tests/testthat/_snaps/te_weights.md
In CAM-Roche/RandomisedTrialsEmulation: Causal Analysis of Observational Time-to-Event Data
show works for te_weights_spec objects
Code
show_weight_models(object)
Output
Weight Models for Informative Censoring
---------------------------------------
Weight Models for Treatment Switching
-------------------------------------
Code
show(object@censor_weights)
Output
- Numerator formula: 1 - censored ~ age + x4
- Denominator formula: 1 - censored ~ age + x2 + x4
- Numerator model is pooled across treatment arms. Denominator model is not pooled.
- Model fitter type: te_stats_glm_logit
- Weight models not fitted. Use calculate_weights()
Code
show(object_w_weights)
Output
Trial Sequence Object
Estimand: Per-protocol
Data:
- N: 321 observations from 89 patients
id period treatment x1 x2 x3 x4 age age_s
<int> <int> <num> <num> <num> <int> <num> <num> <num>
1: 1 0 1 1 1.146148362 0 0.7342030 36 0.08333333
2: 1 1 1 1 0.002200337 0 0.7342030 37 0.16666667
---
320: 99 1 1 0 -1.106480738 1 0.5752681 66 2.58333333
321: 99 2 0 0 1.650478074 1 0.5752681 67 2.66666667
outcome censored eligible time_on_regime wt wtS wtC
<num> <int> <num> <num> <num> <num> <num>
1: 0 0 1 0 1.8629733 0.9663301 1.9278851
2: 0 0 0 1 0.8873368 0.9805333 0.9049533
---
320: 0 0 0 1 1.1119420 1.1117399 1.0001818
321: 0 0 0 2 1.6082501 1.5974091 1.0067866
IPW for informative censoring:
- Numerator formula: 1 - censored ~ age + x4
- Denominator formula: 1 - censored ~ age + x2 + x4
- Numerator model is pooled across treatment arms. Denominator model is not pooled.
- Model fitter type: te_stats_glm_logit
- View weight model summaries with show_weight_models()
IPW for treatment switch censoring:
- Numerator formula: treatment ~ age + x4
- Denominator formula: treatment ~ age + x2 + x4
- Model fitter type: te_stats_glm_logit
- View weight model summaries with show_weight_models()
Sequence of Trials Data:
- Use set_expansion_options() and expand_trials() to construct the sequence of trials dataset.
Outcome model:
- Outcome model not specified. Use set_outcome_model()
Code
show_weight_models(object_w_weights)
Output
Weight Models for Informative Censoring
---------------------------------------
[[n]]
Model: P(censor_event = 0 | X) for numerator
term estimate std.error statistic p.value
(Intercept) -2.0539683 0.71502953 -2.872564 4.071553e-03
age 0.1017218 0.01953751 5.206488 1.924486e-07
x4 -0.1659871 0.16180629 -1.025839 3.049677e-01
null.deviance df.null logLik AIC BIC deviance df.residual nobs
256.5508 320 -108.7563 223.5127 234.827 217.5127 318 321
path
/tempdir/model_n.rds
[[d0]]
Model: P(censor_event = 0 | X, previous treatment = 0) for denominator
term estimate std.error statistic p.value
(Intercept) -2.9993953 0.96683551 -3.102281 1.920357e-03
age 0.1163969 0.02718057 4.282359 1.849224e-05
x2 -1.0232012 0.29805533 -3.432924 5.971105e-04
