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Thirteen Clinical Trial Design Questions and Answers. Peter A. Lachenbruch DB Cynthia Rask DCEPT. Usual Disclaimer. The views expressed here are those of the authors and may not reflect those of the FDA We have been asked to address 13 questions (unlucky?). Orientation.

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thirteen clinical trial design questions and answers

Thirteen Clinical Trial Design Questions and Answers

Peter A. Lachenbruch

DB

Cynthia Rask

DCEPT

usual disclaimer
Usual Disclaimer
  • The views expressed here are those of the authors and may not reflect those of the FDA
  • We have been asked to address 13 questions (unlucky?)
orientation
Orientation
  • The NIAID requested that we discuss some questions regarding clinical trial design principles. There are many books on the basics of clinical trial design and analysis
    • Pocock
    • Freedman Furberg and DeMets
    • Piantadosi
orientation 2
Orientation (2)
  • FDA has many guidance documents on their web site www.fda.gov Consult these for further details.
  • The International Conference on Harmonization (ICH) has issued many reports that worldwide regulatory agencies will abide by. Web site: www.ich.org
    • See E9, E10, E3, E6, and E5 for particularly useful documents
general statistical ideas
General Statistical Ideas
  • Clarity of approach
    • Full disclosure of design, sample size calculations
    • Analysis methods
    • Distinction between CONFIRMATORY and Exploratory analyses
    • If “new” methods are used, there should be peer-reviewed citation
statistical ideas 2
Statistical Ideas (2)
  • Must be analytically appropriate
    • Maintain size (α level)
    • Maintain blinding as appropriate
  • Have endpoints (outcomes) that are appropriate
    • Show a clinical benefit
    • Reliable and valid
  • Minimize missing data and provide plan for dealing with them when they occur
question 1
Question 1
  • What pitfalls do the FDA see when information from pre-clinical data (or early clinical data on a similar investigational product) is formulated into a Phase I protocol?
slide8
Q1: (1)
  • Safety issues
    • A main use of pre-clinical data is to ascertain basic safety information. If animal data is limited, the FDA may ask for further study
    • The choice of animal model is important – if it is not accepted as appropriate, there may be need to obtain further information or information on another model
slide9
Q1: (2)
  • Need to characterize the product
    • Potency assays
    • Purity
    • Identity
    • All specifications need to have at least a start at understanding leading to full GMP compliance
slide10
Q1: (3)
  • It is recognized that proof of concept studies in pre-clinical studies may be limited. However, there should not be evidence of poorer outcomes than comparators.
slide11
Q2:
  • What are the various types of study designs available when there are more than two comparators?
slide12
Q2:
  • Depends on purpose of study
    • Testing 2 or more dosage levels versus control
      • Parallel group design – may wish to account for ordering of dosages in the analysis
    • Testing both schedule and dose
      • Factorial design (high and low levels of each dose crossed) – allows examination of interactions in the analysis
slide13
Q2: (2)
  • Test amount of adjuvant and dose
    • Factorial design
  • Crossover designs are generally not used in vaccine trials because the immune system is permanently affected (or at least affected for a long time). Thus, carry over effects are always present
slide14
Q3:
  • What controls are appropriate when there cannot be any blinding in the trial?
slide15
Q3:
  • Almost any control is reasonable: placebo, standard of care
  • In some cases, historical controls may be used, but these are rare
  • The endpoint / outcome variable that is being used and how it’s evaluated is most important
    • A subjective endpoint is usually a problem, so we expect that a blinded evaluator will be used.
    • An objective endpoint (e.g., confirmed disease by laboratory measures) is preferable
slide16
Q4:
  • What are the problems seen by the FDA with randomization in trials?
slide17
Q4:
  • Randomization is absolutely essential in vaccine trials
  • Issues
    • Too many strata make is unlikely that there will be sufficient numbers in each stratum for precise estimation. The number of strata is the product of the number of levels in each stratum (Sex (2), Age (4), Ethnicity (3) = 24 strata)
slide18
Q4: (2)
  • Issues (continued)
    • Inadequate number of strata
      • age<2, 2 ≤ age <12, 12 ≤ age < 18, 18 ≤ age < 50, 50 ≤ age often important in vaccine studies
    • Cheating – unblinding the treatment assignment – need to have robust way of preventing this
    • Introduction of bias
    • Not accounting for the design of the study in the analysis – just because you have stratified, you still must account for the stratification in the analysis.
