tests/testthat/_snaps/generics.md
In CAM-Roche/RandomisedTrialsEmulation: Causal Analysis of Observational Time-to-Event Data
summary for data_preparation separate=TRUE
Code
summary(object, digits = 3)
Output
Expanded Trial Emulation data
Expanded data saved in 10 csv files:
1: random_temp_dir_path/trial_0.csv
2: random_temp_dir_path/trial_1.csv
3: random_temp_dir_path/trial_2.csv
---
8: random_temp_dir_path/trial_7.csv
9: random_temp_dir_path/trial_8.csv
10: random_temp_dir_path/trial_9.csv
Number of observations in expanded data: 2346
First trial period: 0
Last trial period: 9
------------------------------------------------------------
Weight models
-------------
Treatment switch models
-----------------------
switch_models$switch_d0:
P(treatment = 1 | previous treatment = 0) for denominator
term estimate std.error statistic p.value
(Intercept) -0.281 0.067 -4.2 2.69e-05
------------------------------------------------------------
switch_models$switch_n0:
P(treatment = 1 | previous treatment = 0) for numerator
term estimate std.error statistic p.value
(Intercept) -0.0303 0.0763 -0.398 6.91e-01
age_s -0.5164 0.0728 -7.093 1.32e-12
------------------------------------------------------------
switch_models$switch_d1:
P(treatment = 1 | previous treatment = 1) for denominator
term estimate std.error statistic p.value
(Intercept) 0.98 0.0784 12.5 6.51e-36
------------------------------------------------------------
switch_models$switch_n1:
P(treatment = 1 | previous treatment = 1) for numerator
term estimate std.error statistic p.value
(Intercept) 1.203 0.0977 12.31 7.55e-35
age_s -0.379 0.0874 -4.34 1.44e-05
------------------------------------------------------------
Censoring models
----------------
censor_models$cens_d0:
Model for P(cense = 0 | X, previous treatment = 0) for denominator
term estimate std.error statistic p.value
(Intercept) 1.69 0.0914 18.5 4.11e-76
------------------------------------------------------------
censor_models$cens_n0:
Model for P(cense = 0 | X, previous treatment = 0) for numerator
term estimate std.error statistic p.value
(Intercept) 1.493 0.122 12.22 2.48e-34
X1 0.409 0.185 2.21 2.68e-02
------------------------------------------------------------
censor_models$cens_d1:
Model for P(cense = 0 | X, previous treatment = 1) for denominator
term estimate std.error statistic p.value
(Intercept) 2.4 0.127 19 2.08e-80
------------------------------------------------------------
censor_models$cens_n1:
Model for P(cense = 0 | X, previous treatment = 1) for numerator
term estimate std.error statistic p.value
(Intercept) 2.22 0.137 16.19 5.76e-59
X1 0.93 0.367 2.54 1.12e-02
------------------------------------------------------------
Code
print(object$censor_models[[1]], digits = 4)
Output
Model for P(cense = 0 | X, previous treatment = 0) for denominator
term estimate std.error statistic p.value
(Intercept) 1.687 0.09135 18.46 4.113e-76
null.deviance df.null logLik AIC BIC deviance df.residual nobs
787.8 908 -393.9 789.8 794.6 787.8 908 909
Object saved at "random_temp_dir_path/cense_model_d0.rds"
Code
print(object$censor_models[[1]], full = FALSE, digits = 4)
Output
Model for P(cense = 0 | X, previous treatment = 0) for denominator
term estimate std.error statistic p.value
(Intercept) 1.687 0.09135 18.46 4.113e-76
summary for data_preparation separate=FALSE
Code
summary(object, digits = 3)
Output
Expanded Trial Emulation data
id trial_period followup_time outcome weight treatment X1
1: 1 0 0 0 1.000 1 1
2: 1 0 1 0 1.011 1 1
3: 1 0 2 0 0.994 1 1
---
9474: 1000 0 7 0 0.821 1 0
9475: 1000 0 8 0 0.761 1 0
9476: 1000 0 9 1 0.698 1 0
assigned_treatment dose
1: 1 1
2: 1 2
3: 1 3
---
9474: 1 8
9475: 1 9
9476: 1 10
Number of observations in expanded data: 9476
First trial period: 0
Last trial period: 9
------------------------------------------------------------
Weight models
-------------
Treatment switch models
-----------------------
switch_models$switch_d0:
P(treatment = 1 | previous treatment = 0) for denominator
term estimate std.error statistic p.value
(Intercept) -0.303 0.0378 -8.01 1.17e-15
------------------------------------------------------------
switch_models$switch_n0:
P(treatment = 1 | previous treatment = 0) for numerator
term estimate std.error statistic p.value
(Intercept) -0.11 0.0454 -2.42 1.54e-02
age_s -0.32 0.0422 -7.60 3.04e-14
------------------------------------------------------------
switch_models$switch_d1:
P(treatment = 1 | previous treatment = 1) for denominator
term estimate std.error statistic p.value
(Intercept) 0.757 0.0454 16.7 2.27e-62
------------------------------------------------------------
switch_models$switch_n1:
P(treatment = 1 | previous treatment = 1) for numerator
term estimate std.error statistic p.value
(Intercept) 1.004 0.0624 16.09 3.12e-58
age_s -0.341 0.0554 -6.15 7.72e-10
------------------------------------------------------------
Censoring models
----------------
censor_models$cens_d0:
Model for P(cense = 0 | X, previous treatment = 0) for denominator
term estimate std.error statistic p.value
(Intercept) 1.71 0.0518 32.9 1.41e-237
------------------------------------------------------------
censor_models$cens_n0:
Model for P(cense = 0 | X, previous treatment = 0) for numerator
term estimate std.error statistic p.value
(Intercept) 1.558 0.0696 22.39 5.51e-111
X1 0.314 0.1045 3.01 2.65e-03
------------------------------------------------------------
censor_models$cens_d1:
Model for P(cense = 0 | X, previous treatment = 1) for denominator
term estimate std.error statistic p.value
(Intercept) 2.74 0.0888 30.9 2.19e-209
------------------------------------------------------------
censor_models$cens_n1:
Model for P(cense = 0 | X, previous treatment = 1) for numerator
term estimate std.error statistic p.value
(Intercept) 2.67 0.100 26.67 9.51e-157
X1 0.32 0.218 1.47 1.42e-01
------------------------------------------------------------
Code
print(object$switch_models[[1]], digits = 4)
Output
P(treatment = 1 | previous treatment = 0) for denominator
term estimate std.error statistic p.value
(Intercept) -0.3028 0.03781 -8.007 1.174e-15
null.deviance df.null logLik AIC BIC deviance df.residual nobs
3903 2861 -1951 3905 3911 3903 2861 2862
Code
print(object$switch_models[[1]], full = FALSE, digits = 4)
Output
P(treatment = 1 | previous treatment = 0) for denominator
term estimate std.error statistic p.value
(Intercept) -0.3028 0.03781 -8.007 1.174e-15
summary for initiators
Code
summary(object, digits = 3)
Output
Trial Emulation Outcome Model
Outcome model formula:
outcome ~ assigned_treatment + dose + trial_period + I(trial_period^2) +
followup_time + I(followup_time^2) + X1
Coefficent summary (robust):
names estimate robust_se 2.5% 97.5% z p_value
(Intercept) -4.1266 0.2437 -4.6043 -3.6489 -16.931 <2e-16
assigned_treatment 0.3091 0.2788 -0.2373 0.8555 1.109 0.267
dose -0.1922 0.1174 -0.4224 0.0380 -1.636 0.102
trial_period -0.0668 0.0926 -0.2483 0.1147 -0.721 0.471
I(trial_period^2) -0.0116 0.0199 -0.0506 0.0273 -0.585 0.558
followup_time 0.2249 0.1401 -0.0496 0.4995 1.606 0.108
I(followup_time^2) -0.0181 0.0169 -0.0513 0.0151 -1.070 0.284
X1 -0.1110 0.2291 -0.5600 0.3381 -0.484 0.628
object$model contains the fitted glm model object.
object$robust$matrix contains the full robust covariance matrix.
Code
summary(object, digits = 7)
Output
Trial Emulation Outcome Model
Outcome model formula:
outcome ~ assigned_treatment + dose + trial_period + I(trial_period^2) +
followup_time + I(followup_time^2) + X1
Coefficent summary (robust):
names estimate robust_se 2.5% 97.5% z
(Intercept) -4.12662820 0.24372909 -4.60433721 -3.64891919 -16.9312093
assigned_treatment 0.30913237 0.27877733 -0.23727119 0.85553592 1.1088863
dose -0.19218381 0.11744822 -0.42238232 0.03801470 -1.6363280
trial_period -0.06679054 0.09260338 -0.24829317 0.11471209 -0.7212538
I(trial_period^2) -0.01163048 0.01987472 -0.05058494 0.02732398 -0.5851896
followup_time 0.22492444 0.14006997 -0.04961271 0.49946158 1.6058005
I(followup_time^2) -0.01812884 0.01693641 -0.05132420 0.01506652 -1.0704062
X1 -0.11095373 0.22910223 -0.55999411 0.33808665 -0.4842979
p_value
< 2.2e-16
0.2674792
0.1017710
0.4707534
0.5584203
0.1083177
0.2844365
0.6281745
object$model contains the fitted glm model object.
object$robust$matrix contains the full robust covariance matrix.
