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Carrie Deis Nadine Dewdney.  Phase I clinical trials  Standard Designs  Adaptive Designs  Bayesian Approach  Traditional vs. Bayesian  Hybridization.

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Presentation on theme: "Carrie Deis Nadine Dewdney.  Phase I clinical trials  Standard Designs  Adaptive Designs  Bayesian Approach  Traditional vs. Bayesian  Hybridization."— Presentation transcript:

1 Carrie Deis Nadine Dewdney

2  Phase I clinical trials  Standard Designs  Adaptive Designs  Bayesian Approach  Traditional vs. Bayesian  Hybridization  FDA Guidance  Conclusion

3  Conducted to determine toxicity for the dosing of the new intervention  First time the drug is tested in humans  Small number of patients, 20 to 50  Depending on the nature of the new drug, patients are usually healthy volunteers  A higher dose is assumed to be more effective  Goal is to find maximum tolerable dose (MTD)

4  Known prior to the start of the trial: ◦ Starting dose ◦ Toxicity profile and Dose-limiting toxicity ◦ Target toxicity level ◦ Dose Escalation Scheme  Starting dose commonly chosen as: ◦ 1/3 lowest toxic dose in dogs ◦ 1/10 of the LD 10 in mice  Dose escalation is done incrementally ◦ Increments are pre-determined ◦ Modified Fibonacci sequence – increase rate diminishing as the dose gets higher Phase I

5  Patients are assigned to dose levels according to predefined rules ◦ Allow for only escalation and de-escalation of dose  Doses selected such that, D 1,…, D K would be close to MTD  MTD is determined statistically as the dose at which 1/3 of the subjects develop toxicity

6  Subjects are randomized  The number of subjects, r i, developing toxicity would be observed  p i = r i /n i, is used to calculate the proportions exhibiting toxicity  Dose-response is modeled based on the probability of toxicity  The MTD would be fitted to this model

7  Ethical concerns with the traditional approach ◦ Patients might be treated excessively and unnecessarily at low doses ◦ Too many patients may be treated at doses that are too high or too low ◦ Highly likely most subjects are treated at low doses ◦ Not clear that the estimated MTD is the correct dose

8  Adjustments and modifications can be made after the trial has started ◦ Does not affect the integrity of the trial ◦ Goal is to improve upon the probability of success of the trial and correctly identify the clinical benefits of the intervention under investigation  Prospective adaptations include ◦ Stopping a trial early for safety or lack of efficacy ◦ Dropping the loser - Inferior treatments dropped ◦ Sample size re-estimation

9  Modifications hypothesis might be necessary ◦ Inclusion/exclusion criteria ◦ Dose/regimen ◦ Treatment duration ◦ Endpoints  Several types of adaptive designs ◦ Group sequential ◦ Sample size adjustable design ◦ Drop-the-losers design ◦ Adaptive treatment allocation design ◦ Bayesian adaptive methods

10  Based on Bayes Theorem: ◦ Expresses how a subjective degree of belief should rationally change to account for evidence  Used as a statistical inferential tool in adaptive designs  Strength of the Bayesian approach ◦ Decision on trial continuation is made as data accumulates ◦ Sample size not determined in advance although a maximum size might be specified  Drawbacks ◦ Analysis after each subject is treated

11  Calculates the predictive probability that the patient will respond to treatment  Specifies a prior distribution then updates it as information becomes available  Uses the likelihood function and the prior distribution to obtain a posterior distribution  MTD is determined from the posterior distribution  Studies are based on costs and public health benefits

12  Prior Distribution  Logistic model: p(d) = exp (3 + ad)/[ 1 + exp(3 + ad)]  Power model: p(d) = d exp(a)  p(d) is the probability of DLT  d is the dose  a is a model parameter

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14  Once the posterior distribution is calculated: ◦ The MTD is revised based on the distribution of a ◦ The mode of the posterior distribution is used to estimate the next dose  Each patient is treated at the dose which is closest to the MTD  Toxicity profile is updated after each patient is treated  The sequence is repeated until a precise estimate of parameter a is obtained or the sample size is exhausted

15  Example: A dose-finding escalation design from an oncology trial  Traditional approach ◦ The 3+3 traditional escalation rule (TER)  Bayesian approach ◦ The continual reassessment method (CRM)  The objective is to determine the MTD for a new drug using the least amount of patients

