From 68a7731b4118fffbbab269c42679a18d23d83e37 Mon Sep 17 00:00:00 2001 From: Quarto GHA Workflow Runner Date: Sat, 25 Nov 2023 14:56:01 +0000 Subject: [PATCH] Built site for gh-pages --- .nojekyll | 2 +- authors.html | 30 ++ chapter_10.html | 20 +- chapter_16.html | 480 +++++++++--------- .../fig-predben_continuous_outcome-1.png | Bin 65955 -> 64488 bytes index.html | 2 +- search.json | 8 +- 7 files changed, 286 insertions(+), 256 deletions(-) diff --git a/.nojekyll b/.nojekyll index e522b81..6597982 100644 --- a/.nojekyll +++ b/.nojekyll @@ -1 +1 @@ -6035dd7c \ No newline at end of file +0c41c298 \ No newline at end of file diff --git a/authors.html b/authors.html index e071a4f..b9ffc5a 100644 --- a/authors.html +++ b/authors.html @@ -249,6 +249,18 @@

11  +Carl de Moor +Certara +Cambridge, MA +United States + + +Changyu Shen +Biogen +Cambridge, MA +United States + + Christina Read Utrecht University Utrecht @@ -261,6 +273,18 @@

11  +Fabio Pellegrini +Biogen Spain +Madrid +Spain + + +Gabrielle Simoneau +Biogen Canada +Toronto +Canada + + Grammati Sarri Cytel Inc. London @@ -284,6 +308,12 @@

11  +Tammy Jiang +Biogen +Cambridge, MA +United States + diff --git a/chapter_10.html b/chapter_10.html index 63f00cc..a136284 100644 --- a/chapter_10.html +++ b/chapter_10.html @@ -860,16 +860,16 @@

sucra.uc$ranking.random
 ADA160/80     FIL100     FIL200 GOL200/100      INF10       INF5    OZA0.92 
-0.27727273 0.18818182 0.39818182 0.62636364 0.60636364 0.73272727 0.80000000 
+0.28000000 0.16272727 0.45090909 0.64818182 0.59454545 0.75272727 0.76545455 
        PBO      TOF10      UPA45       UST6     VED300 
-0.01909091 0.39909091 0.97636364 0.35636364 0.62000000 
+0.01636364 0.39545455 0.96818182 0.34181818 0.62363636
-

These results indicate that 97.6% of the evaluated treatments are worse than UPA45.

+

These results indicate that 96.8% of the evaluated treatments are worse than UPA45.

diff --git a/chapter_16.html b/chapter_16.html index 8094ed6..84d7bc3 100644 --- a/chapter_16.html +++ b/chapter_16.html @@ -293,13 +293,13 @@

head(ds)
-
  studyid treat         z1         z2  y
-1       1     1 -0.8214272  1.3593988  8
-2       1     1  1.1727588  2.1235720  7
-3       1     0  2.6254253 -0.3173225 12
-4       1     1  0.7068322  1.7374627  7
-5       1     1  2.4244049  1.4709154  6
-6       1     0 -1.3373327 -0.3221516 11
+
  studyid treat         z1          z2  y
+1       1     1 -0.4748633 -1.25667779  8
+2       1     0 -1.2534519  0.77525678 11
+3       1     0 -1.3539458  0.04621007 11
+4       1     1  0.7058639  1.22097664  9
+5       1     1 -0.8753868  0.41972871 10
+6       1     0  0.1235388 -0.76500497 11

The simulated dataset contains information on the following variables:

@@ -326,9 +326,9 @@

0
-(N=266)
+(N=290)
1
-(N=334)
+(N=310) Overall
(N=600)
@@ -342,15 +342,15 @@

Mean (SD) -0.0403 (1.01) --0.00989 (1.11) -0.0124 (1.06) +-0.0590 (1.06) +-0.0854 (1.06) +-0.0727 (1.06) Median [Min, Max] -0.0517 [-3.07, 2.84] -0.0205 [-3.49, 2.75] -0.0272 [-3.49, 2.84] +-0.0917 [-3.05, 2.81] +-0.0442 [-3.99, 2.60] +-0.0717 [-3.99, 2.81] z2 @@ -360,15 +360,15 @@

