1. Classification of clinical trials

2. Chapter 7 Reading instructions
7.1 Introduction
7.2 Multicenter Trials: Read + extra material
7.3 Superiority Trials: Read
7.4 Equivalence/Non-inferiority Trials: Read
7.5 Dose Response Trials: Read
7.6 Combination Trials: Read
7.7 Bridging Trials: Skip
7.8 Vaccine Trails: Skip
7.9 Discussion

3. Center=clinic=study site
Multicenter trials
All large studies are conducted at multiple centers.
Center=clinic=study site
Issues:
Treatment by center interaction*
Estimation of treatment effect.
*) Covered in the lecture on basic statistical concepts

4. Multicenter trials Type I Type II Type III Sum of squares:
Estimation of treatment effect
Model: effect=center + treatment + center*treatment + error
Sum of squares:
Type I
Type II
Type III
Varation due to each specific factor beyond those already in
the model according to order
of specification.
Varation due to each specific factor beyond what can be explained by all other specified factors including interactions
Varation due to each specific factor beyond what can be explained by the others, excluding interactions with this specific factor

5. Multicenter trials Assume k centers with
True treatment effect:
Estimated treatment effect:
Variance of estimated treatment effect:
Overall treatment effect estimated by
where
Type II estimator:
Treatment effects averaged over center weighted according to precision (think ni).
Type III estimator:
Treatment effects averaged over center with equal weight for all centers

6. Multicenter trials
The effect of center imbalance on type III estimates and test
200 patients split on 2 centers, response N(50,25) and N(80,25), no true center effect or interaction between center and treatment.
Model including treatment, center and treatment*center, simulated 1000 times.

7. Superiority, equivalence and non-inferiority
Experimental treatment with true mean effect:
Control treatment with true mean effect:
Superiority:
The experimental treatment is better than the control treatment.
Equivalence:
The experimental treatment and the control treatment are similar.
Non-inferiority:
The experimental treatment is not that much worse than the control treatment.

8. Superiority REJECTED Superiority:
The experimental treatment is better than the control treatment.
The experimental treatment is not better than the control treatment
The experimental treatment is better than the control treatment
REJECTED
95% conf. Int.

9. Equivalence
The experimental treatment and the control treatment differ at least d
The experimental treatment and the control treatment differs less than d -d d
The combined null hypothesis H0 can be tested at level  by testing each of the two disjunct components also at level .
REJECTED
REJECTED -d d
90% conf. Int.

10. What is similarity? Example:
We have developed a new formulation (tablet) for our old best selling drug. How do we prove that the new formulation has the same effect as the old one without new big studies?
Due credit to Chris Miller AstraZeneca biostatistics USA

11. Bioequivalence FDA definition: Operationalized as:
Pharmaceutical equivalents whose rate and extent of absorption are not statistically different when administered to patients or subjects at the same molar dose under similar experimental conditions
Operationalized as:
Compared exposure in terms of AUC and Cmax of the plasma concentration vs time curve and conclude bioequivalence if the confidence for the ration between the formulations lies between 0.8 and 1.25 for both AUC and Cmax.

12. Crash pharmacokinetics
Distribution:
The drug is distributed to the various sub compartments of the body.
Concentration (nmol/L)
Elimination:
The drug leaves the circulation by metabolism or excretion at an exponential rate.
Time (h)
Time of dose=0
Absorbtion:
The drug is absorbed from the cite of administration leading to increased concentration in the blood (plasma).
Tmax=time to Cmax
AUC=Area Under Curve
Cmax=maximal concetration

13. Example Design: Model: Mixed effect ANOVA A B D C
A phase I, open, randomized, four-way cross-over, single-centre study to estimate the pharmacokinetics and tolerability of single oral doses of 100 mg AR-H044277XX given as two different mesylate salt tablets, a base form tablet and an oral solution in healthy male subjects.
The primary objectives of this study are to estimate and compare the pharmacokinetics of two different mesylate salt tablets, a base form tablet and an oral solution given as single oral doses of 100 mg AR-H044277XX in healthy male subjects by assessment of AUC and Cmax.
Design:
Model:
Mixed effect ANOVA A B D C

14. Example cont.
XX and sol are bioequivalent by not AW and sol or AWmicr and sol.

15. Non inferiority REJECTED -d -d
The experimental treatment and the control treatment differ at least d
The experimental treatment and the control treatment differes less than d -d
REJECTED -d
95% conf. Int.

