Package {PITS}

Title:	Power of Interrupted Time Series (ITS) Studies
Version:	0.1.0
Description:	Provides tools for estimating the statistical power of Interrupted Time Series (ITS) designs, with a focus on healthcare applications. The package supports prospective power calculations before a study begins, and retrospective assessments of whether a completed study was adequately powered. It includes functions to estimate nuisance parameters (baseline, residual standard deviation, autocorrelation) from data observed before the intervention, and to estimate power via Monte Carlo simulation for single-site and multi-site designs. Utility functions for design optimisation sweeps and publication- ready plots are also provided.
License:	MIT + file LICENSE
Language:	en-GB
Encoding:	UTF-8
Depends:	R (≥ 4.0.0)
Imports:	nlme, stats, graphics, grDevices, utils
Suggests:	knitr, rmarkdown, testthat (≥ 3.0.0)
VignetteBuilder:	knitr
Config/testthat/edition:	3
LazyData:	true
URL:	https://github.com/drdaviddelorenzo/PITS
BugReports:	https://github.com/drdaviddelorenzo/PITS/issues
Config/roxygen2/version:	8.0.0
NeedsCompilation:	no
Packaged:	2026-06-23 16:20:24 UTC; daviddelorenzo
Author:	David de Lorenzo [aut, cre] (affiliation: UCL Great Ormond Street Institute of Child Health, London, UK; Neotree, London, UK (https://neotree.org/))
Maintainer:	David de Lorenzo <drdaviddelorenzo@gmail.com>
Repository:	CRAN
Date/Publication:	2026-06-30 10:10:02 UTC

PITS: Power of Interrupted Time Series studies

Description

The package provides a complete workflow for ITS power analysis:

Step 1 - Estimate nuisance parameters from pre-intervention data

Use estimate_its_params() to estimate baseline, sigma (residual SD), and rho (AR(1) autocorrelation) from a pre-intervention time series. Individual functions estimate_baseline(), estimate_sigma(), estimate_rho(), and estimate_trend() are also available.

Step 2 - Calculate power via Monte Carlo simulation

Use calculate_power() (single site) or calculate_power_multi() (multiple sites) to estimate the probability of detecting a specified intervention effect for a given study design.

Step 3 - Optimise the design

Use power_sweep() to evaluate power across a range of post-intervention durations, and run_power_grid() for full factorial sensitivity analyses. Visualise results with plot_power_curve() and plot_power_heatmap().

Convenience wrappers

run_its_power() replicates the interactive experience of the original its_power_tool.R script. estimate_and_calculate() chains parameter estimation and power calculation in a single call.

Details

Tools for estimating the statistical power of Interrupted Time Series (ITS) designs, with a focus on healthcare applications.

Key references

• Lopez Bernal J, et al. (2017). Interrupted time series regression for the evaluation of public health interventions: a tutorial. Int J Epidemiol 46:348-355. doi:10.1093/ije/dyw098

• Zhang F, et al. (2011). Simulation-based power calculation for designing interrupted time series analyses of health policy interventions. J Clin Epidemiol 64:1252-1261.

• Wagner AK, et al. (2002). Segmented regression analysis of interrupted time series studies in medication use research. J Clin Pharm Ther 27:299-309.

Author(s)

Maintainer: David de Lorenzo drdaviddelorenzo@gmail.com (ORCID) (affiliation: UCL Great Ormond Street Institute of Child Health, London, UK; Neotree, London, UK (https://neotree.org/))

Authors:

• David de Lorenzo drdaviddelorenzo@gmail.com (ORCID) (affiliation: UCL Great Ormond Street Institute of Child Health, London, UK; Neotree, London, UK (https://neotree.org/))

See Also

Useful links:

• https://github.com/drdaviddelorenzo/PITS

• Report bugs at https://github.com/drdaviddelorenzo/PITS/issues

Build a factorial parameter grid for power calculations

Description

Creates a data frame of all combinations of the supplied parameter vectors, suitable for passing to run_power_grid(). Useful for sensitivity analyses and generating figures showing how power varies across parameter space.

Usage

build_param_grid(
  n_post,
  level_change,
  sigma,
  rho,
  n_pre = 24L,
  baseline = 15,
  slope_change = 0
)

Arguments

Value

A data frame with one row per parameter combination.