Mean (SD) -0.0816 (0.985) -0.0000115 (1.02) -0.0362 (1.00) +0.00458 (0.978) +0.0378 (0.901) +0.0217 (0.938) Median [Min, Max] -0.0892 [-2.46, 2.81] -0.0160 [-2.81, 2.75] -0.0497 [-2.81, 2.81] +-0.0125 [-2.74, 2.65] +0.0230 [-2.67, 2.87] +0.00301 [-2.74, 2.87] studyid @@ -378,38 +378,38 @@

1 -41 (15.4%) -59 (17.7%) +50 (17.2%) +50 (16.1%) 100 (16.7%) 2 -44 (16.5%) -56 (16.8%) +58 (20.0%) +42 (13.5%) 100 (16.7%) 3 -47 (17.7%) -53 (15.9%) +50 (17.2%) +50 (16.1%) 100 (16.7%) 4 -42 (15.8%) -58 (17.4%) +42 (14.5%) +58 (18.7%) 100 (16.7%) 5 -47 (17.7%) -53 (15.9%) +44 (15.2%) +56 (18.1%) 100 (16.7%) 6 -45 (16.9%) -55 (16.5%) +46 (15.9%) +54 (17.4%) 100 (16.7%) @@ -462,7 +462,7 @@

+<environment: 0x5596340f7250>

We can fit the treatment effect model as follows:

@@ -485,36 +485,36 @@

+delta[2] -4.6681 -3.6422 -2.7416 -2.4294 -1.9663 +gamma[1] -0.5592 -0.5022 -0.4783 -0.4601 -0.4203 +gamma[2] 0.4443 0.4665 0.4898 0.5028 0.5365 +sd 1.3564 1.6883 2.0863 2.4144 2.9956

@@ -525,10 +525,10 @@

round(treatment.effect(ipd, samples, newpatient = c(z1 = 1, z2 = 0.5)), 2)
0.025   0.5 0.975 
--3.75 -2.63 -0.99 
+-4.93 -3.01 -2.24
-

This means that the predicted outcome for patient with covariate values z1 = 1 and z2 = 0.5 will differ by -2.63 units when receiving the active treatment (treat = 1) as compared to the control treatment (treat = 0).

+

This means that the predicted outcome for patient with covariate values z1 = 1 and z2 = 0.5 will differ by -3.01 units when receiving the active treatment (treat = 1) as compared to the control treatment (treat = 0).

We can also predict treatment benefit for all patients in the sample, and look at the distribution of predicted benefit.

library(dplyr)
@@ -590,7 +590,7 @@ 

round(treatment.effect(ipd, samples, newpatient = c(1,0.5)), 2)

0.025   0.5 0.975 
--4.18 -2.74 -0.22 
+-4.84 -3.55 -2.48
@@ -604,13 +604,13 @@

ds2 <- generate_ipdma_example(type = "binary")
 head(ds2)
-
  studyid treat          w1         w2 y
-1       1     1  0.97798495  0.2418152 1
-2       1     0 -0.84567231 -1.9754609 0
-3       1     0  0.09372058 -0.5530126 1
-4       1     0 -1.88483041  1.3732045 1
-5       1     1  0.86942122  0.0903347 1
-6       1     1 -1.14036111 -1.4076500 1
+
  studyid treat          w1          w2 y
+1       1     0 -0.73745864 -2.47059002 1
+2       1     0  1.61868842  0.69188892 1
+3       1     0  0.96062482 -1.15073512 0
+4       1     1  1.68194479 -0.35461869 1
+5       1     1  0.08564951 -0.92986933 0
+6       1     0 -0.51586759  0.02180216 1