16. Why Non inferiority? Intrinsic non-inferiority: Indirect superiority:
To claim that the effect of the test drug is at least not to a relevant degree worse than the comparator.
Often combined with superiority on other variable e.g. non inferior effect and superior safety.
Indirect superiority:
To claim that the effect of the test drug is better than placebo when placebo not considered ethical.

17. Dose Response trials objectives
Confirm efficacy
Investigate shape of dose reponse curve
Estimate a appropriate starting dose
Indentify optimal individual dose adjustment strategy
Determination of maximal dose
Safety!
(ICH E9)
Effect
emax e0
e50
ed50
Dose
Toxic effect
Beneficial effect
Therapeutic window

18. Dose Concentration(s) Effects c E
Time E
Time
One dose results in concentration vs time profiles for the given compound as well as one or several metabolites
Depending on the mechanisms of action we get effect vs time profiles for both wanted and unwanted effects

19. Inter individual variation in dose response
Effect
Dose
Population average

20. Dose response trials design
Design options
Parallell groups
Forced titration
Cross over
Large
Easy to impement
No confounding
Easy to analyse
Small
Confounding
Small
Carry over effects

21. Dose response trials design
Dose k
Control
N=n1
N=n2
N=nk
N=nk+1
Design:
Parallell groups with k dose groups and a control.
Placebo?
How are the doses selected?
Which sample size(s) should be used?

22. Dose Response trials models
Separate means with equal variance ;
Regression model
Effect
emax e0
e50
ed50
Dose

23. Confiriming efficacy
Dose 1
Dose 2
Dose 3
No placebo but significant linear dose response means efficacy confirmation
Dose 1
Dose 2
Dose 3
Placebo
Significant difference from placebo confirms efficacy
Dose 1
Dose 2
Dose 3
Active
Noninferiority to active control confirms efficacy

24. Dose response trials design options
Fixed design: doses and sample sizes are descided upfront and subsequently not changed during the trial.
Adaptive design: Initial doses and sample sizes are descided upfront may subsequently be changed during the trial depending on the outcome.
Basic adaptive variants:
Bayesian
Frequentist (D-optimality)

25. Dose response trial analysis options
Pairwise comparisons: The doses are compared using significance tests often adjusted for multiple comparisons and the aim is to show effect vs the comparator and to separate the doses.
Limited assumptions
Easy to compare doses
Need relatively many observations
No estimate of a dose reponse curve
Model based: The effect is assumed to follow a parameteric model with parameters estimated from the data.
More assumptions
Tricky to compare doses
Need relatively few observations
Estimates a dose reponse curve

26. Example of the design of a dose response trial
The design of a dose response trial is based on data from previous studies with the same or similar drugs.
In this example we have plenty of data on the relation between the dose and the effect on a biomarker.
We could also find litterature data relating the effect on the biomarker to clinical effect.

27. Example of the design of a dose response trial
Samplesize:
Highest dose to have power 80% against competitor
Same sample size in all groups

28. Combination treatments
Evaluation of a fixed combination of drug A and drug B
A placebo
B placebo
A active
B active P B AB A
The U.S. FDA’s policy (21 CFR ) regarding the use of a fixed-dose combination
The agency requires:
Each component must make a contribution to the claimed effect of the combination.
Implication: At specific component doses, the combination must be superior to its components at the same respective doses