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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 treatmenteffect. • Randomization *) partly Covered in the lecture on basicstatisticalconcepts

  4. Multi center trials: interaction Example: Treatment by center interaction Average treatment effect: -4.39 [-6.3, -2.4] mmHg Treatment by center: p=0.01 What can be said about the treatment effect?

  5. Multicenter trials: interaction • Is to be expected but difficult to detect Qualitative interaction Quantitative interaction No interaction Quantitative interaction Treatment A Treatment A Treatment A Treatment A Treatment B Treatment B Treatment B Treatment B 1 1 1 1 2 2 2 2 Center Center Center Center • Quantitative (i.e., same direction only magnitude differs) very common • Qualitative (i.e., treatment shows benefit in some centers , Placebo shows benefit in others) less common • Qualitative interaction is of concern but not found very often and hard to establish

  6. Multi center trials: interaction ICH E9: • Test main effect first , if significant: • Test interaction as an exploratory analysis • If there are a large number of centers, it is less important to consider interaction. Sometimespoeple suggest to pool small centers. Easy to seewhy (more patients/center) butwhatdoes it mean??

  7. Multicanter trial: statistical model Obsijk=grand mean + centeri+treatmentj+(center*treatment)ij+errorijk Alternatives: • Fixed: Center and treatment*center interaction are fixed effects • Random: Center and treatment*center interaction are random effects • Fixed: • Gives a precise answer to a fairly well defined question, does the drug work for patients at these centers? • The only option for a single center study • Centers are carefully chosen, not randomly • The definition of center is arbitrary • Random: • We want to say something about patients in general and this is the best shot at this difficult question • Wider confidence interval reflects the true uncertainty of centers are really different. • Allows prediction of the effect in one specific center using information from all centers.

  8. 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

  9. 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

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

  11. Multi center trial-randomization • Central randomization • One central randomization list • Potentially large anounts of drug wasted • Near perfectly balanced treatemnt groups over all • Potentially large inbalance between treatment group in individual center • Block size can be open • Stratified randomization • One randomization list per center • Small amount of drugs wasted • Treatment groups can be inbalanced over all* • Close to balanced treatment groups within centers • Block size should be sectret For large studies with many small centers the imbalance is often ”small”, and can be estimated using a normal approximation see Anisimov 2011

  12. Example The Jupiter study includes 17802 patients recruited from 1348 centers. If the recruitment rate is modeled as a Poisson process with a intensity that follows a Gamma distribution the parameter of that gamma distribution is estimated to be scale=18.05, shape=0.73. Upper limit of the imbalance between treatment groups for a study similar to Jupiter with center stratified randomization and block size=4 Number of patients Number of centers 95% Imbalance limit 17000 1307 143 18000 1384 148 19000 1461 154 The 95% upper limit for the imbalance in the number of randomized patients between the two treatment groups of a study like Jupiter with almost 18000 patients and 1348 centers is 148 patients.’ Jupiter is an outcome study with low event rates and the imbalance of 148 patients corresponds to an imbalance of one event

  13. 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.

  14. 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

  15. 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.

  16. 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

  17. 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.

  18. 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

  19. 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

  20. Examplecont. Compare 3 new formulations to a solution XX and sol are bioequivalent by not AW and sol or AWmicr and sol.

  21. 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.

  22. 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.

  23. Challenges in Non-inferiority • The choice of delta: • How muchworse (i.e. Δ) canwe be but still claim ”no worse than…”) • Work with Key Opinion Leaders on thisto get a global agreement • Assaysensitivity: • Is a property of a clinical trial defined as the ability to distinguish an effective treatment from a less effective or ineffective treatment • Weneed to know that if placebo wasincluded – wewhouldhavebeensuperior • Similar designs as previously used including length of treatment, entry criteria etc.

  24. Effect Objectives: • Confirmefficacy • Investigateshape of dosereponsecurve • Estimate a appropriate starting dose • Indentify optimal individualdoseadjustmentstrategy • Determination of maximal dose • Safety! emax e50 e0 ed50 Dose Toxiceffect Dose Response trials objectives (ICH E9) Beneficialeffect Therapeuticwindow

  25. Dose Concentration(s) Effects c E Time One doseresults in concentration vs time profiles for the given compound as well as one or severalmetabolites Depending on the mechanisms of action we get effectvs time profiles for bothwanted and unwantedeffects Time

  26. Effect Dose Inter individual variation in doseresponse Population average

  27. Doseresponsetrials design Design options Parallell groups Forcedtitration Cross over • Large • Easy to impement • No confounding • Easy to analyse • Small • Confounding • Small • Carry over effects

  28. 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 dosesselected? Whichsamplesize(s) should be used?

  29. Effect: y Emax E50 E0 ed50 Dose Dose Response trials models Choice of statisticalmodel Regression model (here an Emaxmodel) Separate means with equalvariance ; Design criteria? • Power of statistical test • Pariwise • Trend • Estimation of model parameter • Optimal design (E, D,…) • Decisionmodel for choice of dose • Whichutility, NPV? Seriously non trivial!

  30. Confirimingefficacy No placebo butsignificantlineardoseresponsemeansefficacyconfirmation Dose 1 Dose 2 Dose 3 Significantdifference from placebo confirmsefficacy Noninferiority to activecontrolconfirmsefficacy Placebo Dose 1 Dose 2 Dose 3 Active Dose 1 Dose 2 Dose 3

  31. 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)

  32. 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

  33. How to design 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.

  34. Samplesize: Highestdose to have power 80% againstcompetitor -Howdoes this helpus to select the best dose???

  35. backup

  36. Multicenter trials Type II: Assuming (true) within center variance equal in all centers. Type III:

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