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tests/testthat/_snaps/generics.md
In TrialEmulation: 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
<int> <int> <int> <num> <num> <num> <num>
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
<num> <num>
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.
Try the TrialEmulation package in your browser
Any scripts or data that you put into this service are public.
TrialEmulation documentation built on Sept. 11, 2024, 9:06 p.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
<int> <int> <int> <num> <num> <num> <num>
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
<num> <num>
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.
Try the TrialEmulation package in your browser
Any scripts or data that you put into this service are public.
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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