x4 -0.4475813 0.22040610 -2.030712 4.228420e-02
null.deviance df.null logLik AIC BIC deviance df.residual nobs
172.8729 169 -61.88922 131.7784 144.3216 123.7784 166 170
path
/tempdir/model_d0.rds
[[d1]]
Model: P(censor_event = 0 | X, previous treatment = 1) for denominator
term estimate std.error statistic p.value
(Intercept) 0.20802433 1.52244361 0.1366384 0.89131658
age 0.06527658 0.03837472 1.7010309 0.08893719
x2 -0.17882403 0.38088489 -0.4694963 0.63871496
x4 -0.29105768 0.36381453 -0.8000166 0.42370117
null.deviance df.null logLik AIC BIC deviance df.residual nobs
68.21358 150 -31.9729 71.9458 84.01492 63.9458 147 151
path
/tempdir/model_d1.rds
Weight Models for Treatment Switching
-------------------------------------
[[n1]]
Model: P(treatment = 1 | previous treatment = 1) for numerator
term estimate std.error statistic p.value
(Intercept) 1.23927187 0.7983788 1.5522354 0.1206059128
age -0.01404746 0.0173406 -0.8100905 0.4178881792
x4 0.79347625 0.2162862 3.6686408 0.0002438434
null.deviance df.null logLik AIC BIC deviance df.residual nobs
188.829 150 -85.39205 176.7841 185.8359 170.7841 148 151
path
/tempdir/model_n1.rds
[[d1]]
Model: P(treatment = 1 | previous treatment = 1) for denominator
term estimate std.error statistic p.value
(Intercept) 1.36823919 0.81213243 1.6847489 0.0920370351
age -0.01484948 0.01760642 -0.8434126 0.3989977540
x2 0.33852867 0.19500928 1.7359619 0.0825705753
x4 0.78724133 0.21455503 3.6691814 0.0002433283
null.deviance df.null logLik AIC BIC deviance df.residual nobs
188.829 150 -83.82451 175.649 187.7181 167.649 147 151
path
/tempdir/model_d1.rds
[[n0]]
Model: P(treatment = 1 | previous treatment = 0) for numerator
term estimate std.error statistic p.value
(Intercept) 0.084860976 0.67437905 0.12583572 8.998620e-01
age 0.001012695 0.01625994 0.06228156 9.503386e-01
x4 1.108633792 0.21415037 5.17689418 2.256101e-07
null.deviance df.null logLik AIC BIC deviance df.residual nobs
232.2705 169 -94.30944 194.6189 204.0263 188.6189 167 170
path
/tempdir/model_n0.rds
[[d0]]
Model: P(treatment = 1 | previous treatment = 0) for denominator
term estimate std.error statistic p.value
(Intercept) 0.1214193693 0.67770501 0.17916257 8.578101e-01
age 0.0002235908 0.01632757 0.01369406 9.890741e-01
x2 0.1068088770 0.19102028 0.55914942 5.760597e-01
x4 1.1040693183 0.21338578 5.17405287 2.290700e-07
null.deviance df.null logLik AIC BIC deviance df.residual nobs
232.2705 169 -94.15216 196.3043 208.8475 188.3043 166 170
path
/tempdir/model_d0.rds
Code
show(object_w_weights@switch_weights@fitted$n1)
Output
Model: P(treatment = 1 | previous treatment = 1) for numerator
term estimate std.error statistic p.value
(Intercept) 1.23927187 0.7983788 1.5522354 0.1206059128
age -0.01404746 0.0173406 -0.8100905 0.4178881792
x4 0.79347625 0.2162862 3.6686408 0.0002438434
null.deviance df.null logLik AIC BIC deviance df.residual nobs
188.829 150 -85.39205 176.7841 185.8359 170.7841 148 151