The simulated dataset contains information on the following variables:

@@ -637,9 +637,9 @@

0
-(N=290)
+(N=297)
1
-(N=310)
+(N=303) Overall
(N=600)
@@ -653,15 +653,15 @@

Mean (SD) -0.0292 (1.03) -0.0278 (0.873) -0.0284 (0.952) +-0.0410 (1.04) +-0.0312 (1.05) +-0.0361 (1.05) Median [Min, Max] -0.0423 [-4.35, 2.50] --0.0243 [-2.26, 2.83] --0.0125 [-4.35, 2.83] +-0.0612 [-3.56, 3.12] +-0.0597 [-3.53, 2.78] +-0.0605 [-3.56, 3.12] w2 @@ -671,15 +671,15 @@

Mean (SD) --0.117 (0.987) --0.0358 (0.961) --0.0752 (0.974) +-0.00812 (0.960) +-0.121 (0.937) +-0.0652 (0.949) Median [Min, Max] --0.141 [-2.63, 2.57] --0.00852 [-2.63, 3.05] --0.0671 [-2.63, 3.05] +-0.0152 [-3.17, 3.18] +-0.195 [-2.32, 2.42] +-0.129 [-3.17, 3.18] studyid @@ -689,38 +689,38 @@

1 -52 (17.9%) -48 (15.5%) +53 (17.8%) +47 (15.5%) 100 (16.7%) 2 -54 (18.6%) -46 (14.8%) +45 (15.2%) +55 (18.2%) 100 (16.7%) 3 -42 (14.5%) -58 (18.7%) +49 (16.5%) +51 (16.8%) 100 (16.7%) 4 -52 (17.9%) -48 (15.5%) +49 (16.5%) +51 (16.8%) 100 (16.7%) 5 -44 (15.2%) -56 (18.1%) +53 (17.8%) +47 (15.5%) 100 (16.7%) 6 -46 (15.9%) -54 (17.4%) +48 (16.2%) +52 (17.2%) 100 (16.7%) @@ -778,7 +778,7 @@

+<environment: 0x559637f832c8>
@@ -797,37 +797,37 @@

+ 2.5% 25% 50% 75% 97.5% +alpha[1] -0.53205 -0.28253 -0.19605 -0.05655 0.28018 +alpha[2] -1.14026 -0.68274 -0.43837 -0.11271 0.02442 +alpha[3] -0.68259 -0.40154 -0.26974 -0.08671 0.10882 +alpha[4] -0.71084 -0.52669 -0.30900 -0.14016 0.11853 +alpha[5] -0.73357 -0.48145 -0.41020 -0.19635 -0.04802 +alpha[6] -0.93229 -0.55623 -0.43720 -0.27949 0.19616 +beta[1] -0.30817 -0.07763 -0.01204 0.09767 0.29586 +beta[2] -0.41297 -0.14288 -0.09653 -0.03259 0.10662 +delta[1] 0.00000 0.00000 0.00000 0.00000 0.00000 +delta[2] -0.60965 -0.54179 -0.36293 -0.22600 0.21256 +gamma[1] -0.49007 -0.27695 -0.13693 -0.04969 0.04752 +gamma[2] -0.05721 0.06727 0.19971 0.29631 0.57380 +sd 0.04809 0.10680 0.23808 0.58446 0.95884

The predicted treatment benefit for a new patient with covariates w1 = 1.6 and w2 = 1.3 is given as:

@@ -835,10 +835,10 @@

round(treatment.effect(ipd2, samples, newpatient = c(w1 = 1.6, w2 = 1.3)), 2)
0.025   0.5 0.975 
- 0.57  0.99  1.42 
+ 0.36 0.74 1.24
-

In other words, the aforementioned patient 0.99 (95% Credibility Interval: 0.57 to 1.42)

+

In other words, the aforementioned patient 0.74 (95% Credibility Interval: 0.36 to 1.24)

