1 Statistical Designs for Stratified Medicine Cindy Billingham Professor of Biostatistics, School of Cancer Sciences Director of Statistics, Cancer Research UK Clinical Trials Unit Lung Cancer Research Stratified Medicine Educational Event Birmingham, June 22 nd 2015

2 Agenda Early phase clinical trials for stratified medicine Illustrated using National Lung Matrix Trial Bayesian adaptive design Example of an ‘umbrella trial’ Later phase clinical trials for stratified medicine Typical randomised controlled designs (RCTs) Umbrella and basket trials Do we always need an RCT to change clinical practice in stratified medicine?

3 Actionable targets (biomarkers) and targeted drugs = 17 drug-biomarker cohorts PrevA:AZD 4547 B:AZD 2014 C:Palb ociclib D:Crizo tinib E:Sel +Doc F:AZD 5363 G:AZD 9291 A1: FGFR2/3 mutation-NSCLC4.0% B1: TSC1/2 mutation-NSCLC2.7% B2: LKB1 TIER1 mutation-NSCLC6.4% C1: Proficient Rb & p16 loss-SCC29.0% C2: Proficient Rb & CDK4 amp-NSCLC7.0% C3: Proficient Rb & CCND1 amp-NSCLC7.3% C4: Proficient Rb & KRAS mutation-ADC25.8% D1: Met amplified-NSCLC2.3% D2: ROS1 gene fusion-NSCLC1.7% E1: NF1 mutation-SCC5.8% E2: NF1 mutation-ADC4.6% E3: NRAS mutation-ADC1.0% F1: PIK3CA mutation-SCC11.0% F2: PIK3CA amp-SCC15.0% F3: PIK3/AKT deregulation-NSCLC4.5% F4: PTEN loss & mutation-SCC20.0% G1: EGFR mutation & T790M+-NSCLC8.0%

4 National Lung Matrix Trial Schema Final biopsy result (diagnostic and/or repeat) mandated for National Lung Matrix Trial entry: Biopsy failure (diagnostic and repeat) or ineligible No actionable genetic aberration Multiple actionable target with open treatment arms Single actionable target with open treatment arm Outcome Measures (common set for all arms with treatment-arm- specific primary): Best objective response rate (ORR), Change in total target lesion size (PCSD), Progression-free survival time (PFS), Time-to- progression (TTP), Overall survival time (OS), Toxicity Allocated to single treatment arm relevant to actionable target prioritised by CI if eligible Allocated to single treatment arm relevant to actionable target if eligible Standard treatment OR recruitment to another relevant trial Allocated to no actionable genetic change (NA) cohort if eligible Standard Clinical Outcome Measures Actionable target but no target therapy arms open

5 Typical Single Arm Phase II Trial Eligible Patients NEW Treatment Response rate Historical data / clinical experience of standard treatment Benchmark response rate 0%100% ? Common Designs: A’Herns single stage Simon’s two-stage

6 Flexible design that embraces study complexity and facilitates decision-making Need efficient and flexible design that: has potential to stop early for lack of efficacy allows for differing prevalence rates of biomarkers allows continued recruitment to any sample size as appropriate has potential to incorporate relevant information from other biomarker cohorts within each drug arm (‘borrowing information’) has potential to incorporate pre-existing evidence and emerging external evidence enables cumulative learning Question that we really want to answer: What is the probability that the TRUE signal of efficacy is above x% Make Go-NoGo decision for further research based on probability Bayesian Adaptive Design Ref: Berry, Carlin, Lee, Muller; Bayesian Adaptive Methods for Clinical Trials, Chapman and Hall /CRC Biostatistics Series 2011

7 What is a Bayesian Approach to Statistical Analysis? Alternative method of statistical analysis to the classical / frequentist approach ‘The explicit quantitative use of external evidence in the design, monitoring, analysis, interpretation of a health-care evaluation’ Spiegelhalter et al 2004 Based on theorem devised by Reverend Thomas Bayes ( ) Basic maths: Prior x Data → Posterior Bayes Theorem: Posterior probability distribution P(HR<1)=0.75

