Equipoise
In order to conduct an experimental study, equipoise must exist. Equipoise exists when there is legitimate uncertainty about the benefit of the intervention.
Type of intervention (note: this is not an exhaustive list!)
- Drug
- Educational
- Behavioral
- Environmental
- Organizational (clinic level, hospital level)
Treatment assignment and randomization
Random assignment is the ideal way to assign treatment conditions because it is less prone to bias. With a large enough sample size, researchers can feel confident that randomization controls for both known and unknown confounders
- Methods
- Simple randomization - if the planned sample size for a study is n=100, a randomization scheme for assignment can be created. But it is possible that the computer-generated sample assignment could be that the first 50 patients are assigned control, and the last 50 are assigned to the treatment arm. This is not an ideal randomization scheme. To combat this, one can use blocking and/or stratification.
- Blocking - for the planned sample size of 100, one might choose to use block randomization with blocks of size 20. So, for patients 1-20, we’d know that there would be 10 patients assigned to the control arm and 10 to the treatment arm. This would be repeated for the next 4 sets of 20 enrolled patients. This method keeps randomization balanced over the course of the study enrollment.
- Stratification - There may be one variable that we believe is likely to be a strong confounder and we want to make sure it is balanced between arms. Thus, we perform randomization separately within each stratum.
- Assignment - the intervention can be assigned at either the individual level or at a cluster level
- Individual level - Each participant in the study is assigned to their experimental condition separately. When the treatment is a specific medication or educational/behavioral material given to the participant, this usually works fine. If, however, the study is being conducted in a medical center, and clinicians see patients in both the treatment and control arms of a study, there might be a spillover of the intervention into all patients.
- Cluster level - experimental conditions are assigned at a cluster/group level. In a hospital, this may be when clinicians are assigned to a certain condition, and thus all patients they see receive the same intervention. Or when many hospitals are selected for a trial, and the entire hospital does or does not receive the intervention. In a school, this could be at the classroom, grade, or even school level.
- Randomization
Random assignment is the ideal way to assign treatment conditions because it is less prone to bias. With a large enough sample size, researchers can feel confident that randomization controls for both known and unknown confounders- Methods
- Simple randomization - if the planned sample size for a study is n=100, a randomization scheme for assignment can be created. But it is possible that the computer-generated sample assignment could be that the first 50 patients are assigned control, and the last 50 are assigned to the treatment arm. This is not an ideal randomization scheme. To combat this, one can use blocking and/or stratification.
- Blocking - for the planned sample size of 100, one might choose to use block randomization with blocks of size 20. So, for patients 1-20, we’d know that there would be 10 patients assigned to the control arm and 10 to the treatment arm. This would be repeated for the next 4 sets of 20 enrolled patients. This method keeps randomization balanced over the course of the study enrollment.
- Stratification - There may be one variable that we believe is likely to be a strong confounder and we want to make sure it is balanced between arms. Thus, we perform randomization separately within each stratum.
- Methods
Masking
Masking can be done in various forms to help ensure that experimental conditions are as identical as possible between study arms. If participants and/or investigators know who is receiving which interventions they can be biased in their assessment of its effect.
- Methods
- Drug trials often use placebo pills which look exactly the same, but do not contain the active ingredient of interest
- Sham procedures can be used that resemble the treatment, but this can be hard based on the nature of the procedure (ie. surgery)
- Trials that are evaluating educational interventions can provide the treatment group with all the official study material, but also provide the control group a basic pamphlet that may already be around the clinic so that both groups receive some reading material.
- Outcome Assessment - when an assessment of outcomes is subjective, it is very important to employ masking, and this can be done at different levels
- Single-masked: participants but not investigators are masked. If patients are the ones providing the assessment (via self-reports), this is usually sufficient.
- Double-masked: both participants and investigators are masked. If investigators are also tasked with providing some subjective report of efficacy it is best to keep investigators masked as well
- Triple-masked: participants, investigators, and monitoring committee members are all masked. This situation arises for larger trials when it is important to monitor serious adverse or beneficial events and possibly recommend an early ending for the study.
Advantages & Disadvantages Section
Advantages
- Can control study conditions to isolate effect of interest
- Random assignment to study conditions can eliminate baseline differences between groups
- Use of placebo controls can allow masking which prevents biased ascertainment of outcome measures
Disadvantages
- Can be very expensive
- Patients may be unwilling to be “guinea pigs” for science
- Ethical issues - only permissible when there is a state of equipoise in the medical community
- Placebo controls vs another effective treatment as control
- Patients may be non-compliant with treatment regimen
- Eligibility can be strict and not allow generalization back to all real-world scenarios
Examples in Research Section
Effect of Clinical Decision Support With Audit and Feedback on Prevention of Acute Kidney Injury in Patients Undergoing Coronary Angiography: A Randomized Clinical Trial | Acute Kidney Injury | JAMA | JAMA Network
Design elements include:
- Educational and organizational intervention
- Cluster randomized
- No masking
Design elements include:
- Medical device intervention (intervention arm= fully magnetically levitated centrifugal-flow, control arm = axial-flow LVAD
- Stratified randomization within each center, 1:1 randomization
- No masking