16  Results from animal studies: ◦ The dose limiting toxicity rate was determined to be 1% for the starting dose of 25 mg/m 2, 1/10 of the lethal dose  The MTD is estimated to be 150 mg/m 2  The dose limiting toxicity rate is defined as 0.25  Selected Model: A logistic toxicity model  Dose sequence was chosen with interim factors = 2, 1.67, 1.33, 1.33, 1.33, 1.33, 1.33, 1.33, 1.33

17 Summary of simulation results for the designs Method Assumed True MTD Mean Predicted MTD Mean number of Patients Mean number of DLTs 3+3 TER CRM TER CRM TER CRM

18  Both approaches underestimate the true MTD ◦ However, the Bayesian approach was much closer to the true value for all dose levels  At all three dose levels the Bayesian approach required less patients  The mean number of DLTs for the Bayesian approach was either less than or equal to the traditional approach at all dose levels  The Bayesian CRM approach proved to be more favorable

19  The Bayesian approach can be used alone or as a hybrid with the classic approach  As a hybrid, the Bayesian approach is used to increase the probability of success  Example: Two-arm parallel design ◦ Compares a test treatment and a control ◦ Use data from 3 clinical trials with similar sample sizes ◦ Prior probabilities for the effect size are 0.1, 0.25, and 0.4 with 1/3 probability for each trial

20  The classic approach: ◦ Mean of the effect size, = 0.25, is used to calculate the sample size. For the design with β = 0.2:  The Bayesian approach:  The power of the effect size is Φ is the c.d.f. of the standard normal distribution  Prior, π(ε), is the uncertainty of ε, the expected power

21  Assuming, one-sided α = 0.025,  P exp =0.66  With the hybrid approach the power is less than the 80% power stated in the frequentist approach, recall β = 0.2. In order to reach the expected power of 80%, the sample size needs to be increased  The Bayesian approach piece is used to increase the probability of success given that the final criterion is p ≤ α =

22  Prior information and Assumptions  Criterion for success for safety and effectiveness  Justification for the proposed sample size  Prior probability of the study claim ◦ This is the probability of the study claim before seeing any new data, and it should not be too high ◦ Ensures the prior information does not overwhelm the current data, potentially creating a situation where unfavorable results from the proposed study get masked by a favorable prior distribution  Program Code

23  Operating characteristics ◦ Provide tables of the probability of satisfying the study claim, given “true” parameter values and sample sizes for the new trial ◦ Provides an estimate of the probability of a type I error in the case where the true parameter values are consistent with the null hypothesis, or power in the case where the true parameter values are consistent with the alternative  Effective Sample Size ◦ Quantifies the efficiency you are gaining from using the prior information and gauges if the prior is too informative

24  Bayesian full approach is more beneficial in Phase I studies ◦ Inherent adaptive nature of the design ◦ Conditions are more dynamic than other phases and the flexible nature of the Bayesian approach allows for unexpected changes ◦ Produces a posterior probability which is useful in decision making and the transitioning from one phase to the next ◦ Dose levels can be modified which could be beneficial for a phase I cancer study

25  Even without using a full Bayesian method, hybridization results in increased probability of success in trials  Maintaining the validity and integrity of the study and control of the type I error in applications of the method is important  Feasibility should be evaluated in order to prevent abuse of this method in applications such as endpoints or hypotheses changes  The FDA is cautious of the growing trend of Bayesian designs and continues to set guidelines for its use in Phase I trials

26  Chang, Mark (2008). Adaptive Design Theory and Implementation Using SAS and R. Boca Raton: Chapman & Hall/CRC  Berry, Scott M., Carlin, Bradley P., Lee, J.Jack, Muller, Peter (2011). Bayesian Adaptive Methods for Clinical Trials. Boca Raton: Chapman & Hall/CRC  Chow, Shein-Chung and Chang, Mark (2008). Adaptive Design Methods in Clinical Trials – A Review. Orphanet Journal of Rare Diseases, 3 11  Cook, Thomas D. and DeMets, David L. (2008). Introduction to Statistical Methods for Clinical Trials. Boca Raton: Chapman & Hall/CRC  The FDA Center for Drug Evaluation and Research, and Center for Biologics Evaluation and Research, Guidance for Industry: Adaptive Design Clinical Trials for Drugs and Biologics:

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