slide19
Q5:
  • What are the steps in designing a dose-ranging/dose-escalation study?
slide20
Q5:
  • Goals:
    • To establish maximum tolerated dose (MTD), dose limiting toxicities, and/or maximum feasible dose
    • To establish minimum effective dose
  • Designs
    • One dose per subject, gradual increase by fixed amount (typically half log increases, with rules for stopping)
      • What’s the right starting dose?
q5 2 dose ranging dose escalation
Q5: (2) Dose ranging / dose escalation
  • Multiple doses per subject for short (3-7 days) or long (1-4 weeks or greater) periods
  • What is range that generates useful levels of antibodies? What level has adverse events?
  • Dose Escalation
    • Give successively larger doses or number of doses (booster doses) until subject responds
    • May not be helpful with vaccines because of permanent effect of a vaccine
slide22
Q5: (3)
  • For a vaccine both dose and schedule need to be determined
    • A factorial design may be useful
      • Test all doses and all schedules
      • Can look for interactions to see if the response is additive or not
      • The specific adjuvant may be important
    • This may be expanded to look at a response surface
      • Useful for first trials to pick a dose-schedule combination for later trials
slide23
Q6:
  • How does one deal with multiple variables that will affect the outcome measure (with an understanding of fixed randomization schemes and adaptive/dynamic randomization schemes)
slide24
Q6:
  • If there are strata, including study sites, these always should be included in the analysis model.
  • Common covariates include (if not made strata) age, sex, ethnicity, disease stage
  • Usual method is to conduct an analysis of covariance – some covariates may not be ordered, often they are
slide25
Q6: (2)
  • The analysis of covariance does an analysis of variance on the adjusted response.
  • Assumptions:
    • Normal distribution of residual error
    • Covariates not affected by treatment – measure at baseline!
    • Parallelism – no interaction of treatment and slope – i.e. the response treatment rate of change is same for all covariate combinations
q6a fixed and adaptive randomization schemes
Q6a: Fixed and Adaptive Randomization Schemes
  • Fixed scheme
    • Same proportion assigned to each treatment group
    • Often 1:1 allocation, but may become 2:1 or 3:1
      • Smallest variance of treatment effect associated with 1:1 allocation, but it may be important to gain understanding of safety profile, so a more extreme allocation may be used. More extreme than 3:1 is not very useful and leads to much larger sample size
q6a 2
Q6a: (2)
  • Here are total sample sizes for α=0.5, =0.1, mean difference=1, =3
    • 1:1 allocation 380
    • 2:1 allocation 429 13% increase
    • 3:1 allocation 508 34% increase
    • 5:1 allocation 684 80% increase
    • Thus, the unbalanced allocation leads to a substantial increase in sample size and consequent budget
q6a 3
Q6a: (3)
  • Adaptive randomization
    • Next randomization depends on outcome of prior subjects in the trial
      • Need fairly early response in trial. May be possible with skin or other reactions (if they occur within a few hours of immunization), a bit less so with immunogenicity (outcome after first series of immunizations that may take 6 months), unlikely with clinical outcome (occurs after series of immunizations and a relatively long follow up period)
q6a 4
Q6a: (4)
  • Another form of adaptive randomization attempts to balance covariates (e.g., minimization)
    • This can be done with vaccine studies
    • Needs to have a measure of imbalance
      • Must adjust for covariates used in imbalance score
      • Potential for manipulation?
    • May be problems in appropriate analysis model – considerable disagreement
slide30
Q7:
  • What factors are used to estimate sample size?
q7 what factors are used to estimate sample size
Q7: What factors are used to estimate sample size?
  • Most popular question to statisticians
  • Factors (using a two group test as an example)
    • Significance level (α, often 0.05)
    • Type 2 error (, often 0.2 or 0.1; 1- is the power)
    • Standard deviation of observation ()
    • Difference in means (1 - 2 )
    • Allocation ratio
slide32
Q7: (2)
  • Significance level, type 2 error and allocation ratio are relatively easy to determine
  • Mean difference and standard deviation are usually based on preliminary studies and may be quite uncertain.
    • I find it useful to take these preliminary differences and halve them
    • What is really needed is the ratio of treatment difference to the standard deviation
slide33
Q7: (3)
  • In vaccines we often want to estimate the vaccine efficacy and find a confidence interval with a lower bound that gives us assurance the vaccine is working well

