New Developments in East

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Carmel Parsons
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1 New Developments in East MAMS: Multi-arm Multi-stage Trials Presented at the Fifth East User Group Meeting March 16, 2016 Cyrus Mehta, Ph.D. President, Cytel Inc

2 Multi-arm Multi-stage Designs Generaliza8on of group sequen8al design to more than two arms An alterna8ve to the combina8on func8on approach of Posch et. al. (SiM, 2005) Current implementa8on: trial stops if any arm crosses efficacy boundary trial stops if all arms cross fu8lity boundary drop the losers at each interim look Under development: dose selec8on and adap8ve SSR 2

3 Mathematical Framework K- look GSD Only 1 comparison to a control, made K 8mes K- look MAMS: K K D comparisons to common control, made K 8mes Generaliza8on of DunneR s test i 1 i 1 [ ] P ( I W < e and W e ) = α 0 j j i i i= 1 j= 1 [ ] P ( I max{w...w } < e and max{w...w } e ) = α 0 j1 jd j i1 id i i= 1 j= 1 3

4 Boundary Computations W sta8s8c e1 W1 e2 W2 W3 e3 Two Arm Trial: Look 1 Look 2 Look 3 W j, j=1,2,3, are scalers. Trial stops if W 1 e 1 or W 2 e 2 or W 3 e 3 We want P 0 (W 1 e 1 or W 2 e 2 or W 3 e 3 )=α Computa8ons are simplified because W j and (W j - W j- 1 ) are independent 4

5 Boundary Computations W sta8s8c e1 W1 e2 W2 W3 e3 Mul-- arm Trial: Look 1 Look 2 Look 3 W j =(W j1,w j2,...w jd ) are vectors. Trial stops if max(w 11,W 12,...W 1D ) e 1 or max(w 21,W 22,...W 2D ) e 2 or max(w 31,W 32,...W 3D ) e 3 Want P 0 {max(w 11,W 12,...W 1D ) e 1 or max(w 21,W 22,...W 2D ) e 2 or max(w 31,W 32,...W 3D ) e 3 }=α Computa8ons are complex because the components (W j1,w j2,...w jd ) are correlated whereas W j and (W j - W j- 1 ) are independent 5

6 Inhance Trial: Chronic Obstructive Pulmonary Disease Once daily bronchodilators for COPD (Am. J. Respiratory & Cri8cal Care, vol 182, 2010) Compare three doses (150 mg, 300 mg, 500 mg) of Indacaterol to Placebo Endpoint: Week 12 change from baseline in 24 hour trough FEV1 Expect differences from placebo of between 0.14 and 0.18 liters with standard devia8on σ=0.5 Design for 90% power at one- sided α=

7 Two-arm, Three-look GSD Requires 165 pa8ents/arm for δ=0.18, σ=0.5 Expected sample size under H1 is 132/arm 7

8 Four-arm, Three-look MAMS Requires 130 pa8ents/arm for δ=0.18, σ=0.5 Expected sample size under H1 is 104/arm 8

9 Compare the 2 arm and 4-arm boundaries 2- arm boundaries on Z- scale 4- arm boundaries on Z- scale 9

10 Higher hurdle with 4-arm trial Table of Boundary Comparisons Look Info Frac8on Two Arm Four Arm Plot of Boundary Comparisons

11 Boundary and Sample Size Comparison The boundaries for the 4- arm trial are higher than for the 2 arm trial This compensates for the greater probability of boundary crossing under H0 But the sample size/arm is lower for 4- arm trial. (More chances to exit under H1) What would happen if the value of δ was not the same for each treatment 11

pa8ents/arm than 2- arm design The higher efficacy boundary hurdle is not offset by

12 4-arm design with different δ values Same boundaries, but requires commitment of 168/arm Expected sample size under H1 is 135/arm Here 4- arm design requires more pa8ents/arm than 2- arm design The higher efficacy boundary hurdle is not offset by extra opportuni8es to cross the efficacy boundary because only one dose has a strong effect 12

13 Introduce a futility boundary for 671patient trial with δ=(0.18,.14,.14) 13

Impact of futility boundary; 2% power drop Power

boundary The efficacy boundary is unchanged since

fu8lity only if ALL the arms cross the fu8lity

14 Impact of futility boundary; 2% power drop Power dropped to 88% due to introduc8on of a fu8lity boundary The efficacy boundary is unchanged since fu8lity boundary is non- binding Trial stops for fu8lity only if ALL the arms cross the fu8lity boundary But individual arms that cross the fu-lity boundary will be dropped 14

15 Simulate trial for additional insight 15

16 More simulation details 16

17 Marginal and Detailed Outcome Tables 17

18 What if two treatments were ineffective 18

19 More simulation details 19

20 Marginal and detailed outcome tables 20

21 Simulation under the global null 21

22 Comparison with Existing Method East can compute efficacy and fu8lity boundaries for 6- arm 4- look STAMPEDE trial in 4 minutes (Sydes et al, 2009) Compare with Wason and Jaki (Sta8s8cs in Medicine, 2012) for 4- arm 4- look TAILOR trial: 22

23 Still being tested for East 6.4 Recompute boundaries if arms are dropped Make a dose selec8on at an interim look Increase sample size in promising zone at an interim look When this is complete, it will be possible to perform head- to- head comparison with 2- stage methods of Posch et al based on combining p- values and closed tes<ng which is also available in East

24 Future Development of MAMS Parameter Es8ma8on P- values Point es8mates Confidence Intervals This is s8ll an area of research 24

25 Reference Paper 25

Adaptive designs beyond p-value combination methods. Ekkehard Glimm, Novartis Pharma EAST user group meeting Basel, 31 May 2013

Adaptive designs beyond p-value combination methods Ekkehard Glimm, Novartis Pharma EAST user group meeting Basel, 31 May 2013 Outline Introduction Combination-p-value method and conditional error function