8 Start Recruitment Interim Analysis 1 N=15 if P(  <30%) ≥ 0.9 STOP Interim Final Analysis N=30 Final if P(  >30%) < 0.5 if P(  >30%) ≥ 0.5 GO Arms A, B, D, F & G Bayesian Adaptive Two-Stage Design if P(  <30%) < 0.9 Cohorts: A1, B1, B2, B3, D1, D2 Primary outcome measure: objective response rate (ORR)  : true ORR

9 Illustrating Statistical Analysis Plan Example: GO-GO Prior: Beta(1,1) Interim Posterior: Beta(4,13) Interim analysis:3/15 = 20% Final analysis:13/30 = 43% P(  <30%) =0.75 Final Posterior: Beta(14,18) P(  >30%) =0.95 Beta-Binomial conjugate analysis Prior:  ~ Beta(a 0, b 0 ) Posterior:  |r,n ~ Beta(a 0 +r,b 0 +n-r)

10 Cumulative Learning Using Bayesian Analysis n=1,r=1n=2,r=1n=3,r=1n=4,r=2n=5,r=2 n=6,r=2n=7,r=2n=8,r=2n=9,r=2n=10,r=2 n=11,r=2n=12,r=2n=13,r=2n=14,r=2 n=15,r=3 n=16,r=4n=17,r=5n=18,r=5n=19,r=6n=20,r=7 n=21,r=7n=22,r=7n=23,r=8n=24,r=8n=25,r=8 n=26,r=9n=27,r=10n=28,r=11n=29,r=12 n=30,r=13

11 How well does this design work? Are other designs better? Desirable operating characteristics Sample size criteria: Need minimum of 10 and maximum of 15 at interim Need minimum of 20 and maximum of 40 at final Interim analysis criteria: Need p(STOP early|  =10%)>80% (true stopping rate) Need p(STOP early|  =40%)<5% (false stopping rate) Final analysis criteria: Need p(GO at final|  =20%)<10% (false positive rate) Need p(GO at final|  =40%)>80% (true positive rate) Of all the designs that satisfy these criteria, the optimal design is that which maximises the true positive rate

12 Start Recruitment Interim Analysis 1 N=15 if P(  <30%) ≥ 0.9 STOP Interim Final Analysis N=30 Final if P(  >30%) < 0.5 if P(  >30%) ≥ 0.5 GO Arms A, B, D, F & G Design and Operating Characteristics 2/15=13% or less 8/30=27% or less 3/15=20% or more 9/30=30% or more if P(  <30%) < 0.9  =10%  =20%  =30%  =40%  =50% STOP early81.6%39.8%12.7%2.7%0.4% STOP at final18.2%47.8%31.8%7.7%0.7% GO at final0.2%12.4%55.5%89.6%99.0% Operating characteristics based on exact binomial probabilities  : true ORR

13 Start Recruitment Interim Analysis 1 N=15 if P(  <40%) ≥ 0.9 STOP Interim Final Analysis N=30 Final if P(  >40%) < 0.5 if P(  >40%) ≥ 0.5 GO Arm E (Selumetinib+Docetaxel) Design and Operating Characteristics if P(  <40%) < 0.9  =10%  =20%  =30%  =40%  =50% STOP early94.4%64.8%29.7%9.1%1.8% STOP at final5.6%34.3%54.7%34.7%8.7% GO at final0.0%0.9%15.6%56.2%89.5% Operating characteristics based on exact binomial probabilities Cohorts: E1, E2, E3 - objective response rate (ORR)  : true ORR

14 Start Recruitment Interim Analysis 1 N=15 if P(  <3mths) ≥ 0.8 STOP Interim Final Analysis N=30 Final if P(  >3mths) < 0.5 if P(  >3mths) ≥ 0.5 GO Arm C (Palbociclib) Bayesian Adaptive Two-Stage Design if P(  <3mths) < 0.8 Cohorts: C1, C2, C3, C4 Primary outcome measure: progression-free survival time (PFS)  : true median PFS (months)