See Also

run_power_grid()

Examples

grid <- build_param_grid(
  n_post       = c(12, 18, 24, 30),
  level_change = c(-1, -2, -3),
  sigma        = c(1.5, 2.5, 3.5),
  rho          = c(0.2, 0.4, 0.6)
)
nrow(grid)   # 4 * 3 * 3 * 3 = 108 combinations

Estimate statistical power for a single-site ITS design

Description

Runs a Monte Carlo simulation to estimate the probability of detecting a specified intervention effect in a single-site ITS study.

Usage

calculate_power(
  n_pre,
  n_post,
  baseline,
  level_change,
  slope_change = 0,
  sigma,
  rho,
  test = c("level", "slope", "both"),
  alpha = 0.05,
  n_sim = 1000L,
  seed = 123L,
  pre_trend = 0
)

Arguments

Value

A named list:

power

Numeric. Estimated power (proportion of simulations with p < \alpha).

power_pct

Numeric. Power as a percentage.

interpretation

Character. Qualitative label (see interpret_power()).

p_values

Numeric vector. Raw p-values from all n_sim replications (NA = non-convergence).

n_converged

Integer. Number of replications that converged.

n_sim

Integer. Total replications requested.

params

Named list. Input parameters, for traceability.

See Also

power_sweep(), calculate_power_multi(), estimate_its_params()

Examples

result <- calculate_power(
  n_pre = 24, n_post = 24,
  baseline = 15, level_change = -3,
  sigma = 2.5, rho = 0.4,
  n_sim = 500, seed = 42
)
print(result$power_pct)

Estimate statistical power for a multi-site ITS design

Description

Runs a Monte Carlo simulation to estimate the probability of detecting a common intervention effect across multiple sites (e.g. hospitals), using a mixed-effects segmented regression model.

Usage

calculate_power_multi(
  sites,
  test = c("level", "slope", "both"),
  alpha = 0.05,
  n_sim = 1000L,
  seed = 123L
)

Arguments

Details

The model is a linear mixed-effects model with site-specific random intercepts and AR(1) autocorrelation within each site:

y_{it} = \beta_0 + \beta_1 t + \delta D_t + \gamma T_t^* + u_i + \varepsilon_{it}

where u_i \sim N(0, \tau^2) are site random intercepts and \varepsilon_{it} follows AR(1) within each site.

Value

Same structure as calculate_power(), plus:

n_sites

Integer. Number of sites.

site_names

Character vector. Site names.

See Also

calculate_power(), power_sweep()

Examples

sites <- list(
  list(name = "Hospital A", n_pre = 24, n_post = 24,
       baseline = 15, level_change = -3, slope_change = 0,
       sigma = 2.5, rho = 0.4),
  list(name = "Hospital B", n_pre = 24, n_post = 24,
       baseline = 18, level_change = -3, slope_change = 0,
       sigma = 3.0, rho = 0.4)
)
result <- calculate_power_multi(sites, n_sim = 200, seed = 42)
result$power_pct

Diagnostic plots for pre-intervention data

Description

Produces a 2x2 panel of diagnostic plots for pre-intervention data: the observed series with fitted trend, residuals over time, a Q-Q normality plot, and the residual ACF. These help assess whether the ITS model assumptions are plausible.

Usage

diagnose_params(
  data,
  outcome_col = "outcome",
  time_col = "time",
  main = "Pre-intervention diagnostics"
)

Arguments

Value

Invisibly, a list with elements params (estimated parameters) and residuals (residual vector).

See Also

estimate_its_params()

Examples

data("example_cfr_data")
diagnose_params(example_cfr_data)

Estimate parameters and calculate power in one step

Description

Convenience function that chains estimate_its_params() and calculate_power(). Supply a pre-intervention dataset, a level_change, and a target n_post, and receive a power estimate.

Usage

estimate_and_calculate(
  data,
  level_change,
  n_post,
  outcome_col = "outcome",
  time_col = "time",
  verbose = TRUE,
  ...
)

Arguments

Value

Output from calculate_power().

See Also

estimate_its_params(), calculate_power(), run_its_power()

Examples

data("example_cfr_data")
# Small n_sim for a fast example; use 1000+ for a reportable estimate.
result <- estimate_and_calculate(
  data         = example_cfr_data,
  level_change = -1.0,
  n_post       = 24,
  n_sim        = 100,
  verbose      = FALSE
)
result$power_pct

Estimate baseline outcome from pre-intervention data

Description

Fits a linear trend to the pre-intervention series and returns the intercept at t = 1, which represents the baseline (mean) outcome at the start of the observation period.