@@ -853,13 +853,13 @@

ds3 <- generate_ipdnma_example(type = "continuous")
 head(ds3)
-
  studyid treat         z1         z2  y
-1       1     2  0.8973962  0.9590160  8
-2       1     2  0.6877835 -3.5259374  4
-3       1     2 -1.1966600  0.5973101  9
-4       1     1  0.6523842 -0.6923363 11
-5       1     1 -0.7626743 -0.5484090 11
-6       1     1 -0.8399396  0.8927062 11
+
  studyid treat          z1         z2  y
+1       1     2 -0.29014630  0.3343684  9
+2       1     2 -2.12394415  1.8528517 11
+3       1     1  0.82391508  0.9143050 11
+4       1     2  1.28022147 -1.5851092  6
+5       1     2  0.08384111  0.4531303  8
+6       1     1 -1.45182331  1.6525457 11

Let us look into the data a bit in more detail:

@@ -881,11 +881,11 @@

1
-(N=368)
+(N=345)
2
-(N=355)
+(N=357) 3
-(N=277)
+(N=298) Overall
(N=1000)
@@ -900,17 +900,17 @@

Mean (SD) -0.0568 (1.01) -0.104 (1.00) --0.0155 (0.982) -0.0536 (1.00) +0.0155 (0.990) +0.0631 (0.946) +0.0137 (0.990) +0.0319 (0.974) Median [Min, Max] -0.0415 [-2.53, 3.38] -0.0386 [-2.51, 3.72] -0.0318 [-2.70, 2.71] -0.0400 [-2.70, 3.72] +0.00344 [-3.30, 2.72] +0.0562 [-3.46, 2.96] +0.0241 [-2.94, 2.64] +0.0420 [-3.46, 2.96] z2 @@ -921,17 +921,17 @@

Mean (SD) --0.0189 (0.984) -0.0133 (0.929) --0.0582 (1.05) --0.0184 (0.983) +0.0100 (1.06) +-0.0400 (0.999) +-0.0626 (1.07) +-0.0295 (1.04) Median [Min, Max] --0.0182 [-2.61, 2.74] --0.0185 [-3.53, 2.73] --0.206 [-2.89, 2.88] --0.0458 [-3.53, 2.88] +0.0241 [-2.90, 3.31] +-0.0760 [-2.70, 2.39] +-0.0994 [-2.79, 3.32] +-0.0578 [-2.90, 3.32] studyid @@ -942,72 +942,72 @@

1 -52 (14.1%) -48 (13.5%) +51 (14.8%) +49 (13.7%) 0 (0%) 100 (10.0%) 2 -53 (14.4%) -47 (13.2%) +45 (13.0%) +55 (15.4%) 0 (0%) 100 (10.0%) 3 -49 (13.3%) -51 (14.4%) +51 (14.8%) +49 (13.7%) 0 (0%) 100 (10.0%) 4 -49 (13.3%) +48 (13.9%) 0 (0%) -51 (18.4%) +52 (17.4%) 100 (10.0%) 5 -53 (14.4%) +57 (16.5%) 0 (0%) -47 (17.0%) +43 (14.4%) 100 (10.0%) 6 0 (0%) -50 (14.1%) -50 (18.1%) +56 (15.7%) +44 (14.8%) 100 (10.0%) 7 0 (0%) -53 (14.9%) -47 (17.0%) +47 (13.2%) +53 (17.8%) 100 (10.0%) 8 -31 (8.4%) -42 (11.8%) -27 (9.7%) +28 (8.1%) +37 (10.4%) +35 (11.7%) 100 (10.0%) 9 -51 (13.9%) -27 (7.6%) -22 (7.9%) +36 (10.4%) +25 (7.0%) +39 (13.1%) 100 (10.0%) 10 -30 (8.2%) -37 (10.4%) -33 (11.9%) +29 (8.4%) +39 (10.9%) +32 (10.7%) 100 (10.0%) @@ -1078,7 +1078,7 @@

