Final 1 Flashcards
What is the goal of a Phase I Trial?
- Determine which dose of drug is safe and most likely to show benefit
- Estimate largest size of a dose before unacceptable toxicity is experienced by patients (Maximally tolerated dose – MTD)
- Start with a low dose and escalate until a prespecified level of toxicity is achieved
Give a brief overview of the 3+3 (Step Up/Step Down) Design
- Rule Based
• Treat 3 participants at dose K
– If no DLT escalate to dose level k+1
– If 2+ DLTs, de-escalate to dose level k-1
– If 1 DLT, treat 3 additional participants at dose level K
— If 1 in 6 DLT, escalate to dose level K+1
— If 2 in 6 DLT, de-escalate to dose K-1
– MTD is the highest dose where 0 or 1 DLT is observed (repeat as needed)
What are some issues with the Step Up-Step Down (3+3 Design)
o Ignores dose history other than previous 3 patients
o Imprecise and inaccurate MTD estimation
o Low probability of selecting true MTD
o High variability in MTD estimates
o Dangerous outcomes
What is the goal of a Phase II Study?
Identify agents with POTENTIAL efficacy (does not test efficacy).
- Discard agents without promise
- OR support continuing the experiment
What is a pilot study? (And what are the two types)?
- Pilot Study: A non-randomized clinical trial to determine if a treatment should be tested in a large RCT
- Phase IIa: Pilot studies designed to demonstrate clinical efficacy or biological activity
- Phase IIb: Studies to determine the optimal dose for biological activity with minimal side effects (for some conditions this is phase I)
How do we determine the sample size of a Pilot/Type II study?
Want a large sample that will have a high prob of detecting any common complications in treatments
- p=1-(1-r)^N
• p = prob of observing at one complication
• r= complication rate
• N = sample size
*Which type of error is a more serious concern in pilot studies?
Type II error is a more serious concern
- Don’t want to reject treatments that offer large patient benefits on the basis of small pilot studies – don’t want to conclude that a new treatment is ineffective when it might be
- Type I errors will be caught in Phase III (theoretically)
What is the formulation of null hypotheses in Futility studies and how does it compare to conventional studies?
o Formulation of the null and alternative hypotheses are reversed. Higher alpha (flip alpha and beta so alpha about 0.2 and beta about 0.05)
o Often there is no comparison arm (single arm study) or sometimes a historical control
o Smaller sample size
o One sided hypothesis – result compared to a pre-specified fixed value
What are the hypotheses in a continuous futility study and how do they compare to a conventional study?
- Control Mean (Cx) + Increase for clinically meaningful (delta) = target threshold. If new treatment mean is less than this, don’t move on to phase III.
- *Figure out how to insert table**
What is the interpretation of errors in a futility study and how does it compare to conventional studies?
- Choose Type I error/Beta: Flip alpha and beta: One Sided alpha – what is the percent that we can tolerate of rejecting an acceptable treatment (0.10 or 0.15)
• Beta – 10-15% - accepting a greater chance of carrying an ineffective treatment forward (since will test in larger study)
Insert Table
What are the hypotheses in a binary futility study and how does it compare to a conventional study?
- Proportion expected to fail in control (P_cx) – reduction in fail considered clinically meaningful (delta) = target threshold
• IF change is greater than P_cx = delta, stop
Insert Table
Describe the different types of control groups for futility studies.
o Historical Controls - Potential for bias – changes in background treatment over time
o Calibration control group – compare calibration group to historical control (small group to test bias of historical controls)
o Controls – If good information on controls is available, may not need any controls or may only need a couple (for randomization/masking)
Describe a Simple Selection Design
- Phase II
- Selecting the best among K treatments to take forward to Phase III based on stat ranking and selection theory
- Sample size estimated to ensure that if that best treatment is superior by at least D, then it will be selected with high probability (ie* 90%)
- Somewhat arbitrary and even the “best” might not carry through
Describe the three types of Selection and Futility/Superiority Designs
- Phase II
- Selection + Superiority (A)
- Multiple Steps
- – Randomize to k treatments + control , Calculate test, if max difference is at least as large as cutoff – proceed; if not, stop
- – Randomize more patients to treatment chose in I and control; calculate weighted test statistic between step one and two; if at least as large as cutoff – reject null. If not, stop.
- If reject, proceed to PIII
- Poor specificity (often terminate early) but rarely choose suboptimal treatment
- Lower sample sizes than some others
- Selection + Superiority (B)
- Similar to A but no control in Stage I
- Selection + Futility
- Includes control and a concurrently control Futility study ; requires simulation
Why do we do Phase II/Pilot Studies?
- General notes on selection designs:
- Efficient way to identify a treatment with the most potential for effectiveness
- Designed to select one best treatment of many in the pilot face
- If determining which treatment-s to move forward with, it will be too limiting
- Why do we do this?