Where IV is the incidence rate for the vaccine group and IC is the incidence rate for the control group

Lower bound must be substantially greater than 0

slide34
Q7: (4)
  • It’s easy to find an expression for the confidence interval and then one sets the lower bound of the interval to the desired level (set in consultation with FDA)
    • Often ¾ of the observed VE, and VE is set by needs of clinical prevention. For example if the target VE is 0.8 (or 80%) the lower bound would be 60%
    • These are not absolute criteria!
slide35
Q8:
  • How does the investigator choose the margin of equivalence or non-inferiority (delta or ) in comparative clinical trials?
slide36
Q8:
  • Demonstrate that active control has assay sensitivity – that is, it consistently shows itself better than placebo – need evidence of this.
  • Is the control an appropriate one? Compare to best licensed product, not worst
  • What is clinically important? Depends on context of disease
slide37
Q8: (2)
  • When citing a % difference for VE (or anything) be sure to clarify whether you mean 10 percentage points or 10% of the comparator
    • If comparator has a VE of 80%, do we want the estimated VE of the new vaccine to be 72% or 70% if we choose a 10% margin? Or do we want the relative VE (vaccine vs. control) to be at least 90%
    • It’s easy to become confused so it’s good to be specific
slide38
Q9:
  • How does the investigator deal with missing data?
slide39
Q9:
  • Don’t have any
  • Don’t have very much (under 5% is my initial break point)
  • Discuss how you will deal with it prospectively!!!
    • Complete cases analysis (ugh!)
    • Last Observation Carried Forward (LOCF)
    • Mean values for replacement
    • Regression models for replacement
    • Imputation models
slide40
Q9: (2)
  • Types of missing values
    • Patient misses visit, and an ‘interior’ value is missing
    • Patient drops out, and a series of values at the end are missing
    • (Some) Covariates are missing in one or more visits
slide41
Q9: (3)
  • Last Observation Carried Forward
    • Is almost always a problem for me
    • It ignores any trends in data
    • It reduces variability arbitrarily
slide42
Q9: (4)
  • Mean values imputation
    • Need to be sure you don’t increase the apparent sample size (i.e., replaced values don’t give a more precise estimate)
    • This doesn’t account for patient specific characteristics
    • This can be especially tricky if a long series of values is imputed
    • Mean of patient values or mean of other patients at that visit?
slide43
Q9: (5)
  • Regression models
    • Determine what variables are “good” predictors of the missing value – usually useful to have a small number of such variables so they won’t have a lot of missing value issues
    • Predict missing value using a regression model (try to show that variance of predicted value isn’t too big)
slide44
Q9: (6)
  • More sophisticated imputation models have been developed recently
    • Determine classes of similar patients (“propensity scores”)
    • Fill in missing values by selecting randomly from observations in the same class.
    • Do this multiple times, (5 to 10 is usually enough)
    • Analyze the data and pool results
slide45
Q10:
  • How does an investigator determine what data should and should not be included in analyses (especially in the cases of protocol violations, withdrawals and drop-outs)?
slide46
Q10:
  • The fundamental efficacy data set includes all subjects as randomized
    • Note this does not say “ignore patients who didn’t get medication” – doing this messes up the randomization plan and allows data shaping
    • This is called Intent to Treat (ITT)
    • Modified ITT relaxes this to patients who have had at least one immunization (treatment)
q10 2
Q10: (2)
  • Per Protocol Data set
    • Subjects who had no protocol violations (completed study, etc)
  • Can provide various analysis data sets that may be subsets – e.g. no protocol violations, no withdrawals or dropouts
    • Important to examine comparability of groups and outcomes to ITT, mITT
q10 3
Q10: (3)
  • Data to be included will depend on the purposes of the analysis. All substantial differences from total population need to be explained
