tests/testthat/_snaps/modelling.md
In CAM-Roche/RandomisedTrialsEmulation: Causal Analysis of Observational Time-to-Event Data
Modelling works with where_case
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trial_msm works with analysis_weights = unweighted
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trial_msm works with analysis_weights = p99
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trial_msm works with analysis_weights = weight_limits
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trial_msm works with sample weights
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fit_msm works
Code
fm_01
Output
Trial Sequence Object
Estimand: Intention-to-treat
Data:
- N: 725 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
---
724: 99 6 1 1 -0.033762356 1 0.5752681 71 3.00000000
725: 99 7 0 0 -1.340496520 1 0.5752681 72 3.08333333
outcome censored eligible time_on_regime wt wtC
<num> <int> <num> <num> <num> <num>
1: 0 0 1 0 1.0111849 1.0111849
2: 0 0 0 1 0.9562510 0.9562510
---
724: 0 0 0 1 0.9269537 0.9269537
725: 1 0 0 2 0.9448756 0.9448756
IPW for informative censoring:
- Numerator formula: 1 - censored ~ x3
- Denominator formula: 1 - censored ~ x1 + x2 + x3
- 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()
Sequence of Trials Data:
- Chunk size: 500
- Censor at switch: FALSE
- First period: 0 | Last period: Inf
A TE Datastore CSV object
N: 1558 observations
Periods: 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Path:
Columns: id, trial_period, followup_time, outcome, weight, treatment, x1, x2, x3, assigned_treatment
Outcome model:
- Formula: outcome ~ assigned_treatment + x1 + x2 + followup_time + x3
- Treatment variable: assigned_treatment
- Adjustment variables: x1 x2 x3
- Model fitter type: te_stats_glm_logit
Model Summary:
term estimate std.error statistic p.value conf.low conf.high
(Intercept) -6.2053 0.846 -7.337 2.2e-13 -7.863 -4.548
assigned_treatment 2.0802 0.603 3.451 5.6e-04 0.899 3.262
x1 -0.5030 0.543 -0.926 3.5e-01 -1.567 0.561
x2 0.1017 0.308 0.330 7.4e-01 -0.503 0.706
followup_time -0.0029 0.046 -0.062 9.5e-01 -0.094 0.088
x3 0.7231 0.713 1.014 3.1e-01 -0.675 2.121
null.deviance df.null logLik AIC BIC deviance df.residual nobs
148 1557 -74.8 162 194 136 1552 1558
Outcome data
N: 1558 observations from 89 patients in 18 trial periods
Periods: 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
id trial_period followup_time outcome weight treatment x1
<int> <int> <int> <int> <num> <int> <int>
1: 1 0 0 0 1.0000000 1 1
2: 1 0 1 0 0.9562510 1 1
---
1557: 54 17 1 0 0.9456885 0 0
1558: 54 17 2 0 0.9373179 0 0
x2 x3 assigned_treatment sample_weight w
<num> <int> <int> <num> <num>
1: 1.146148 0 1 1 1.0000000
2: 1.146148 0 1 1 0.9562510
---
1557: 1.846423 1 1 1 0.9456885
1558: 1.846423 1 1 1 0.9373179
CAM-Roche/RandomisedTrialsEmulation documentation built on April 14, 2025, 7:44 a.m.