Usage

estimate_baseline(outcome, time = seq_along(outcome))

Arguments

Details

The baseline is the OLS intercept from the linear model outcome_t = \beta_0 + \beta_1 \cdot t + \varepsilon_t. When the pre-intervention trend is close to zero (as expected for a stable outcome), this is approximately equal to the mean of outcome.

Value

A single numeric value: the estimated baseline outcome.

See Also

estimate_its_params() for extracting all parameters at once.

Examples

data("example_cfr_data")
estimate_baseline(example_cfr_data$outcome)

Estimate all ITS nuisance parameters from pre-intervention data

Description

Convenience wrapper that estimates all four nuisance parameters needed for ITS power calculation from a pre-intervention data frame or numeric vector. The output can be passed directly to calculate_power() or run_its_power().

Usage

estimate_its_params(
  data,
  outcome_col = "outcome",
  time_col = "time",
  verbose = TRUE
)

Arguments

Details

All four nuisance parameters are estimated jointly by maximum likelihood from a single generalised least squares model with AR(1) errors, fitted with nlme::gls() and nlme::corAR1():

y_t = \beta_0 + \beta_1 t + \varepsilon_t, \quad \varepsilon_t = \rho\,\varepsilon_{t-1} + \nu_t

This is the recommended approach because \sigma and \rho are mutually dependent: ordinary least squares estimates \sigma without accounting for autocorrelation (biased upward when \rho > 0) and then estimates \rho from serially correlated residuals. Fitting both from the same likelihood avoids this inconsistency and yields parameters that are directly compatible with the simulation engine used by calculate_power(). If GLS fails to converge - rare, and usually only for very short series (fewer than about 12 observations) - the function falls back to the OLS two-step estimator and records this in the returned method element.

The individual helpers estimate_baseline(), estimate_sigma(), estimate_rho() and estimate_trend() use the simpler OLS two-step and are provided for quick, single-parameter checks; for power calculation, prefer the jointly estimated values returned here.

This function does not estimate level_change or slope_change. Those are clinical hypotheses - the minimum effects you would consider meaningful to detect - and must be set based on clinical judgement or published evidence, not derived from your own data.

Value

A named list with elements:

n_pre

Integer. Number of pre-intervention observations.

baseline

Numeric. Estimated baseline (model intercept).

sigma

Numeric. Estimated residual standard deviation.

rho

Numeric. Estimated AR(1) autocorrelation.

trend_pre

Numeric. Estimated pre-intervention trend (slope).

method

Character. Estimation method actually used: "GLS-ML" (the default) or "OLS" (the fallback, used only when GLS fails to converge).

See Also

estimate_baseline(), estimate_sigma(), estimate_rho(), estimate_trend(), calculate_power(), diagnose_params()

Examples

data("example_cfr_data")
params <- estimate_its_params(example_cfr_data, verbose = TRUE)
str(params)

# Use directly in calculate_power():
# calculate_power(
#   n_pre        = params$n_pre,
#   n_post       = 24,
#   baseline     = params$baseline,
#   level_change = -1.0,   # your clinical hypothesis
#   sigma        = params$sigma,
#   rho          = params$rho
# )

Estimate AR(1) autocorrelation from pre-intervention data

Description

Estimates the first-order autocorrelation coefficient (\rho) from the residuals of a linear trend fit to the pre-intervention series.

Usage

estimate_rho(outcome, time = seq_along(outcome))

Arguments

Details

The estimate is the Pearson correlation between consecutive residuals:

\hat{\rho} = \text{cor}(\hat{\varepsilon}_t,\, \hat{\varepsilon}_{t+1})

Positive autocorrelation is nearly universal in routine health data and reduces effective sample size, lowering power relative to a naive calculation that assumes independence.

Typical ranges by aggregation frequency:

• Daily: 0.7-0.9

• Weekly: 0.5-0.8

• Monthly: 0.3-0.5

• Quarterly: 0.1-0.4

If outcome has fewer than 3 observations, NA is returned with a warning.

Value

A single numeric value in (-1, 1): the estimated AR(1) autocorrelation coefficient.

See Also

estimate_its_params() for extracting all parameters at once.

Examples

data("example_cfr_data")
estimate_rho(example_cfr_data$outcome)

Estimate residual standard deviation from pre-intervention data

Description

Fits a linear trend to the pre-intervention series and returns the standard deviation of the residuals, used as the noise parameter \sigma in ITS power calculations.

