Interventional studies Flashcards
All interventional studies have
Phases
Differentiators of phases
Purpose/focus
Population studied (healthy/diseased)
Sample size
Duration (depends on disease)
Pre-clinical stage
“Bench” or animal research
Phase 0
- Assess drug-target actions and possibly pharacokinetics in single or first few doses (first in-human use)
- Healthy or diseased volunteers
- Very small sample size (<20)
- Very short duration (single dose/few days)
Safety and efficacy not seen here
Phase 1
- Assess safety/tolerance and pharmacokinetics of one or more dosages
- Healthy or diseased volunteers
- Small N (20-80)
- Short duration (few weeks)
Phase 2
- Assess effectiveness (continue to assess safety)
- Diseased volunteers (NO HEALTHY SUBJECTS, may have narrow inclusion criteria)
- Larger N (100-300)
- Short to medium duration (few weeks-few months)
Phase 3
- Assess effectiveness and safety
- Disease volunteers (NO HEALTHY subjects, may expand inclusion criteria)
- Larger N (500-3000)
- Longer duration (few months to a year+)
If company is using study to submit to FDA for approval, it is phase 3
Phase 4
Post FDA approval
- Assess long term safety, effectiveness, optimal use
- Diseased volunteers (expand selection criteria)
- Population few hundred to few hundred thousand
- Wide range of durations
Advantages of interventional trials
Can demonstrate causation
Only study designs used by “FDA” for approval process
Disadvantages of interventional trials
Cost
Complexity/time
Ethical considerations
Generalizability/external validity
Explanatory (pragmatic) trials
Patients can switch drugs in middle of study
Simple interventional design
Divides subjects exclusively into 2+ groups
Commonly used to test a single hypothesis at a time
Only ONE randomization step
Factorial interventional design
Randomizes subjects into 2+ groups and then further randomizes each group into 2+ sub groups
Used to test multiple hypotheses at same time
Pros/cons factorial design
Improves efficiency for answering clinical questions
Increases study sample size
Increases complexity (may be a barrier to recruitment)
Increases risk of dropouts
May restrict generalizability
Parallel interventional design
Groups simultaneously and exclusively managed
No switching of intervention groups after initial randomization
All simple/factorial study designs are also parallel
Cross-over interventional design
Groups serve as their own control by crossing over from one intervention to another during study
-allows for smaller total N
Run-in/Lead-in phase
Before study begins All study subjects blindly given one or more placebos for initial therapy to determine a "new" base-line of disease -Can assess study protocol compliance -Can wash out existing medication -Can determine amount of placebo effect
Disadvantages of cross over design
Only suitable for long term conditions which are not curable or which treatment provides short-term relief
Duration of study for each subject is longer
Carry-over effects during cross over (wash-out required)
Complexity in data analysis
Smaller N requirement only applicable if within-subject variation less than between-subject variation
Patient oriented end points
Death
Stroke
Hospitalization
Disease oriented end points
Blood pressure
Cholesterol
Table 1 customarily used to
Show group characteristics
Blocked randomization
Ensures balance within each intervention group
-when researches want to assure that all groups are equal in size
Stratified randomization
Ensures balance with known confounding variables (i.e., gender, age, race)
Single blind masking
Study subjects not informed what group they are in but researcher know
Open label
No masking/blinding
Double-dummy
More than one placebo is used
Post-hoc sub-group analysis
No considered appropriately if not prospectively planned
Intention to treat
Way of managing drop-outs
- use last known observation for all subsequent, yet missed assessments
- convert all subsequent yet missed assessments to a null-effect (no benefit)
Intention to treat results in
Preserves randomization process
Preserves baseline characteristics and group balance at baseline
Maintains statistical power
Techniques to manage drop-outs
Intention to treat
Ignore them - Per protocol or efficacy analysis
Treating them “as treated” - allow subjects to switch groups and be evaluated in whichever one they are iin
Impact of Per-protocol technique
Biases estimates of effect- commonly overestimates
Reduces generalizability
Methods of assessing compliance
Drug levels
Pill counts at each visit
Bottle counter tops
Methods of improving compliance
Frequent follow ups
Treatment alarms
Medication blister packs/dosage containers