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Modelling works with where_case
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trial_msm works with analysis_weights = unweighted
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trial_msm works with analysis_weights = p99
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trial_msm works with analysis_weights = weight_limits
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"type": "double",
"attributes": {},
"value": [-3.43908322, -1.28989098, -1.10533082, -2.00320581, 2.11964464, 0.41744887, 0.54632031, 1.28103704, -0.14862475]
},
{
"type": "double",
"attributes": {},
"value": [0.00058369, 0.19708851, 0.26901627, 0.0451552, 0.03403603, 0.67635012, 0.58484577, 0.20018066, 0.88184974]
}
]
}
trial_msm works with sample weights
{
"type": "list",
"attributes": {
"names": {
"type": "character",
"attributes": {},
"value": ["names", "estimate", "robust_se", "2.5%", "97.5%", "z", "p_value"]
},
"row.names": {
"type": "integer",
"attributes": {},
"value": [1, 2, 3, 4, 5, 6, 7, 8, 9]
},
"class": {
"type": "character",
"attributes": {},
"value": ["data.frame"]
}
},
"value": [
{
"type": "character",
"attributes": {},
"value": ["(Intercept)", "assigned_treatment", "trial_period", "followup_time", "X1", "X2", "X3", "X4", "age_s"]
},
{
"type": "double",
"attributes": {},
"value": [-3.86140786, -1.51756024, -0.37353376, -0.82824879, 1.41958257, 0.14505724, 0.72382671, 0.86103731, 0.05905786]
},
{
"type": "double",
"attributes": {},
"value": [1.08163206, 1.19667548, 0.43416259, 0.36449798, 0.72582293, 0.56898797, 1.0903126, 0.881077, 0.40424615]
},
{
"type": "double",
"attributes": {},
"value": [-5.9814067, -3.86304417, -1.22449244, -1.54266483, -0.00303037, -0.97015917, -1.41318598, -0.86587361, -0.7332646]
},
{
"type": "double",
"attributes": {},
"value": [-1.74140902, 0.82792369, 0.47742493, -0.11383275, 2.8421955, 1.26027366, 2.8608394, 2.58794822, 0.85138033]
},
{
"type": "double",
"attributes": {},
"value": [-3.5699828, -1.26814685, -0.86035454, -2.27230009, 1.95582493, 0.25493904, 0.66387081, 0.97725546, 0.14609382]
},
{
"type": "double",
"attributes": {},
"value": [0.000357, 0.20474551, 0.38959364, 0.02306839, 0.05048578, 0.79877019, 0.506773, 0.3284427, 0.88384733]
}
]
}
fit_msm works
Code
fm_01
Output
Trial Sequence Object
Estimand: Intention-to-treat
Data:
- N: 725 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
---
724: 99 6 1 1 -0.033762356 1 0.5752681 71 3.00000000
725: 99 7 0 0 -1.340496520 1 0.5752681 72 3.08333333
outcome censored eligible time_on_regime wt wtC
<num> <int> <num> <num> <num> <num>
1: 0 0 1 0 1.0111849 1.0111849
2: 0 0 0 1 0.9562510 0.9562510
---
724: 0 0 0 1 0.9269537 0.9269537
725: 1 0 0 2 0.9448756 0.9448756
IPW for informative censoring:
- Numerator formula: 1 - censored ~ x3
- Denominator formula: 1 - censored ~ x1 + x2 + x3
- 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()
Sequence of Trials Data:
- Chunk size: 500
- Censor at switch: FALSE
- First period: 0 | Last period: Inf
A TE Datastore CSV object
N: 1558 observations
Periods: 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Path:
Columns: id, trial_period, followup_time, outcome, weight, treatment, x1, x2, x3, assigned_treatment
Outcome model:
- Formula: outcome ~ assigned_treatment + x1 + x2 + followup_time + x3
- Treatment variable: assigned_treatment
- Adjustment variables: x1 x2 x3
- Model fitter type: te_stats_glm_logit
Model Summary:
term estimate std.error statistic p.value conf.low conf.high
(Intercept) -6.2053 0.846 -7.337 2.2e-13 -7.863 -4.548
assigned_treatment 2.0802 0.603 3.451 5.6e-04 0.899 3.262
x1 -0.5030 0.543 -0.926 3.5e-01 -1.567 0.561
x2 0.1017 0.308 0.330 7.4e-01 -0.503 0.706
followup_time -0.0029 0.046 -0.062 9.5e-01 -0.094 0.088
x3 0.7231 0.713 1.014 3.1e-01 -0.675 2.121
null.deviance df.null logLik AIC BIC deviance df.residual nobs
148 1557 -74.8 162 194 136 1552 1558
Outcome data
N: 1558 observations from 89 patients in 18 trial periods
Periods: 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
id trial_period followup_time outcome weight treatment x1
<int> <int> <int> <int> <num> <int> <int>
1: 1 0 0 0 1.0000000 1 1
2: 1 0 1 0 0.9562510 1 1
---
1557: 54 17 1 0 0.9456885 0 0
1558: 54 17 2 0 0.9373179 0 0
x2 x3 assigned_treatment sample_weight w
<num> <int> <int> <num> <num>
1: 1.146148 0 1 1 1.0000000
2: 1.146148 0 1 1 0.9562510
---
1557: 1.846423 1 1 1 0.9456885
1558: 1.846423 1 1 1 0.9373179
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