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PITS — Power of an Interrupted Time Series

An R package for estimating the statistical power of Interrupted Time Series (ITS) studies, with a focus on healthcare applications.

Use PITS to answer: > “How many months of post-intervention follow-up do I need to reliably detect a meaningful effect in my ITS study?”

Installation

# From GitHub (once published):
# install.packages("remotes")
# remotes::install_github("drdaviddelorenzo/PITS")

# From source:
install.packages("path/to/PITS", repos = NULL, type = "source")

Dependency: nlme (ships with base R). No other dependencies required.

Workflow

Pre-intervention data (CSV or data frame)
             ↓
  estimate_its_params()
             ↓
  baseline, sigma, rho
             ↓
  + level_change  ← your clinical hypothesis
             ↓
  calculate_power() / power_sweep()
             ↓
  Choose n_post that achieves ≥ 80% power

Quick start

library(PITS)

# 1. Load your pre-intervention data (or use the built-in example)
data("example_cfr_data")

# 2. Estimate nuisance parameters
params <- estimate_its_params(example_cfr_data)
#   n_pre     = 24
#   baseline  = 15.0   (mean pre-intervention CFR, %)
#   sigma     = 2.15   (residual SD)
#   rho       = 0.22   (AR(1) autocorrelation)

# 3. Calculate power for a candidate design
result <- calculate_power(
  n_pre        = params$n_pre,
  n_post       = 30,             # planned follow-up months
  baseline     = params$baseline,
  level_change = -3,             # minimum clinically meaningful effect
  sigma        = params$sigma,
  rho          = params$rho,
  n_sim        = 1000,
  seed         = 123
)
result$power_pct      # e.g. 78.4%
result$interpretation # "Borderline (60-79%)"

# 4. Optimise with a sweep
sweep <- power_sweep(
  sweep_post   = c(12, 18, 24, 30, 36, 48),
  n_pre        = params$n_pre,
  baseline     = params$baseline,
  level_change = -3,
  sigma        = params$sigma,
  rho          = params$rho,
  n_sim        = 1000
)
plot_power_curve(sweep)

# 5. One-call shortcut
result <- estimate_and_calculate(
  data         = example_cfr_data,
  level_change = -3,
  n_post       = 30
)

Function reference

Parameter estimation

Function	Description
`estimate_its_params(data)`	Estimate all nuisance parameters at once
`estimate_baseline(outcome)`	Baseline (intercept at t = 1)
`estimate_sigma(outcome)`	Residual standard deviation
`estimate_rho(outcome)`	AR(1) autocorrelation
`estimate_trend(outcome)`	Pre-intervention trend (slope)

Power simulation

Function	Description
`calculate_power(...)`	Monte Carlo power — single site
`calculate_power_multi(sites, ...)`	Monte Carlo power — multiple sites
`power_sweep(sweep_post, ...)`	Power across a range of `n_post` values
`build_param_grid(...)`	Factorial parameter grid
`run_power_grid(grid, ...)`	Power across a full parameter grid

Plots and diagnostics

Function	Description
`plot_power_curve(sweep)`	Line plot of power vs `n_post`
`plot_power_heatmap(grid)`	Colour grid of power over two parameters
`plot_its_example(...)`	Simulated ITS series with fitted model
`diagnose_params(data)`	2×2 diagnostic panel for pre-intervention data

Utilities and wrappers

Function	Description
`interpret_power(power)`	Qualitative label (Adequate / Borderline / Underpowered)
`validate_params(...)`	Check parameters before simulation
`simulate_predata(...)`	Generate synthetic pre-intervention data
`export_results(result)`	Save results to CSV and text file
`run_its_power(...)`	Full single-site workflow with console output
`estimate_and_calculate(data, ...)`	Parameter estimation + power in one call

Key parameters

Parameter	What it is	How to set it
`n_pre`	Pre-intervention time points	Fixed — use your historical data
`n_post`	Post-intervention time points	Design lever — use `power_sweep()` to optimise
`baseline`	Mean pre-intervention outcome	From `estimate_its_params()`
`sigma`	Residual SD (noise)	From `estimate_its_params()`
`rho`	AR(1) autocorrelation	From `estimate_its_params()`; use 0.4 if unknown
`level_change`	Minimum meaningful step change	Clinical hypothesis — set based on expert judgement
`slope_change`	Minimum meaningful trend change	Clinical hypothesis — set to 0 if testing level only
`test`	Effect to test: `"level"`, `"slope"`, or `"both"`	Depends on intervention type

Multi-site designs

sites <- list(
  list(name = "Hospital A", n_pre = 24, n_post = 30,
       baseline = 15, level_change = -3, slope_change = 0,
       sigma = 2.5, rho = 0.4),
  list(name = "Hospital B", n_pre = 24, n_post = 30,
       baseline = 18, level_change = -3, slope_change = 0,
       sigma = 3.0, rho = 0.4)
)
result <- calculate_power_multi(sites, n_sim = 1000, seed = 123)
result$power_pct

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.
Zhang F, et al. (2011). Simulation-based power calculation for designing interrupted time series analyses. J Clin Epidemiol 64:1252–1261.
Wagner AK, et al. (2002). Segmented regression analysis of interrupted time series studies. J Clin Pharm Ther 27:299–309.

Citing PITS

@software{PITS_2026,
  title  = {PITS: Power of Interrupted Time Series Studies},
  author = {de Lorenzo, David},
  year   = {2026},
  url    = {https://github.com/drdaviddelorenzo/PITS}
}

Author

David de Lorenzo (ORCID 0000-0003-2042-0961) — UCL Great Ormond Street Institute of Child Health, London, UK; Neotree, London, UK.

Licence

MIT — see LICENSE.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.