path
/tempdir/model_n1.rds
weight_model_data_indices works
Code
data.frame(values = rle_result$values, lengths = rle_result$lengths)
Output
values lengths
1 TRUE 1
2 FALSE 5
3 TRUE 2
4 FALSE 2
5 TRUE 1
6 FALSE 4
7 TRUE 5
8 FALSE 4
9 TRUE 1
10 FALSE 7
11 TRUE 1
12 FALSE 8
13 TRUE 1
14 FALSE 1
15 TRUE 1
16 FALSE 5
17 TRUE 9
18 FALSE 1
19 TRUE 3
20 FALSE 1
21 TRUE 10
22 FALSE 1
23 TRUE 4
24 FALSE 2
25 TRUE 1
26 FALSE 1
27 TRUE 1
28 FALSE 4
29 TRUE 6
30 FALSE 1
31 TRUE 1
32 FALSE 2
33 TRUE 1
34 FALSE 2
35 TRUE 5
36 FALSE 2
37 TRUE 2
38 FALSE 2
39 TRUE 6
40 FALSE 1
41 TRUE 1
42 FALSE 5
43 TRUE 1
44 FALSE 1
45 TRUE 6
46 FALSE 1
47 TRUE 1
48 FALSE 17
49 TRUE 2
50 FALSE 3
51 TRUE 4
52 FALSE 1
53 TRUE 2
54 FALSE 2
55 TRUE 1
56 FALSE 1
57 TRUE 2
58 FALSE 1
59 TRUE 18
60 FALSE 1
61 TRUE 1
62 FALSE 2
63 TRUE 6
64 FALSE 7
65 TRUE 3
66 FALSE 3
67 TRUE 3
68 FALSE 3
69 TRUE 1
70 FALSE 1
71 TRUE 2
72 FALSE 2
73 TRUE 3
74 FALSE 3
75 TRUE 1
76 FALSE 3
77 TRUE 1
78 FALSE 1
79 TRUE 3
80 FALSE 2
81 TRUE 2
82 FALSE 2
83 TRUE 1
84 FALSE 2
85 TRUE 11
86 FALSE 2
87 TRUE 2
88 FALSE 5
89 TRUE 3
90 FALSE 2
91 TRUE 6
92 FALSE 1
93 TRUE 1
94 FALSE 3
95 TRUE 1
96 FALSE 2
97 TRUE 5
98 FALSE 7
99 TRUE 4
100 FALSE 1
101 TRUE 5
102 FALSE 1
103 TRUE 2
104 FALSE 2
105 TRUE 2
106 FALSE 3
107 TRUE 1
108 FALSE 2
CAM-Roche/RandomisedTrialsEmulation documentation built on April 14, 2025, 7:44 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
show works for te_weights_spec objects
Code
show_weight_models(object)
Output
Weight Models for Informative Censoring
---------------------------------------
Weight Models for Treatment Switching
-------------------------------------
Code
show(object@censor_weights)
Output
- Numerator formula: 1 - censored ~ age + x4
- Denominator formula: 1 - censored ~ age + x2 + x4
- Numerator model is pooled across treatment arms. Denominator model is not pooled.
- Model fitter type: te_stats_glm_logit
- Weight models not fitted. Use calculate_weights()
Code
show(object_w_weights)
Output
Trial Sequence Object
Estimand: Per-protocol
Data:
- N: 321 observations from 89 patients
id period treatment x1 x2 x3 x4 age age_s
<int> <int> <num> <num> <num> <int> <num> <num> <num>
1: 1 0 1 1 1.146148362 0 0.7342030 36 0.08333333
2: 1 1 1 1 0.002200337 0 0.7342030 37 0.16666667
---
320: 99 1 1 0 -1.106480738 1 0.5752681 66 2.58333333
321: 99 2 0 0 1.650478074 1 0.5752681 67 2.66666667
outcome censored eligible time_on_regime wt wtS wtC
<num> <int> <num> <num> <num> <num> <num>
1: 0 0 1 0 1.8629733 0.9663301 1.9278851
2: 0 0 0 1 0.8873368 0.9805333 0.9049533
---
320: 0 0 0 1 1.1119420 1.1117399 1.0001818
321: 0 0 0 2 1.6082501 1.5974091 1.0067866
IPW for informative censoring:
- Numerator formula: 1 - censored ~ age + x4
- Denominator formula: 1 - censored ~ age + x2 + x4
- Numerator model is pooled across treatment arms. Denominator model is not pooled.