- Good for dose and determining if a drug is worth studying more
- Too many drugs and combos to test all in PIII – long and expensive
- – Long term outcome requires at least 5 years to first interim analysis so can’t assess futility in short term
- Too many drugs and combos to test all in PIII – long and expensive
- Starting with patients and doing P2 to P3 – seamless P2 to P3
- – Hard to recruit and retain because of long commitment (Esp for placebo)
- – Don’t allow for change over time with Standard of Care, etc.
- Starting with patients and doing P2 to P3 – seamless P2 to P3
Define Intraclass correlation and cluster randomization
- People in clusters are more like than people across clusters
- POSITIVE INTRACLASS CORRELATION REDUCES VARIATION AMONG MEMBERS OF THE SAME GROUP
- Total var= var within group + var between groups
- - Var within group = var_y(1-ICC)
- – If you don’t take ICC into account, within group var=overall var
- - Var due to group = var_gc(ICC)
Define the Variance Inflation Factor.
- 1+(n-1)ICC Design inflation factor
- Design Effect (DEFF) = Design inflation factor; is directly proportional with the alpha level of the test. Type I error will increase if we ignored the DEFF since the variance estimate would be biased downwards
- - Variance of group mean in cluster trial is greater in group randomized trial by a factor of DEFF (1 + (n-1)ICC).
- - EXAMPLE Z=(bar(x)-mu_0)/var and var is smaller than it should be than Z will be larger than it should be and therefore the pvalue will be smaller than it should be so type I error increased
What impact would this DEFF have on the computer alpha level of the test if we ignored the DEFF?
Variance estimate will be biased downwards. Variance is in the denominator of Z value. Therefore Z will be larger than it should be. Therefore, p-value will be smaller than it should be. Therefore Type I error increased.
Do we perform matched or unmatched analysis for cluster randomized trials and why?
Even though randomization units are matched, we perform an unmatched analysis to preserve Type I error rate and improve power (could be an issue for small number of pairs and low matching correlation)
What is a non-inferiority/equivalence trial and when would we use one?
- A trial with the primary objective of showing that the response to the investigational product is NOT CLINICALLY INFERIOR to the control
- Non-inferiority studies good for treatments that significantly cheaper, easier, less-risky, etc.
- Non-inferiority can be for efficacy or for safety
- New intervention might have other benefits such as less side effects, simpler, less invasive, lower cost -Willing to accept these advantages but at how great of a cost to efficacy
- Controls: Current gold standard – historical data, placebo, active controls
What is an active control trial?
- A trial in which the experimental intervention is compared with an accepted standard intervention (active control – proven better than placebo). Goal to show efficacy of the new treatment by showing that it’s
• Superior to the active control OR
• As good as the active control
• Based on clinical and statistical judgment (ie* cost/benefit) - Widely used treatment that established efficacy in a well documented superiority trial and the new treatment should show similar efficacy
What is the margin of non-inferiority delta? What is a key concern of it?
- Margin of non-inferiority Delta: specified in protocol, maximum difference in responses between two interventions that is considered clinical unacceptable (ie* ½ or 1/3 of the established superiority) – retain a certain proportion of the active control’s efficacy
• Placebo – 40 % mortality
• AC – 30% mortality
• Margin – ½ then will accept 5% or 35% mortality - Non-inferiority is comparing to an active control but we need to consider the effectiveness of active control as compared to the placebo in choosing the margin
- Have to determine the margin of non-inferiority delta – maximum difference between two interventions that is considered clinically acceptable
– Placebo 40% mortality; Active control 30% mortality; Margin: 35%-30%=5% or 37%-30%=7%? Definitely cannot go to 10% margin because then you’re at placebo. Need to establish what is acceptable.
What are some concerns with the margin of inferiority delta?
- Must be very valid. Clear protocol and carefully, rigorously conducted (minimal drop out, non-compliance, missing data that might bias the results toward the null in a non-inferiority study because they will look more like “placebo” if they’re not taking a treatment). ESPECIALLY IMPORTANT FOR NON-INFERIORITY BECAUSE IT WILL BIAS TOWARDS THE NULL FOR NON-INFERIORITY WHILE IT WOULD BIAS AWAY FROM THE NULL IN EFFECTIVENESS. This matters because it would lead you to be more likely to call a treatment INCREASES PROBABILITY OF TYPE II ERROR.
- Watch for things that might bias towards the null (drop out, non-compliance, missing data) even more so than normal since we don’t want to reject the null in this case
- - Might actually be doing worse – new treatment might cause more deaths but might not be able to detect it because of above (increases prob of type II error).
What is assay sensitivity and what assumptions are necessary?
The ability of a trial to distinguish an effective treatment from a less effective or ineffective treatment.
- C > Placebo is based on previous studies
- A > B > C is non inferiority study
- Is trial internally valid?
- Assumptions:
• Active control is truly effective – if not a less effective intervention may be approved based on the active control test
• Constancy of the control effect – control effect does not change over time (if not needs placebo arm NOT a noninferiority test)