PITS News

PITS 0.1.0 (2026-03-26)

Initial release

Parameter estimation

• estimate_its_params() — all-in-one wrapper: estimates baseline, sigma, rho, and trend_pre jointly by maximum likelihood from a single GLS model with AR(1) errors (nlme::gls + nlme::corAR1), with an automatic OLS fallback if GLS fails to converge. The estimation method used is reported in the returned method element. This matches the simulation engine and avoids the upward bias in sigma (and the unreliable rho) that the OLS two-step produces under autocorrelation. Accepts a data frame or a plain numeric vector; handles date-indexed time columns and missing values with informative warnings.
• Individual functions: estimate_baseline(), estimate_sigma(), estimate_rho(), estimate_trend(). Each uses a simple OLS two-step and is intended for quick single-parameter checks; for power calculation, prefer the jointly estimated values from estimate_its_params().

Power simulation

• simulate_its_data() — data-generating process for a single ITS site: Gaussian outcome with linear trend, step change, slope change, and AR(1) autocorrelated errors. Supports negative rho.
• fit_its_model() — segmented regression via nlme::gls() with AR(1) correction; returns p-value for "level", "slope", or "both" effects.
• calculate_power() — Monte Carlo power estimation for single-site ITS. Returns a pits_power_result object.
• calculate_power_multi() — power for multi-site designs via nlme::lme() with site random intercepts and per-site AR(1). Returns a pits_power_result object.
• power_sweep() — runs calculate_power() across a vector of n_post values; returns a pits_sweep_result data frame.
• build_param_grid() — constructs a factorial parameter grid for sensitivity analyses.
• run_power_grid() — applies calculate_power() to every row of a grid; appends power estimates to the grid data frame.

Plots and diagnostics

• plot_power_curve() — line plot of power vs n_post, with 80% target line and minimum adequate duration marker.
• plot_power_heatmap() — colour-coded power grid across two parameters, with cell labels and adequacy marker.
• plot_its_example() — simulated ITS series with fitted segmented regression overlaid; useful for figures in papers and protocols.
• diagnose_params() — 2×2 diagnostic panel: observed series + trend, residuals over time, Q-Q plot, and residual ACF.

S3 methods

• print.pits_power_result() — formatted console output for calculate_power() and calculate_power_multi() results.
• summary.pits_power_result() — extended output including p-value quantile distribution.
• print.pits_sweep_result() — formatted table with adequacy markers for power_sweep() output.

Utilities and wrappers

• interpret_power() — converts a numeric power estimate to "Adequate (>= 80%)", "Borderline (60-79%)", or "Underpowered (< 60%)".
• validate_params() — pre-flight parameter checks with informative errors and warnings.
• simulate_predata() — generates synthetic pre-intervention data with known parameters; useful for testing and vignette examples.
• export_results() — saves pits_power_result or pits_sweep_result objects to timestamped CSV and plain-text summary files. The output directory (dir) must be supplied by the user; there is no default path, so nothing is ever written to the working directory or home filespace unless explicitly requested.
• run_its_power() — full single-site workflow with console output and optional sweep and file saving.
• estimate_and_calculate() — chains parameter estimation and power calculation in a single call.

Data

• example_cfr_data — 24 months of synthetic monthly case fatality rate data (baseline ≈ 14.7%, sigma ≈ 1.51, rho ≈ 0.37) for use in vignettes and examples.

Vignettes

• PITS: Power of an ITS — CDSS/CFR worked example: complete workflow from parameter estimation through design optimisation, sensitivity analysis, multi-site power, and export.
• Estimating ITS parameters from pre-intervention data: detailed guide to individual and all-in-one estimation functions, diagnostic interpretation, and handling of missing data, date columns, and short pre-periods.