- Model fitter type: te_stats_glm_logit
- View weight model summaries with show_weight_models()
IPW for treatment switch censoring:
- Numerator formula: treatment ~ age + x4
- Denominator formula: treatment ~ age + x2 + x4
- Model fitter type: te_stats_glm_logit
- View weight model summaries with show_weight_models()
Sequence of Trials Data:
- Use set_expansion_options() and expand_trials() to construct the sequence of trials dataset.
Outcome model:
- Outcome model not specified. Use set_outcome_model()
Code
show_weight_models(object_w_weights)
Output
Weight Models for Informative Censoring
---------------------------------------
[[n]]
Model: P(censor_event = 0 | X) for numerator
term estimate std.error statistic p.value
(Intercept) -2.0539683 0.71502953 -2.872564 4.071553e-03
age 0.1017218 0.01953751 5.206488 1.924486e-07
x4 -0.1659871 0.16180629 -1.025839 3.049677e-01
null.deviance df.null logLik AIC BIC deviance df.residual nobs
256.5508 320 -108.7563 223.5127 234.827 217.5127 318 321
path
/tempdir/model_n.rds
[[d0]]
Model: P(censor_event = 0 | X, previous treatment = 0) for denominator
term estimate std.error statistic p.value
(Intercept) -2.9993953 0.96683551 -3.102281 1.920357e-03
age 0.1163969 0.02718057 4.282359 1.849224e-05
x2 -1.0232012 0.29805533 -3.432924 5.971105e-04
x4 -0.4475813 0.22040610 -2.030712 4.228420e-02
null.deviance df.null logLik AIC BIC deviance df.residual nobs
172.8729 169 -61.88922 131.7784 144.3216 123.7784 166 170
path
/tempdir/model_d0.rds
[[d1]]
Model: P(censor_event = 0 | X, previous treatment = 1) for denominator
term estimate std.error statistic p.value
(Intercept) 0.20802433 1.52244361 0.1366384 0.89131658
age 0.06527658 0.03837472 1.7010309 0.08893719
x2 -0.17882403 0.38088489 -0.4694963 0.63871496
x4 -0.29105768 0.36381453 -0.8000166 0.42370117
null.deviance df.null logLik AIC BIC deviance df.residual nobs
68.21358 150 -31.9729 71.9458 84.01492 63.9458 147 151
path
/tempdir/model_d1.rds
Weight Models for Treatment Switching
-------------------------------------
[[n1]]
Model: P(treatment = 1 | previous treatment = 1) for numerator
term estimate std.error statistic p.value
(Intercept) 1.23927187 0.7983788 1.5522354 0.1206059128
age -0.01404746 0.0173406 -0.8100905 0.4178881792
x4 0.79347625 0.2162862 3.6686408 0.0002438434
null.deviance df.null logLik AIC BIC deviance df.residual nobs
188.829 150 -85.39205 176.7841 185.8359 170.7841 148 151
path
/tempdir/model_n1.rds
[[d1]]
Model: P(treatment = 1 | previous treatment = 1) for denominator
term estimate std.error statistic p.value
(Intercept) 1.36823919 0.81213243 1.6847489 0.0920370351
age -0.01484948 0.01760642 -0.8434126 0.3989977540
x2 0.33852867 0.19500928 1.7359619 0.0825705753
x4 0.78724133 0.21455503 3.6691814 0.0002433283