+<environment: 0x559638277b10>
samples <- ipd.run(ipd3, n.chains = 2, n.iter = 20, 
                    pars.save = c("alpha", "beta", "delta", "sd", "gamma"))
@@ -1105,54 +1105,54 @@

+gamma[2,2] 0.60625 0.63123 0.6486 0.6596 0.6793 +gamma[3,2] 0.44309 0.47550 0.4881 0.5070 0.5401 +sd 0.07331 0.09925 0.1138 0.1255 0.1966

As before, we can use the treatment.effect() function of bipd to estimate relative effects for new patients.

@@ -1161,11 +1161,11 @@

$`treatment 2`
     0.025       0.5     0.975 
--2.623772 -2.511195 -2.377612 
+-2.526016 -2.343607 -2.134030 
 
 $`treatment 3`
      0.025        0.5      0.975 
--0.7427505 -0.6510396 -0.5205575 
+-0.7111509 -0.5663623 -0.3391895

This gives us the relative effects for all treatments versus the reference. To obtain relative effects between active treatments we need some more coding:

@@ -1182,7 +1182,7 @@

samples.all$gamma.3.2.*newpatient[2]) )
-
[1] -1.866705
+
[1] -1.77163
quantile(samples.all$delta.2.+samples.all$gamma.2.1.*
            newpatient[1]+samples.all$gamma.2.2.*newpatient[2]
@@ -1191,7 +1191,7 @@ 

, probs = 0.025)

     2.5% 
--2.004625 
+-1.929766
quantile(samples.all$delta.2.+samples.all$gamma.2.1.*
            newpatient[1]+samples.all$gamma.2.2.*newpatient[2]
@@ -1200,7 +1200,7 @@ 

, probs = 0.975)

    97.5% 
--1.736188 
+-1.609052
@@ -1226,9 +1226,9 @@

          
            s1 s2 s3
-  treat A: 51 57 34
-  treat B: 49  0 31
-  treat C:  0 43 35
+ treat A: 49 49 39 + treat B: 51 0 35 + treat C: 0 51 26 @@ -1357,18 +1357,18 @@

study 1 --2.964 (SE = 0.047 ) +-3.002 (SE = 0.046 ) study 2 --1.167 (SE = 0.068 ) +-1.133 (SE = 0.055 ) study 3 --2.945 (SE = 0.080 ) --1.012 (SE = 0.075 ) +-2.979 (SE = 0.069 ) +-1.022 (SE = 0.075 ) @@ -1434,23 +1434,23 @@

+delta[2] -3.0663 -3.0142 -2.9836 -2.9616 -2.8965 +delta[3] -1.1931 -1.1228 -1.0847 -1.0589 -1.0031 +gamma[1,1] -0.8431 -0.8115 -0.7846 -0.7507 -0.7067 +gamma[2,1] 0.7255 0.8042 0.8278 0.8593 0.9194 +gamma[1,2] -0.5897 -0.5238 -0.4875 -0.4619 -0.3775 +gamma[2,2] 0.3046 0.3953 0.4236 0.4628 0.5240
# calculate  treatment effects
 samples.all = data.frame(rbind(samps4.3[[1]], samps4.3[[2]]))
@@ -1461,21 +1461,21 @@ 

samples.all$gamma.2.1.*newpatient[2] )

-
[1] -1.8603
+
[1] -2.110639
quantile(samples.all$delta.2.+samples.all$gamma.1.1.*newpatient[1]+
            samples.all$gamma.2.1.*newpatient[2]
          , probs = 0.025)
     2.5% 
--2.047671 
+-2.282136
quantile(samples.all$delta.2.+samples.all$gamma.1.1.*newpatient[1]+
            samples.all$gamma.2.1.*newpatient[2]
          , probs = 0.975)
    97.5% 
--1.702371 
+-1.919591
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