Renders the same full console display as print.lago(): the
inputs recap, outcome-model coefficient table, overall intervention-effect
test, recommended intervention (with cost and the estimated-outcome CI), and
the confidence set. Provided so summary() works as expected on a "lago"
object.
Usage
# S3 method for class 'lago'
summary(object, ...)Arguments
- object
A "lago" object returned by
lago_optimization().- ...
Ignored.
Examples
# summary() currently renders exactly the same output as print(). The lower
# bounds start at 1 while the data also contains 0s, so the optimizer warns
# about that; the warning is expected here.
result <- lago_optimization(
data = BB_data,
outcome_name = "pp3_oxytocin_mother",
outcome_type = "binary",
glm_family = "binomial",
intervention_components = c("coaching_updt", "launch_duration"),
center_characteristics = c("birth_volume_100"),
center_characteristics_optimization_values = 1.75,
intervention_lower_bounds = c(1, 1),
intervention_upper_bounds = c(40, 5),
cost_list_of_vectors = list(c(0, 1700), c(0, 8000)),
outcome_goal = 0.85,
outcome_goal_intention = "maximize",
include_confidence_set = TRUE,
confidence_set_grid_step_size = c(1, 1),
quiet = TRUE
)
#> Warning: The lower bound for the intervention component coaching_updt is greater than the minimum value in the data.
#> Warning: The lower bound for the intervention component launch_duration is greater than the minimum value in the data.
summary(result)
#>
#> ── LAGO optimization result ────────────────────────────────────────────────────
#>
#> ── Inputs
#> Input data dimensions: 6124 rows, 21 columns
#> Outcome name: pp3_oxytocin_mother
#> Outcome type: binary
#> 2 intervention component(s): coaching_updt, launch_duration
#> 1 center characteristic(s): birth_volume_100
#> Outcome model family: binomial
#> Outcome model link: logit
#> Fixed center effects: FALSE
#> Fixed time effects: FALSE
#> Outcome goal: 0.85
#> Power goal: not specified
#> Intervention component costs: c(0, 1700), c(0, 8000)
#> Intervention lower bounds: 1, 1
#> Intervention upper bounds: 40, 5
#>
#> ── Outcome model fit
#> Estimate Std. Error
#> (Intercept) -2.29989 0.068371
#> coaching_updt 0.02514 0.006112
#> launch_duration 1.02447 0.074135
#> birth_volume_100 0.66451 0.029627
#> Standard errors are those of the fitted glm().
#>
#> ── Overall intervention-effect test
#> To see the overall test results, include a 'group' column in the data with
#> values 'treatment' or 'control' (binary outcomes only).
#>
#> ── Recommended intervention
#> coaching_updt: 1
#> launch_duration: 2.7785
#> Cost: 23928
#> Estimated outcome: 0.85
#> 95% CI for the estimated outcome: 0.802 - 0.898
#> Outcome goal: 0.85
#>
#> ── Confidence set
#> 95% confidence set size: 9% of the grid
#> IQR of the cost within the 95% confidence set: 62325 - 76775
#> First rows of the confidence set (use $cs for all):
#> coaching_updt launch_duration birth_volume_100 CI_lower_bound CI_upper_bound
#> 68 27 2 1.75 0.811 0.851
#> 69 28 2 1.75 0.814 0.855
#> 70 29 2 1.75 0.816 0.859
#> 71 30 2 1.75 0.819 0.863
#> 72 31 2 1.75 0.822 0.867
#> 73 32 2 1.75 0.824 0.871
#> cost
#> 68 61900
#> 69 63600
#> 70 65300
#> 71 67000
#> 72 68700
#> 73 70400