Adaptive biomarker-driven designs in Phase III clinical trials Alex Dmitrienko Vice President Center for Statistics in Drug Development Quintiles Innovation KU Medical Center Department of Biostatistics April 18, 2014
Outline – Personalized medicine approach • Development of tailored therapies – Clinical trial designs • Subpopulation-only designs versus multi-population designs – Case study • Oncology Phase III trial – Adaptive biomarker-driven clinical trial designs • Development and optimization of complex data-driven decision rules 2
Personalized medicine approach – Tailored therapies • Novel therapies that target subgroups of patients with certain characteristics • Patient characteristics include demographic variables, clinical variables, gene or protein expression markers, etc – Regulatory guidance documents • U.S. (Food and Drug Administration): Enrichment strategies for clinical trials to support approval of human drugs and biological products (December 2012) • Europe (Committee for Medicinal Products for Human Use: Guideline on the investigation of subgroups in confirmatory clinical trials (January 2014) 3
Clinical trial designs – Patient populations • Overall population (OP) • Marker-positive subgroup (M+) • Marker-positive subgroup (M-) – Therapeutic benefit • Greater therapeutic benefit is expected in the marker-positive subgroup 4
Clinical trial designs – Subpopulation-only designs • Enrollment is restricted to M+ (only marker-positive patients are enrolled) and M- is not studied (Freidlin, McShane and Korn, 2010) • Also known as biomarker-enriched designs – Multi-population designs • All patients are enrolled and treatment effect is examined in OP as well as M+ (Millen et al., 2012) • Also known as biomarker-stratified designs 5
Subpopulation-only designs – Breast cancer example • Herceptin (trastuzumab) for treatment of breast cancer (Romond et al., 2005) • Important marker: Amplified HER2 gene • Efficacy in marker-positive patients (with HER2-positive tumors) was established and marker-negative patients were not enrolled • Marker-negative patients were denied access to potentially beneficial treatments but Herceptin is likely to have a positive effect in classifier-negative patients (Paik et al., 2008) – Regulatory position • Desirable to evaluate the efficacy and safety of new treatments in both marker-positive and marker-negative patients (FDA enrichment guidance) 6
Case study – Phase III clinical trial • Patients with a rare type of cancer – Treatment comparison • Experimental therapy plus chemotherapy versus placebo plus chemotherapy – Primary endpoint • Overall survival (OS) – Potential predictive biomarker • Biomarker (protein expression marker) discovered in Phase II trial • Believed to be predictive of treatment response 7
Adaptive biomarker-driven trial design – Two-stage design • Interim analysis to review the safety and efficacy data – Stage I (before the interim analysis) • Multi-population trial design (both marker-negative and marker- positive patients are enrolled) – Stage II (after the interim analysis) • Trial design may be adaptively modified at the interim analysis to improve the overall probability of success 8
Two-stage design Interim analysis positive Marker ??? negative Marker Stage I Stage II Futility stopping rules, population selection rules and sample size adjustment rules will be applied at the interim analysis 9
Decision rules at the interim analysis – Futility stopping • Evaluate futility in the overall population and marker-positive subgroup – Population selection • Select the overall population or marker-positive subgroup for Stage II – Sample size adjustment • Increase the sample size in Stage II if necessary 10
Decision rules at the interim analysis Probability of success in M+ High ↑ OP M+ OP M+ Medium ↑ M+ ↑ OP Low Stop Low Medium High Probability of success in OP Summary of all decision rules ( ↑ increase the sample size) 11
Futility stopping rules – Futility stopping rules • Futility stopping rules will be implemented based on predicted probability of success at the interim analysis – Commonly used approaches • Frequentist (conditional power) • Bayesian (predictive power) • Bayesian (predictive probability) 12
Conditional power Statistically significant Observed data Future data are generated difference from assumed treatment difference Start Interim look End Conditional power function CP n ( d )=P(Z N z a |Z n , d ) • Likelihood of a statistically significant result given the interim data • Z n , Test statistic at the interim analysis • Z N , Test statistic at the planned end • d , Assumed treatment difference 13
Predictive power Statistically distribution distribution significant Observed data Future data are Posterior difference Prior generated from posterior distribution Start Interim look End Predictive power function PP n = CP n ( d )f( d |Z n )d d • Likelihood of a statistically significant result given the interim data averaged over the posterior distribution of d • f( d |Z n ), Posterior density of treatment difference d given the interim data 14
Predicted probability of success – Outcome 1: Low probability of success • Discontinue enrollment due to futility if the predicted probability of success at the planned study end is low (e.g., less than 30%) – Outcome 2: Medium probability of success • Apply the population selection and sample size adjustment rules if the predicted probability of success at the planned study end is medium (e.g., 30-70%) – Outcome 3: High probability of success • Apply the population selection and sample size adjustment rules if the predicted probability of success at the planned study end is high (e.g., greater than 70%) 15
Population selection rules Probability of success in M+ High Medium NA Low NA NA Low Medium High Probability of success in OP Since greater benefit is expected in M+, predicted probability of success in OP is lower than in M+ 16
Population selection rules Probability of success in M+ High Medium Low Stop Low Medium High Probability of success in OP No group is selected for Stage II (futility rule is met in both OP and M+) because the success probability in M+ is low 17
Subgroup selection rules Probability of success in M+ High OP Medium OP Low Low Medium High Probability of success in OP OP is selected for Stage II because the success probability in OP is comparable to that in M+ (non-informative marker) 18
Population selection rules Probability of success in M+ High M+ Medium M+ Low Low Medium High Probability of success in OP M+ is selected for Stage II because the success probability in M+ is medium or high and no effect in M- 19
Population selection rules Probability of success in M+ High OP M+ Medium Low Low Medium High Probability of success in OP M+ and OP are selected for Stage II because the success probability is high in M+ and medium in OP 20
Population selection rules Probability of success in M+ High OP M+ OP M+ Medium M+ OP Low Stop Low Medium High Probability of success in OP Summary of population selection rules 21
Potential regulatory claims – Broad claim • Treatment effectiveness in the overall population only (OP only) – Restricted claim • Treatment effectiveness in the marker-positive subgroup only (M+ only) – Enhanced claim • Treatment effectiveness in the overall population as well as marker- positive subgroup (OP and M+) 22
Sample size adjustment rules – Promising zone approach • Adjust the sample size in the overall population or marker-positive subgroup in Stage II based on the predicted probability of success – Outcome 1: Original sample size • in M+ or OP if the predicted probability of success is high (e.g., greater than 70%) – Outcome 2: Increase the sample size • Increase the sample size in M+ or OP in Stage II if the predicted probability of success is medium [promising zone] (e.g., 30-70%) • The sample size will be adjusted to increase the predicted probability of success to 70% • Sample size increase will be capped (e.g., at 30%) 23
Sample size adjustment rules Probability of success in M+ High OP M+ OP M+ Medium M+ OP Low Stop Low Medium High Probability of success in OP Use the original sample size in M+ or OP since the predicted probability of success is high 24
Sample size adjustment rules Probability of success in M+ High OP M+ OP M+ Medium M+ OP Low Stop Low Medium High Probability of success in OP Increase the sample size in M+ since the success probability is medium 25
Sample size adjustment rules Probability of success in M+ High OP M+ OP M+ Medium M+ OP Low Stop Low Medium High Probability of success in OP Increase the sample size in OP since the predicted probability of success is medium 26
Final decision rules Probability of success in M+ High ↑ OP M+ OP M+ Medium ↑ M+ ↑ OP Low Stop Low Medium High Probability of success in OP Summary of all decision rules at the interim analysis ( ↑ increase the sample size) 27
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