    • E.g., Immunogenicity analysis had only 48% of the total sample because only 50% of subjects were solicited for bleeding
  • FDA will expect to have access to all data and may audit some of it.
slide49
Q11:
  • What types of analyses should be used when there are multiple time points (multiple observations) of data collection (there is no dichotomous outcome/endpoint)?
slide50
Q11:
  • There has been an active area of statistical research on longitudinal data analysis in the past few years
    • In vaccines research, the common issue is in immunogenicity levels over time. These are often in log(GMC) or log(titer). These look like multiple continuous measurements
    • Can also have whether a four-fold increase in titer has been achieved at various times. This is a series of dichotomous variables (yes or no)
q11 2
Q11: (2)
  • Longitudinal analysis accounts for subject, treatment, other covariates and time in the analysis
    • Measurements made at different times are correlated
    • Must determine appropriate correlation structure
    • Shape of response curve (linear over time? Curve? )
q11 3
Q11: (3)
  • Longitudinal analysis
    • GEE models provide great flexibility
    • Dichotomous variables (e.g., seroconversion at different times) can be handled with GEE
  • Alternatives – may be appropriate
    • Change from baseline to final observation (usually need to have a common last time) - does this depend on baseline? Should we use last observation with baseline as covariate?
slide53
Q12:
  • In analyzing data from clinical trials involving multiple sites, should site be treated as a fixed or random effect?
slide54
Q12:
  • The site should be included in the analysis model, especially if randomization is stratified. If it’s not stratified, you may not wish to include it if sites are generally small. With small sites, the d.f. that are used up reduces the precision of the comparison
q12 2
Q12: (2)
  • If we include sites, they are random effects rather than fixed effects since the intent is to generalize beyond those sites in the trial
    • With random effects, the inference extends to all possible sites (i.e., the population of sites)
    • With fixed effects, the population is just the sites that have enrolled patients
  • Main idea is to analyze the data according to the way in which they have been collected
q12 3
Q12: (3)
  • Interesting question (at least for statisticians!)
    • How can we regard the sites as a random sample of all sites if we have selected them because they have talented and committed physicians conducting the research there?
    • No decent answer – but we have little interest in drawing conclusions that apply only to the sites that have entered patients
slide57
Q13:
  • When should a planned interim analysis (for safety and/or efficacy and/or sample size re-estimation) be appropriate? What are the pros and cons? What are the methods used?
slide58
Q13:
  • There are three reasons for doing an interim analysis:
    • Examine the safety of the vaccine at an early time to ensure that we are not harming subjects - either stop or continue
    • Examine efficacy of the vaccine at an early time – may stop because vaccine is very good or very bad, or may continue
    • Re-estimate sample size – learn that study is too small to show a difference (variability too large, treatment effect is too small) – probably don’t want to increase sample size by more than 50% total
q13 2
Q13: (2)
  • All interim analyses carry a risk of unblinding the study
    • If study stops, all is known
    • If study continues, a reasonable inference is that the study has a small p-value (>0.05) since we would have stopped if there was little hope of showing a difference
    • Someone might inadvertently (or deliberately) let information slip out
q13 3
Q13: (3)
  • These require adjustment of the critical values. It is complicated and several programs (EaST, PEST, SPlus for sequential analysis) are available
  • All interim analyses or sample size re-estimation analyses need to be specified a priori in the protocol
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