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.
summary for data_preparation separate=TRUE
Code
summary(object, digits = 3)
Output
Expanded Trial Emulation data
Expanded data saved in 10 csv files:
1: random_temp_dir_path/trial_0.csv
2: random_temp_dir_path/trial_1.csv
3: random_temp_dir_path/trial_2.csv
---
8: random_temp_dir_path/trial_7.csv
9: random_temp_dir_path/trial_8.csv
10: random_temp_dir_path/trial_9.csv
Number of observations in expanded data: 2346
First trial period: 0
Last trial period: 9
------------------------------------------------------------
Weight models
-------------
Treatment switch models
-----------------------
switch_models$switch_d0:
P(treatment = 1 | previous treatment = 0) for denominator
term estimate std.error statistic p.value
(Intercept) -0.281 0.067 -4.2 2.69e-05
------------------------------------------------------------
switch_models$switch_n0:
P(treatment = 1 | previous treatment = 0) for numerator
term estimate std.error statistic p.value
(Intercept) -0.0303 0.0763 -0.398 6.91e-01
age_s -0.5164 0.0728 -7.093 1.32e-12
------------------------------------------------------------
switch_models$switch_d1:
P(treatment = 1 | previous treatment = 1) for denominator
term estimate std.error statistic p.value
(Intercept) 0.98 0.0784 12.5 6.51e-36
------------------------------------------------------------
switch_models$switch_n1:
P(treatment = 1 | previous treatment = 1) for numerator
term estimate std.error statistic p.value
(Intercept) 1.203 0.0977 12.31 7.55e-35
age_s -0.379 0.0874 -4.34 1.44e-05
------------------------------------------------------------
Censoring models
----------------
censor_models$cens_d0:
Model for P(cense = 0 | X, previous treatment = 0) for denominator
term estimate std.error statistic p.value
(Intercept) 1.69 0.0914 18.5 4.11e-76
------------------------------------------------------------
censor_models$cens_n0:
Model for P(cense = 0 | X, previous treatment = 0) for numerator
term estimate std.error statistic p.value
(Intercept) 1.493 0.122 12.22 2.48e-34
X1 0.409 0.185 2.21 2.68e-02
------------------------------------------------------------
censor_models$cens_d1:
Model for P(cense = 0 | X, previous treatment = 1) for denominator
term estimate std.error statistic p.value
(Intercept) 2.4 0.127 19 2.08e-80
------------------------------------------------------------
censor_models$cens_n1:
Model for P(cense = 0 | X, previous treatment = 1) for numerator
term estimate std.error statistic p.value
(Intercept) 2.22 0.137 16.19 5.76e-59
X1 0.93 0.367 2.54 1.12e-02
------------------------------------------------------------
Code
print(object$censor_models[[1]], digits = 4)
Output
Model for P(cense = 0 | X, previous treatment = 0) for denominator
term estimate std.error statistic p.value
(Intercept) 1.687 0.09135 18.46 4.113e-76
null.deviance df.null logLik AIC BIC deviance df.residual nobs
787.8 908 -393.9 789.8 794.6 787.8 908 909
Object saved at "random_temp_dir_path/cense_model_d0.rds"
Code
print(object$censor_models[[1]], full = FALSE, digits = 4)
Output
Model for P(cense = 0 | X, previous treatment = 0) for denominator
term estimate std.error statistic p.value
(Intercept) 1.687 0.09135 18.46 4.113e-76
summary for data_preparation separate=FALSE
Code
summary(object, digits = 3)
Output
Expanded Trial Emulation data
id trial_period followup_time outcome weight treatment X1
1: 1 0 0 0 1.000 1 1
2: 1 0 1 0 1.011 1 1
3: 1 0 2 0 0.994 1 1
---
9474: 1000 0 7 0 0.821 1 0
9475: 1000 0 8 0 0.761 1 0
9476: 1000 0 9 1 0.698 1 0
assigned_treatment dose
1: 1 1
2: 1 2
3: 1 3
---
9474: 1 8
9475: 1 9
9476: 1 10
Number of observations in expanded data: 9476
First trial period: 0
Last trial period: 9
------------------------------------------------------------
Weight models
-------------
Treatment switch models
-----------------------
switch_models$switch_d0:
P(treatment = 1 | previous treatment = 0) for denominator
term estimate std.error statistic p.value
(Intercept) -0.303 0.0378 -8.01 1.17e-15
------------------------------------------------------------
switch_models$switch_n0:
P(treatment = 1 | previous treatment = 0) for numerator
term estimate std.error statistic p.value
