and Seamon, 2013) Data Binary (no-lesion progression: D) Continuous (log-tumoursize ratio: Y ) Models Logistic models: g Multivariate normal distribution: probability of response f ( ) Pr 1 i S q = = { }{ } ( ) 1 2 log(0.7 1 i0 2 i0 1 1 ) 1 2 1 2 1 Pr(D 1| z ) 1 Pr(D 1| z , ,D 0 ; ) YY i i i i i f y y dy dy y q ¥ -¥ -¥ - = - = = ò ò
(mAugbin) Ø Comparative trials δ0 and δ1 : the mean log tumour size at the final endpoint for control and experimental arms, respectively. δ0 =log(0.7) + x δ1 =log(0.7) - x Figure 1. Power of the three methods for n=75 as the mean log tumour size (x) varies. Figure 2. Power for BOR at T=4. 14
time: # Green at time t • BOR: #Green before Red or time t • Survival: # no Red at censored time t S(t)=P(T > t) =P(min(TD , Ttumour )> t) TD > t Ttumour > t
log(1.2) log(1.2) log(1.2) , ... 1 1 1 (1 ) ... ... ; ... T T i ti Y Y i Ti i Ti t S t f y y dy dy q q p q -¥ -¥ -¥ = = - Õ ò ò ò ∫ ∫ ∫ ∫ Random variable : Y1 Y2 Y3 Y4 (1-π1 ) (1-π2 ) (1-π3 ) (1-π4 ) π : the probability of lesion progression at time log ( tumoursize ratio ) Method log(1.2) log(1.2) log(1.2) log(1.2)
q ( ) 1 1 n i S t n q = = å ( ) ( ) ( ) log 1 S t t S t q q q = - l Var ℓ t ˆ θ ( ) ( )≈ ∇ ℓ t ˆ θ ( ) ( )T Var ˆ θ ( )∇ ℓ t ˆ θ ( ) ( ) expit ℓ t ˆ θ ( )±1.96 Var ℓ t ˆ θ ( ) ( ) ⎧ ⎨ ⎪ ⎩ ⎪ ⎫ ⎬ ⎪ ⎭ ⎪
can obtain H(t) = -log S(t) h(t) = H(t) –H(t-1), where h(0) = 0. § Introduce an arm indicator R to the models, with 0 for control and 1 for experimental arms. H0 : h1 (t)=…=hk (t), for all t ≤ T ( ) { } k j h h T Z D i i ij j ,..., 1 , 1 = - = å = χ2 = Z 1 T ( )...Z k−1 T ( ) ( )Σ−1 Z 1 T ( )...Z k−1 T ( ) ( )T ~ χ2 k−1
arms) Time points cox Logrank mAugbin 4 0.061 0.063 0.054 6 0.059 0.061 0.051 7 0.062 0.062 0.050 346 patients for placebo and 484 patients for cediranib 20 mg The empirical Type I error rate (1000 replicates) for no difference between placebo and cediranib 20mg for time to progression censored at time 4, 6, and 7.
rank test and Augmented binary for time to progression censored at time 4 { } { } exp [0.4 0.6 0.8 1] log(0.7) [0.4 0.6 0.8 1] log(0.7) T con T x x = ´ + = ´ - µ µ 0.1 , and 0.6, , 1,...,4 ij ii i j i j S = " ¹ S = = Binary (no-lesion progression: D) Continuous (log-tumoursize ratio: Y ) §α=-2, corresponding to a 12% chance of failing §β=0 ( ) logit p baseline a b = + ´ X Logrank mAugbin 0 0.049 0.05 0.1 0.125 0.123 0.2 0.315 0.319 0.3 0.587 0.589 0.4 0.804 0.812 ( ) 2 , ~ S µ Y N
construct a survival function Ø Calculate median/mean & SE Ø Comparative trial n R-Shiny of the augmented binary method for using fixed time and survival Future /current work n Investigate why the method seems to boost power for the response/BOR setting but so far not for the PFS setting.