null.deviance df.null logLik AIC BIC deviance df.residual nobs
188.829 150 -83.82451 175.649 187.7181 167.649 147 151
path
/tempdir/model_d1.rds
[[n0]]
Model: P(treatment = 1 | previous treatment = 0) for numerator
term estimate std.error statistic p.value
(Intercept) 0.084860976 0.67437905 0.12583572 8.998620e-01
age 0.001012695 0.01625994 0.06228156 9.503386e-01
x4 1.108633792 0.21415037 5.17689418 2.256101e-07
null.deviance df.null logLik AIC BIC deviance df.residual nobs
232.2705 169 -94.30944 194.6189 204.0263 188.6189 167 170
path
/tempdir/model_n0.rds
[[d0]]
Model: P(treatment = 1 | previous treatment = 0) for denominator
term estimate std.error statistic p.value
(Intercept) 0.1214193693 0.67770501 0.17916257 8.578101e-01
age 0.0002235908 0.01632757 0.01369406 9.890741e-01
x2 0.1068088770 0.19102028 0.55914942 5.760597e-01
x4 1.1040693183 0.21338578 5.17405287 2.290700e-07
null.deviance df.null logLik AIC BIC deviance df.residual nobs
232.2705 169 -94.15216 196.3043 208.8475 188.3043 166 170
path
/tempdir/model_d0.rds
Code
show(object_w_weights@switch_weights@fitted$n1)
Output
Model: P(treatment = 1 | previous treatment = 1) for numerator
term estimate std.error statistic p.value
(Intercept) 1.23927187 0.7983788 1.5522354 0.1206059128
age -0.01404746 0.0173406 -0.8100905 0.4178881792
x4 0.79347625 0.2162862 3.6686408 0.0002438434
null.deviance df.null logLik AIC BIC deviance df.residual nobs
188.829 150 -85.39205 176.7841 185.8359 170.7841 148 151
path
/tempdir/model_n1.rds
weight_model_data_indices works
Code
data.frame(values = rle_result$values, lengths = rle_result$lengths)
Output
values lengths
1 TRUE 1
2 FALSE 5
3 TRUE 2
4 FALSE 2
5 TRUE 1
6 FALSE 4
7 TRUE 5
8 FALSE 4
9 TRUE 1
10 FALSE 7
11 TRUE 1
12 FALSE 8
13 TRUE 1
14 FALSE 1
15 TRUE 1
16 FALSE 5
17 TRUE 9
18 FALSE 1
19 TRUE 3
20 FALSE 1
21 TRUE 10
22 FALSE 1
23 TRUE 4
24 FALSE 2
25 TRUE 1
26 FALSE 1
27 TRUE 1
28 FALSE 4
29 TRUE 6
30 FALSE 1
31 TRUE 1
32 FALSE 2
33 TRUE 1
34 FALSE 2
35 TRUE 5
36 FALSE 2
37 TRUE 2
38 FALSE 2
39 TRUE 6
40 FALSE 1
41 TRUE 1
42 FALSE 5
43 TRUE 1
44 FALSE 1
45 TRUE 6
46 FALSE 1
47 TRUE 1
48 FALSE 17
49 TRUE 2
50 FALSE 3
51 TRUE 4
52 FALSE 1
53 TRUE 2
54 FALSE 2
55 TRUE 1
56 FALSE 1
57 TRUE 2
58 FALSE 1
59 TRUE 18
60 FALSE 1
61 TRUE 1
62 FALSE 2
63 TRUE 6
64 FALSE 7
65 TRUE 3
66 FALSE 3
67 TRUE 3
68 FALSE 3
69 TRUE 1
70 FALSE 1
71 TRUE 2
72 FALSE 2
73 TRUE 3
74 FALSE 3
75 TRUE 1
76 FALSE 3
77 TRUE 1
78 FALSE 1
79 TRUE 3
80 FALSE 2
81 TRUE 2
82 FALSE 2
83 TRUE 1
84 FALSE 2
85 TRUE 11
86 FALSE 2
87 TRUE 2
88 FALSE 5
89 TRUE 3
90 FALSE 2
91 TRUE 6
92 FALSE 1
93 TRUE 1
94 FALSE 3
95 TRUE 1
96 FALSE 2
97 TRUE 5
98 FALSE 7
99 TRUE 4
100 FALSE 1
101 TRUE 5
102 FALSE 1
103 TRUE 2
104 FALSE 2
105 TRUE 2
106 FALSE 3
107 TRUE 1
108 FALSE 2
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