Usage

estimate_sigma(outcome, time = seq_along(outcome))

Arguments

Details

\sigma is the standard deviation of the detrended pre-intervention series. It captures how much the outcome varies from one time point to the next after accounting for any underlying trend. Larger \sigma means noisier data and lower power.

As a rough guide, \sigma is typically 10-20\ monthly hospital rates. If your estimate is much larger, consider whether the outcome series is stable or whether aggregation to a lower frequency would reduce noise.

Value

A single positive numeric value: the estimated residual SD (\sigma).

See Also

estimate_its_params() for extracting all parameters at once.

Examples

data("example_cfr_data")
estimate_sigma(example_cfr_data$outcome)

Estimate pre-intervention trend from pre-intervention data

Description

Fits a linear trend to the pre-intervention series and returns the slope (change per time unit). Ideally this should be close to zero, indicating a stable pre-intervention period.

Usage

estimate_trend(outcome, time = seq_along(outcome))

Arguments

Details

A non-trivial pre-intervention trend (e.g. |trend| > 0.05 \times baseline) may indicate that the outcome was not stable before the intervention. This can bias ITS estimates and should be discussed in study planning.

Value

A single numeric value: the estimated trend (slope) per time unit.

See Also

estimate_its_params() for extracting all parameters at once.

Examples

data("example_cfr_data")
estimate_trend(example_cfr_data$outcome)

Example pre-intervention case fatality rate data

Description

Monthly case fatality rate (CFR) observations from a hypothetical hospital over a 24-month pre-intervention period. This dataset is used in the package vignettes to illustrate the full PITS workflow: parameter estimation followed by power calculation. It represents the motivating example from the accompanying paper, in which a hospital evaluates whether a clinical decision support system (CDSS) reduces case fatality rate and wishes to determine how long post-intervention follow-up is needed to detect a meaningful effect.

Usage

example_cfr_data

Format

A data frame with 24 rows and 2 variables:

time

Integer. Sequential monthly time index, 1 to 24.

outcome

Numeric. Case fatality rate, expressed as a percentage (for example, 3.5 represents 3.5 per cent).

Details

The pre-intervention CFR is approximately 3.6 per cent, with low residual variability (sigma approximately 0.2) and moderate positive autocorrelation (rho approximately 0.3 to 0.5), consistent with monthly hospital data.

Source

Simulated dataset generated for illustrative purposes.

Examples

data("example_cfr_data")
head(example_cfr_data)
plot(example_cfr_data$time, example_cfr_data$outcome, type = "o",
     xlab = "Month", ylab = "CFR (per cent)",
     main = "Pre-intervention CFR")

# Estimate parameters:
params <- estimate_its_params(example_cfr_data)

Export PITS results to CSV and plain-text summary

Description

Writes the output of calculate_power() or power_sweep() to timestamped files in the specified directory.

Usage

export_results(result, dir, prefix = "pits")

Arguments

Value

Invisibly, a named character vector of file paths written.

Examples

result <- calculate_power(n_pre = 24, n_post = 24, baseline = 15,
                          level_change = -3, sigma = 2.5, rho = 0.4,
                          n_sim = 100)
# Write to a temporary directory (use your own path in practice):
export_results(result, dir = tempdir())

Fit a segmented regression model to an ITS dataset

Description

Fits a Gaussian segmented-regression model with AR(1) autocorrelation correction using nlme::gls(), and returns the p-value for the coefficient(s) specified by test.

Usage

fit_its_model(data, test = c("level", "slope", "both"))

Arguments

Details

The model fitted is:

y_t = \beta_0 + \beta_1 t + \delta D_t + \gamma T_t^* + \varepsilon_t, \quad \varepsilon_t \sim AR(1)

Estimation is by maximum likelihood (method = "ML") to support likelihood-ratio tests. For the "both" option, the full model is compared against a model with only a linear trend (\delta = \gamma = 0).

Value

A single numeric p-value, or NA if the model failed to converge.

See Also

simulate_its_data(), calculate_power()

Examples

df <- simulate_its_data(
  n_pre = 24, n_post = 24,
  baseline = 15, level_change = -3,
  slope_change = 0, sigma = 2.5, rho = 0.4
)
fit_its_model(df, test = "level")

Interpret a power estimate qualitatively

Description

Converts a numeric power estimate to a short descriptive label using conventional thresholds.

Usage

interpret_power(power)

Arguments

Value

A character string: "Adequate (>= 80%)", "Borderline (60-79%)", or "Underpowered (< 60%)".