(Intercept) -0.11 0.0454 -2.42 1.54e-02
age_s -0.32 0.0422 -7.60 3.04e-14
------------------------------------------------------------
switch_models$switch_d1:
P(treatment = 1 | previous treatment = 1) for denominator
term estimate std.error statistic p.value
(Intercept) 0.757 0.0454 16.7 2.27e-62
------------------------------------------------------------
switch_models$switch_n1:
P(treatment = 1 | previous treatment = 1) for numerator
term estimate std.error statistic p.value
(Intercept) 1.004 0.0624 16.09 3.12e-58
age_s -0.341 0.0554 -6.15 7.72e-10
------------------------------------------------------------
Censoring models
----------------
censor_models$cens_d0:
Model for P(cense = 0 | X, previous treatment = 0) for denominator
term estimate std.error statistic p.value
(Intercept) 1.71 0.0518 32.9 1.41e-237
------------------------------------------------------------
censor_models$cens_n0:
Model for P(cense = 0 | X, previous treatment = 0) for numerator
term estimate std.error statistic p.value
(Intercept) 1.558 0.0696 22.39 5.51e-111
X1 0.314 0.1045 3.01 2.65e-03
------------------------------------------------------------
censor_models$cens_d1:
Model for P(cense = 0 | X, previous treatment = 1) for denominator
term estimate std.error statistic p.value
(Intercept) 2.74 0.0888 30.9 2.19e-209
------------------------------------------------------------
censor_models$cens_n1:
Model for P(cense = 0 | X, previous treatment = 1) for numerator
term estimate std.error statistic p.value
(Intercept) 2.67 0.100 26.67 9.51e-157
X1 0.32 0.218 1.47 1.42e-01
------------------------------------------------------------
Code
print(object$switch_models[[1]], digits = 4)
Output
P(treatment = 1 | previous treatment = 0) for denominator
term estimate std.error statistic p.value
(Intercept) -0.3028 0.03781 -8.007 1.174e-15
null.deviance df.null logLik AIC BIC deviance df.residual nobs
3903 2861 -1951 3905 3911 3903 2861 2862
Code
print(object$switch_models[[1]], full = FALSE, digits = 4)
Output
P(treatment = 1 | previous treatment = 0) for denominator
term estimate std.error statistic p.value
(Intercept) -0.3028 0.03781 -8.007 1.174e-15
summary for initiators
Code
summary(object, digits = 3)
Output
Trial Emulation Outcome Model
Outcome model formula:
outcome ~ assigned_treatment + dose + trial_period + I(trial_period^2) +
followup_time + I(followup_time^2) + X1
Coefficent summary (robust):
names estimate robust_se 2.5% 97.5% z p_value
(Intercept) -4.1266 0.2437 -4.6043 -3.6489 -16.931 <2e-16
assigned_treatment 0.3091 0.2788 -0.2373 0.8555 1.109 0.267
dose -0.1922 0.1174 -0.4224 0.0380 -1.636 0.102
trial_period -0.0668 0.0926 -0.2483 0.1147 -0.721 0.471
I(trial_period^2) -0.0116 0.0199 -0.0506 0.0273 -0.585 0.558
followup_time 0.2249 0.1401 -0.0496 0.4995 1.606 0.108
I(followup_time^2) -0.0181 0.0169 -0.0513 0.0151 -1.070 0.284
X1 -0.1110 0.2291 -0.5600 0.3381 -0.484 0.628
object$model contains the fitted glm model object.
object$robust$matrix contains the full robust covariance matrix.
Code
summary(object, digits = 7)
Output
Trial Emulation Outcome Model
Outcome model formula:
outcome ~ assigned_treatment + dose + trial_period + I(trial_period^2) +
followup_time + I(followup_time^2) + X1
Coefficent summary (robust):
names estimate robust_se 2.5% 97.5% z
(Intercept) -4.12662820 0.24372909 -4.60433721 -3.64891919 -16.9312093
assigned_treatment 0.30913237 0.27877733 -0.23727119 0.85553592 1.1088863
dose -0.19218381 0.11744822 -0.42238232 0.03801470 -1.6363280
trial_period -0.06679054 0.09260338 -0.24829317 0.11471209 -0.7212538
I(trial_period^2) -0.01163048 0.01987472 -0.05058494 0.02732398 -0.5851896
followup_time 0.22492444 0.14006997 -0.04961271 0.49946158 1.6058005
I(followup_time^2) -0.01812884 0.01693641 -0.05132420 0.01506652 -1.0704062
X1 -0.11095373 0.22910223 -0.55999411 0.33808665 -0.4842979
p_value
< 2.2e-16
0.2674792
0.1017710
0.4707534
0.5584203
0.1083177
0.2844365
0.6281745
object$model contains the fitted glm model object.
object$robust$matrix contains the full robust covariance matrix.
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.
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