Code
show(trial_sequence("PP"))
Output
Trial Sequence Object
Estimand: Per-protocol
Data:
- No data has been set. Use set_data()
IPW for informative censoring:
- No weight model specified
IPW for treatment switch censoring:
- No weight model specified
Outcome model:
- Outcome model not specified. Use set_outcome_model()
Code
show(trial_sequence("ITT"))
Output
Trial Sequence Object
Estimand: Intention-to-treat
Data:
- No data has been set. Use set_data()
IPW for informative censoring:
- No weight model specified
Outcome model:
- Outcome model not specified. Use set_outcome_model()
Code
show(trial_sequence("AT"))
Output
Trial Sequence Object
Estimand: As treated
Data:
- No data has been set. Use set_data()
IPW for informative censoring:
- No weight model specified
IPW for treatment switch censoring:
- No weight model specified
Outcome model:
- Outcome model not specified. Use set_outcome_model()
Code
show(result)
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
<num> <int> <num> <num>
1: 0 0 1 0
2: 0 0 0 1
---
320: 0 0 0 1
321: 0 0 0 2
IPW for informative censoring:
- No weight model specified
IPW for treatment switch censoring:
- No weight model specified
Sequence of Trials Data:
- Use set_expansion_options() and expand_trials() to construct the sequence of trials dataset.
Outcome model:
- Formula: outcome ~ assigned_treatment + followup_time + I(followup_time^2) + trial_period + I(trial_period^2)
- Treatment variable: assigned_treatment
- Adjustment variables:
- Model fitter type: te_stats_glm_logit
Use fit_msm() to fit the outcome model
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