Examples

interpret_power(0.85)
interpret_power(0.70)
interpret_power(0.45)

Plot a simulated ITS example

Description

Generates and plots one realisation of an ITS dataset, showing the pre-intervention trend, the post-intervention trajectory, the intervention breakpoint, and the fitted segmented regression lines. Useful for illustrating the ITS model in papers and presentations.

Usage

plot_its_example(
  n_pre = 24L,
  n_post = 24L,
  baseline = 15,
  level_change = -3,
  slope_change = 0,
  sigma = 2.5,
  rho = 0.4,
  seed = 42L,
  xlab = "Time",
  ylab = "Outcome",
  main = "Simulated ITS example",
  pre_col = "steelblue",
  post_col = "firebrick"
)

Arguments

Value

Invisibly, the simulated data frame.

Examples

plot_its_example(
  n_pre = 24, n_post = 24,
  baseline = 15, level_change = -3,
  sigma = 2.5, rho = 0.4
)

Plot power as a function of post-intervention duration

Description

Produces a line plot of estimated power against n_post, as returned by power_sweep(). Highlights the 80\ adequate duration.

Usage

plot_power_curve(
  sweep_result,
  target = 80,
  xlab = "Post-intervention time points (n_post)",
  ylab = "Estimated power (%)",
  main = "ITS power curve",
  col = "steelblue",
  ...
)

Arguments

Value

Invisibly NULL. Called for its side-effect (plot).

See Also

power_sweep(), plot_power_heatmap()

Examples

# Small n_sim for a fast example; use 1000+ for a reportable estimate.
sweep <- power_sweep(
  sweep_post = c(12, 24, 36),
  n_pre = 24, baseline = 15, level_change = -3,
  sigma = 2.5, rho = 0.4, n_sim = 50, seed = 42, verbose = FALSE
)
plot_power_curve(sweep)

Plot a power heatmap across two parameters

Description

Displays estimated power as a colour-coded grid, with one parameter on each axis. Useful for visualising how power responds to combinations of effect size, follow-up duration, noise, or autocorrelation.

Usage

plot_power_heatmap(
  grid_result,
  x = "n_post",
  y = "level_change",
  xlab = x,
  ylab = y,
  main = "ITS power heatmap",
  palette = NULL
)

Arguments

Value

Invisibly NULL. Called for its side-effect (plot).

See Also

run_power_grid(), build_param_grid()

Examples

grid <- build_param_grid(
  n_post       = c(12, 24, 36),
  level_change = c(-1, -2, -3),
  sigma        = 2.5,
  rho          = 0.4
)
results <- run_power_grid(grid, n_sim = 100, verbose = FALSE)
plot_power_heatmap(results, x = "n_post", y = "level_change")

Design optimisation sweep: power across a range of n_post values

Description

Runs calculate_power() for a vector of post-intervention durations, returning a data frame of power estimates. Use this to identify the minimum n_post needed to achieve \ge 80\% power.

Usage

power_sweep(sweep_post = c(6L, 12L, 18L, 24L, 30L, 36L), ..., verbose = TRUE)

Arguments

Value

A data frame with columns:

n_post

Integer. Follow-up duration evaluated.

power

Numeric. Estimated power (0-1).

power_pct

Numeric. Power as a percentage.

interpretation

Character. Qualitative label.

n_converged

Integer. Number of converged replications.

See Also

calculate_power(), plot_power_curve()

Examples

# Small n_sim for a fast example; use 1000+ for a reportable estimate.
sweep <- power_sweep(
  sweep_post   = c(12, 24, 36),
  n_pre        = 24,
  baseline     = 15,
  level_change = -3,
  sigma        = 2.5,
  rho          = 0.4,
  n_sim        = 100,
  seed         = 42,
  verbose      = FALSE
)
plot_power_curve(sweep)

Print method for PITS power results

Description

Displays a formatted summary of the output from calculate_power() or calculate_power_multi().

Usage

## S3 method for class 'pits_power_result'
print(x, ...)

Arguments

Value

Invisibly x.

Print method for PITS sweep results

Description

Displays a formatted table of power estimates across post-intervention durations, as returned by power_sweep().

Usage

## S3 method for class 'pits_sweep_result'
print(x, ...)

Arguments

Value

Invisibly x.

Full single-site ITS power workflow

Description

Convenience wrapper that replicates the interactive experience of its_power_tool.R. Runs the power simulation and optionally a design sweep, prints formatted output to the console, and optionally saves results.

