Jonathon Hopkins Course Module 1 Flashcards
Phase 1 of clinical trials -how many people are involved?
10-30 healthy volunteers or people with disease
The goal of phase 1?
The goal of phase one trials is to identify a tolerable dose, and also to provide information on drug metabolism and excretion and really gather information on toxicities.
Phase 2 clinical trials?
Phase two studies are slightly larger, they usually have 30 to 100 people. And in phase two we start to collect preliminary information on efficacy, but we continue to collect information on side effects and safety. Phase two trials are sometimes controlled and sometimes uncontrolled.
Phase 3?
Phase three trials are the final approval stage for drug trials. They usually involve a 100 or more people, and the goal is to assess both efficacy and safety. Phase three trials are controlled and almost always randomized.
Phase 4?
Phase four studies are frequently observational, but sometimes they are control trials.
Type of trials that are covered in this course?
- parallel designs
- crossover designs
- group allocation
- factorial designs
- large sample
- equivalency
- non inferiority
- adaptive designs
Comparison structure of a trial?
Thats means that experimental group are beeing compared with a control group.
In that gruop fall parallel, crossover and group allocation.
Why is randomization good in parallel design?
We use randomization to allocate patients because it removes bias in the allocation process which is called selection bias.
What is randomized in crossover design?
in a crossover design we randomize whether they receive A first and then B or B and then A.
The most importatnt sentence for crossover desihn?
each patient serves as his or her own control. This is a nice feature because variability is almost always higher between measurements of an outcome taken on different people, than in repeated measurements taken on the same person.
The result in a reduction of a variability in crossover design?
As a result of the reduction in variability, we need fewer patients to test the hypothesis of interest. So the crossover design is more efficient than the comparable parallel design.
Disadvantages of a crossover design?
- They can not be used for treatments that provide permanent cure.
- Potential carryover effect if washout period is not long enough
- Testing to make sure that there is no carryover effect, which is not powerful test
- Dropouts could be problematic
- Complicated analysis, because you correlate outcomes on the individuals
Examples of crossover designs?
So examples of chronic diseases for which this design is used are asthma, hypertension and sometimes arthritis or other pain relief studies. The treatments in a crossover design as I mentioned must have short term effects and relieve only the signs and symptoms of the disease, but they really shouldn’t have any permanent effect on the underlying disease process
-Crossover designs are also used sometimes when we are in the early phases of studying a drug and we are looking at the metabolic bioavailability or tolerability of a new product.
What could be randomization unit?
Usually we think that a person could be randomized only but for instance, eyes of the same indivual could be radnomized to recieve treatment A or B.
What is group allocation design?
In a group allocation design, the randomization unit is a whole group of individuals, such as a community, or a school, or a clinic. The entire group of individuals is allocated to the same intervention. This type of randomization is also sometimes called cluster randomization.
when we use cluster randomization?
- When individual randomization is not feasable
- contamination
- It’s also important to recognize that if there’s correlation in the responses within a group, and there usually is, this design, the group allocation design, loses some efficiency. In other words, the group allocation design requires more individuals to address a given hypothesis, as compared to a parallel or a crossover design.
Factorial design?
In the factorial design we are testing two or sometimes more experimental interventions simultaneously. So we test treatment A versus the control for treatment A, and we test treatment B versus the control for treatment B.
Reeasons to do factorial designs?
Usually for a economical reasons, rarely to see if there is an interaction.
When we look at a table for factroial design how do we estimate main effecs assuming that there is no interaction and what we do if we want to look for an interaction?
when we do a factorial design we are usually interested in the estimation of the main effects assuming that the treatments do not have an interaction. To make these comparisons, we use the responses of the people and the margins of the table, and we’ll go back to the graph in a moment to review these responses. In the case where we are interested in the interaction, we have to compare the responses in the cells instead of in the margins
Large simple designs?
Many participants to find modest benefit
Large simple designs- interactions?
Another premise to a large, simple design is that there are unlikely to be many treatment interactions. We aren’t likely to have a well-defined subgroup of people that respond well to therapy when others don’t. Given the assumption that it’s unlikely to have treatment interactions, it’s not that important to collect a lot of information about baseline characteristics and interim response variables because we are not expecting to want to look at the treatment effect in lots of different subgroups or examine the mechanism, because we aren’t expecting a group of people to respond very differently from another group of people.
Large simple design-countering a eror
We’ll have less control over the training and standardization of administration of treatment and of outcome assessment, so we have to expect that there will be more error or increased variance in the estimation of the outcome measures. And we counter this increase in error with a large sample size.
Requirements for large simple designs?
- easily administred product
- The treatment shouldn’t require any adjustments to the dose or the timing of the administration, and it also shouldn’t need any ongoing monitoring for adverse events.
- easily-ascertained outcome.
- no complex baseline measurements
- simple data are persuasive enough
Why people use equvivaleny and noninferiority designs?
- These are designs that can be used to compare a new intervention to an established intervention. When we use one of these designs we might think that treatment A is as good as or the same as treatment B for treating or preventing a specific condition. But we believe that the use of treatment A might have some other kind of benefit such as less severe adverse events, or treatment A might be easier to administer than treatment B, or treatment A might be cheaper than treatment B.
- Another use of these designs is to do head-to-head comparisons to two or more established treatments for a specific condition. This uses has been discussed recently quite a bit with respect to comparative effectiveness research.
