Precomputed sample-size simulations

Results from four completed sample-size searches, one for each of the package's entry points. A search repeatedly simulates datasets, fits a prediction model to each and evaluates its performance across a range of candidate sample sizes, so a realistic run takes minutes to hours. These objects were computed once, with the calls shown under Source, so that the examples and the vignette can demonstrate the output without recomputing it.

Usage

binary_example

continuous_example

survival_example

custom_example

Format

A list of class "pmsims". The most useful components are:

min_n

The estimated minimum sample size.

perf_n

Expected performance at min_n.

target_performance

The performance target the search aimed for.

metric

The performance metric used.

model

The model fitted to each simulated dataset.

mlpwr_ds

The sampled designs and simulated performance values behind the fitted curve, used by plot().

summaries

Aggregated performance summaries across replications.

simulation_time

Elapsed time of the original run, in seconds.

Objects produced by the wrapper functions additionally record the data-generating configuration (for example outcome_prevalence, correlation and complexity); see simulate_binary() for the full set.

An object of class pmsims of length 36.

An object of class pmsims of length 35.

An object of class pmsims of length 37.

An object of class pmsims of length 24.

Source

Generated by data-raw/precomputed-examples.R.

binary_example:

set.seed(123)
simulate_binary(
  signal_parameters = 20,
  noise_parameters = 0,
  complexity = 1,
  data_control = list(correlation = 0.3),
  outcome_prevalence = 0.30,
  maximum_achievable_cstatistic = 0.80,
  model = "glm",
  metric = "calibration_slope",
  target_performance = 0.85,
  n_reps_total = 1000,
  mean_or_assurance = "assurance"
)

continuous_example:

set.seed(123)
simulate_continuous(
  signal_parameters = 15,
  noise_parameters = 0,
  complexity = 1,
  data_control = list(correlation = 0.3),
  maximum_achievable_rsquared = 0.50,
  model = "lm",
  metric = "calibration_slope",
  target_performance = 0.95,
  n_reps_total = 1000,
  mean_or_assurance = "assurance"
)

survival_example:

set.seed(123)
simulate_survival(
  signal_parameters = 15,
  noise_parameters = 0,
  complexity = 1,
  data_control = list(correlation = 0.3),
  maximum_achievable_cindex = 0.70,
  baseline_hazard = 0.01,
  censoring_rate = 0.30,
  model = "coxph",
  metric = "calibration_slope",
  target_performance = 0.90,
  n_reps_total = 1000,
  mean_or_assurance = "assurance"
)

custom_example: a linear model with three independent predictors and a population \(R^2\) of 0.5, targeting a calibration slope of 0.9 with 80% assurance. The search uses bounds of 25 to 1,000 participants, 1,000 replications and 30,000 test observations. See simulate_custom() for the generating functions and executable call (seed 123).

Details

Each object is a "pmsims" object, as returned by simulate_binary(), simulate_continuous(), simulate_survival() and simulate_custom(), and can be used with print() and plot() in the usual way.

See also

simulate_binary(), simulate_continuous(), simulate_survival(), simulate_custom()

Examples

binary_example
#>                     ┌────────────────────────────────────────┐
#>                     │ pmsims: Sample size simulation summary │
#>                     └────────────────────────────────────────┘
#> 
#> ──────────────────────────────────── Inputs ────────────────────────────────────
#> 
#> Data-generating scenario
#>   Outcome                   Binary
#>   Prevalence                0.30
#>   Predictors                20 signal
#>   Predictor distribution    Normal
#>   Predictor correlation     0.30
#>   Signal form               Linear
#> 
#> Model and performance
#>   Model                     Logistic regression
#>   Large-sample C-statistic  0.800
#>   Sample-size criterion     Calibration slope ≥ 0.850
#> 
#> Simulation
#>   Mode                      Assurance
#>   Replications              1,000
#> 
#> ──────────────────────────────────── Results ───────────────────────────────────
#> 
#>   Minimum sample size       985
#> 
#>   Performance at N = 985
#>     Calibration slope       0.849    (target ≥ 0.850)
#>     C-statistic             0.794
#> 
#>   Running time              3 minutes 58 seconds
#> 
#> ────────────────────────────────────────────────────────────────────────────────
#> Assurance mode selects N so that the target is achieved with high probability
#> across repeated datasets.
binary_example$min_n
#> [1] 985