Usage

run_its_power(
  n_pre,
  n_post,
  baseline,
  level_change,
  slope_change = 0,
  sigma,
  rho,
  test = c("level", "slope", "both"),
  alpha = 0.05,
  n_sim = 1000L,
  seed = 123L,
  sweep = FALSE,
  sweep_post = c(6L, 12L, 18L, 24L, 30L, 36L),
  save_output = FALSE,
  output_dir = NULL
)

Arguments

Value

Invisibly, a list with elements result (from calculate_power()) and, if sweep = TRUE, sweep (from power_sweep()).

See Also

calculate_power(), power_sweep(), estimate_its_params()

Examples

run_its_power(
  n_pre = 24, n_post = 24,
  baseline = 15, level_change = -3,
  sigma = 2.5, rho = 0.4,
  n_sim = 100, sweep = TRUE
)

Run power calculations across a parameter grid

Description

Applies calculate_power() to each row of a parameter grid as produced by build_param_grid(), returning a data frame with the estimated power for each combination.

Usage

run_power_grid(
  grid,
  test = c("level", "slope", "both"),
  alpha = 0.05,
  n_sim = 500L,
  seed = 123L,
  verbose = TRUE
)

Arguments

Value

The input grid data frame with columns appended: power, power_pct, interpretation, n_converged.

See Also

build_param_grid(), plot_power_heatmap()

Examples

grid <- build_param_grid(
  n_post       = c(12, 24, 36),
  level_change = c(-2, -3),
  sigma        = 2.5,
  rho          = 0.4
)
results <- run_power_grid(grid, n_sim = 100, verbose = FALSE)
plot_power_heatmap(results, x = "n_post", y = "level_change")

Simulate a single ITS dataset

Description

Generates one realisation of an ITS time series under the alternative hypothesis (i.e. with a true intervention effect), with AR(1) autocorrelated errors. Used internally by calculate_power().

Usage

simulate_its_data(
  n_pre,
  n_post,
  baseline,
  level_change,
  slope_change,
  sigma,
  rho,
  pre_trend = 0
)

Arguments

Details

The data-generating process is the standard segmented-regression ITS model:

y_t = \beta_0 + \beta_1 t + \delta D_t + \gamma T_t^* + \varepsilon_t

where D_t = \mathbf{1}(t > n_{pre}), T_t^* = (t - n_{pre}) \cdot D_t, \delta = level_change, \gamma = slope_change, and \varepsilon_t follows an AR(1) process with innovation SD \sigma \sqrt{1 - \rho^2}.

Value

A data frame with columns:

time

Integer time index, 1 to n_{pre} + n_{post}.

D

Binary intervention indicator (0 = pre, 1 = post).

time_after

Time elapsed since intervention (0 in pre-period).

y

Simulated outcome values.

See Also

calculate_power(), fit_its_model()

Examples

df <- simulate_its_data(
  n_pre = 24, n_post = 24,
  baseline = 15, level_change = -3,
  slope_change = 0, sigma = 2.5, rho = 0.4
)
plot(df$time, df$y, type = "o", pch = 16,
     xlab = "Time", ylab = "Outcome")
abline(v = 24.5, lty = 2, col = "red")

Generate synthetic pre-intervention data

Description

Simulates a pre-intervention time series with known parameters. Useful for testing and for vignette examples when real data are unavailable.

Usage

simulate_predata(
  n = 24L,
  baseline = 15,
  sigma = 2.5,
  rho = 0.4,
  trend = 0,
  seed = 42L
)

Arguments

Value

A data frame with columns time and outcome.

Examples

pre <- simulate_predata(n = 24, baseline = 15, sigma = 2.5, rho = 0.4)
plot(pre$time, pre$outcome, type = "o")

Summary method for PITS power results

Description

Returns an extended summary including the p-value distribution across Monte Carlo replications.

Usage

## S3 method for class 'pits_power_result'
summary(object, ...)

Arguments

Value

Invisibly, a list with power, params, and pvalue_quantiles.

Validate ITS parameter values before simulation

Description

Checks that a set of ITS parameters is internally consistent and within plausible ranges. Issues warnings for unusual but permitted values.

Usage

validate_params(
  n_pre,
  n_post,
  baseline,
  level_change,
  sigma,
  rho,
  alpha = 0.05,
  n_sim = 1000L
)

Arguments

Value

Invisibly TRUE if all checks pass; stops with an error on critical failures.

Examples

validate_params(n_pre = 24, n_post = 24, baseline = 15,
                level_change = -3, sigma = 2.5, rho = 0.4)