15 Arm C (Palbociclib) Design and Operating Characteristics True median = 1 month True median = 2 months True median = 3 months True median = 4 months True median = 5 months True median = 6 months STOP early97.2%49.9%16.8%5.4%2.2%1.0% STOP at final 2.8%49.0%43.7%11.4%1.8%0.3% GO at final<0.1%1.2%39.4%83.2%96.0%98.6% Operating characteristics estimated through simulation Cohort C1, recruiting at 93 patients per annum when all recruitment centres are open

16 Using Bayesian Hierarchical Modelling as Secondary Analysis E1: NF1 mutant - SCC E2: NF1 mutant - ADC Arm E: Selumetinib + Docetaxel 25/30 (83%) 2/15 (13%) Ensures borderline decisions err on positive if drug has shown potential in other cohorts Secondary analysis to aid decision-making, particular when decisions are borderline Can build in expected level of association Never be used to negate a primary analysis that shows a potentially positive result External Evidence

17 National Lung Matrix Trial Schema Final biopsy result (diagnostic and/or repeat) mandated for National Lung Matrix Trial entry: Biopsy failure (diagnostic and repeat) or ineligible No actionable genetic aberration Multiple actionable target with open treatment arms Single actionable target with open treatment arm Outcome Measures (common set for all arms with treatment-arm- specific primary): Best objective response rate (ORR), Change in total target lesion size (PCSD), Progression-free survival time (PFS), Time-to- progression (TTP), Overall survival time (OS), Toxicity Allocated to single treatment arm relevant to actionable target prioritised by CI if eligible Allocated to single treatment arm relevant to actionable target if eligible Standard treatment OR recruitment to another relevant trial Allocated to no actionable genetic change (NA) cohort if eligible Standard Clinical Outcome Measures Actionable target but no target therapy arms open

18 No Actionable Genetic Change Cohorts (NA) No actionable genetic aberration Drug NA1 Drug C Drug NA2 Drug E Drug B Etc Test in NEGATIVES once signal demonstrated in POSITIVES Example Pipeline of options that become available sequentially Drug NA1: MEDI4736 Statistical design: adaptive Bayesian design in line with actionable target cohorts Two co-primary outcomes: ORR and PFS6 Initial sample size for interim analysis: N=20 (determined by drug supply) No comparison will be made between treatments

19 Using NA Cohort for Decisions about Next Steps in Research Pathway E.g. Drug D - Crizotinib NA Cohort 15/30 (50%) 10/20 (50%) 0/20 (0%) All-comers design next Enrichment design next D1: Met amplified - mixed D2: ROS1 Gene fusions - mixed

20 Measure Biomarkers Biomarker+Biomarker- RANDOMISE BM+DrugControl BM+Drug RANDOMISE Stratified Trial Design Marker-Based Strategy Design RANDOMISE Marker-based treatment strategyStandard Care Biomarker+Biomarker- Standard Care Measure Biomarkers BM+Drug

21

22 Basket TrialUmbrella Trial

23 When Might RCTs Not Be Needed / Ethical? Oxford / Sackett All or none criterion E.g. Without intervention ALL patients die within 6 months VS With intervention NONE die within 6 months Nick Black criteria Experimentation may be unnecessary when the effect is so dramatic that unknown confounding factors could be ignored Glasziou P, Chalmers I, Rawlins M, McCulloch P. When are randomised trials unnecessary? Picking signal from noise BMJ 2007;334: "10 x rule" – data from non-RCTs can be trusted if the ratio of treatment effects between two alternative therapies > 10. In all other circumstances, the real treatment effects cannot be reliably separated from the effects of biases and random errors without employing RCT design.

24 Compelling Evidence From Single Case Studies / Clinical Experience

25 Compelling Evidence From A Single Arm Trial

26 What Treatment Can Create Such A Dramatic Effect? ALK Inhibitor Patients whose tumour driven by ALK

27 Regulatory perspective on non-randomised evidence 99 trials supported approvals for 45 drugs for 68 rare cancer indications

28 Summary More innovative statistical designs may be needed as trials become complex Umbrella and basket trials are an efficient approach to stratified medicine research Stratified medicine creates multiple rare cancers that challenge conventional statistical designs Developing the right drugs for the right patient to target specific molecular drivers may create dramatic and biologically plausible treatment effects that do not require RCTs to change practice