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Classification of clinical trials

Classification of clinical trials. 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

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Classification of clinical trials

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

  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. Experimental treatment with true mean effect: Control treatment with true mean effect: Superiority, equivalence and non-inferiority 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 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 0 REJECTED 0 95% conf. Int. 0

  9. Equivalence The experimental treatment and the control treatment differ at least d The experimental treatment and the control treatment differs less than d 0 -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 0 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: 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. A B C D B D A C C A D B D C B A Example 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

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

  15. Non inferiority The experimental treatment and the control treatment differ at least d The experimental treatment and the control treatment differes less than d 0 -d REJECTED 0 -d 95% conf. Int.

  16. Why Non inferiority? Intrinsic non-inferiority: 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. Effect 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! emax e50 e0 ed50 Dose Toxic effect Dose Response trials objectives (ICH E9) Beneficial effect Therapeutic window

  18. Dose Concentration(s) Effects c 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 Time

  19. Effect Dose Inter individual variation in dose response 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 1 N=n1 Dose 2 N=n2 R Dose k N=nk Control N=nk+1 Dose response trials design Design: Parallell groups with k dose groups and a control. Placebo? How are the doses selected? Which sample size(s) should be used?

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

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

  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. P A A placebo B placebo A active B placebo B AB A placebo B active A active B active Combination treatments Evaluation of a fixed combination of drug A and drug B The U.S. FDA’s policy (21 CFR 300.50) 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

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