Interrupted time-series design and diagnostics worksheet

Credibility depends on pre-data volume and absence of concurrent external factors. Check assumptions before design.

When to use this worksheet and what completion involves Fictional completed example

EvalCompass Worksheet

Interrupted time-series design and diagnostics worksheet

Method: Interrupted Time Series Analysis

Credibility depends on pre-data volume and absence of concurrent external factors. Check assumptions before design.

Basic information

Enter evaluation basics

Completed example for this section (fictional)

This fictional example is for explanation. Adapt it to your own program. Your entries will not change.

Policy/program name: Booking system introduction
Analyst: Analyst A (fictional)
Data sources: Monthly registration records (fictional)
Creation date: July 1, 2026 (fictional)

Reason for this entry: Record start time.

Policy/program name

Analyst

Data sources

Creation date

Writing tips and explanation for this section

About this section

Enter evaluation basics

Record start time.

View the method explanation

Writing tips

Follow each field’s heading to record the program name, focus, period, and other details. Leave undecided matters clearly marked as unresolved.

Read the full fictional example

Check time-series data

Check dataset structure

Completed example for this section (fictional)

This fictional example is for explanation. Adapt it to your own program. Your entries will not change.

Outcome and units: Mean waiting time in minutes
Monthly / quarterly / annual units: Monthly
Pre-intervention timepoint count: Plan 24 pre-timepoints
Post-intervention timepoint count: Plan 12 post-timepoints
Exact intervention year/month: April 2026 (fictional)

Reason for this entry: Assumed seasonality coverage, not universal required counts.

Outcome and units

Monthly / quarterly / annual units

Pre-intervention timepoint count

Post-intervention timepoint count

Exact intervention year/month

Writing tips and explanation for this section

About this section

Check dataset structure

Assumed seasonality coverage, not universal required counts.

View the method explanation

Writing tips

Follow each field’s heading to record the program name, focus, period, and other details. Leave undecided matters clearly marked as unresolved.

Read the full fictional example

External factors

List possible concurrent policies and external shocks

Completed example for this section (fictional)

This fictional example is for explanation. Adapt it to your own program. Your entries will not change.

Check staffing, opening hours and user composition. Simultaneous booking and staffing changes limit separate effects.

Reason for this entry: Track structural changes beyond policy.

Writing tips and explanation for this section

About this section

List possible concurrent policies and external shocks

Track structural changes beyond policy.

View the method explanation

Writing tips

Use the example as a reference and record what you have established about your own program. Distinguish assumptions and unchecked matters from confirmed facts.

Read the full fictional example

Consider comparison groups

Enter comparison areas/groups' availability and rationale

Completed example for this section (fictional)

This fictional example is for explanation. Adapt it to your own program. Your entries will not change.

Check similarly defined monthly waiting at nearby hubs. Avoid comparators exposed to booking spillovers.

Reason for this entry: Assess comparison-series validity.

Writing tips and explanation for this section

About this section

Enter comparison areas/groups' availability and rationale

Assess comparison-series validity.

View the method explanation

Writing tips

Use the example as a reference and record what you have established about your own program. Distinguish assumptions and unchecked matters from confirmed facts.

Read the full fictional example

Analysis checklist

Completed example for this section (fictional)

This fictional example is for explanation. Adapt it to your own program. Your entries will not change.

☐ Not checked: Assessed pre/post counts against variability, seasonality, dependence and precision
☐ Not checked: Checked concurrent policy changes or recorded influences
☐ Not checked: Checked ACF and Durbin-Watson autocorrelation
☐ Not checked: Checked seasonality and adjusted if needed
☐ Not checked: Plotted pre/post changes
☑ Checked: Explicitly plotted extrapolated counterfactual
☐ Not checked: Conducted controlled ITS where data available

Reason for this entry: An illustration showing a counterfactual formula. Acquisition and diagnostics are undone; 8 timepoints are not universally sufficient.

Assessed pre/post counts against variability, seasonality, dependence and precision
Checked concurrent policy changes or recorded influences
Checked ACF and Durbin-Watson autocorrelation
Checked seasonality and adjusted if needed
Plotted pre/post changes
Explicitly plotted extrapolated counterfactual
Conducted controlled ITS where data available

Writing tips and explanation for this section

About this section

An illustration showing a counterfactual formula. Acquisition and diagnostics are undone; 8 timepoints are not universally sufficient.

View the method explanation

Writing tips

Read each item and check those you have confirmed. Do not use the fictional example’s checkbox states as findings about your own program.

Read the full fictional example

Before writing, check when to use the worksheet and what completion involves

Interrupted time-series design and diagnostics worksheet is a worksheet for applying Interrupted Time Series Analysis in meetings or individual work. For the overall duration of the method, use “Illustrative planning durations, not common standards: collection 1–4 weeks, analysis 1–2 weeks” as a reference, adjusting for scale and resources. Fill in what you know provisionally and leave unresolved matters as questions to check next.

When this worksheet is useful

Monthly/annual administrative, disease or crime series around law, policy or program changes
Nationwide or whole-area policies without easy controls but with long series
Health/public-health effects of drug approvals, infection measures or screening using aggregates

Participants and preparation

1–2 evaluators/statisticians using R or similar software, securing data with administrative statistics staff

Bring the materials used for decisions, existing data, and differing stakeholder views to help complete the worksheet.

Suggested order

1. Basic informationEnter evaluation basics
2. Check time-series dataCheck dataset structure
3. External factorsList possible concurrent policies and external shocks
4. Consider comparison groupsEnter comparison areas/groups' availability and rationale
5. Analysis checklist

Signs the worksheet is ready for review

Time-series plot: observations, fitted and counterfactual lines
Segmented coefficients with level/slope changes and intervals
Autocorrelation diagnostics: ACF and DW

Cautions for review

Do not use timepoint count alone as pass/fail. Length depends on variability, seasonality, dependence, effect shape and precision.
Ignoring dependence or distributions misestimates uncertainty. Use diagnostics and suitable models.
Distinguish seasonality from intervention changes; explain intervals, cycles, periods and adjustment.

Read a completed fictional example (your entries will not change)

Fictional example; not actual results

A fictional model: pre-waiting is 100−t minutes; introduction at t=12 gives immediate −8 minutes and additional slope −0.5 minutes/month.

Basic information

Policy/program name: Booking system introduction
Analyst: Analyst A (fictional)
Data sources: Monthly registration records (fictional)
Creation date: July 1, 2026 (fictional)

Reason for this entry: Record start time.

Check time-series data

Outcome and units: Mean waiting time in minutes
Monthly / quarterly / annual units: Monthly
Pre-intervention timepoint count: Plan 24 pre-timepoints
Post-intervention timepoint count: Plan 12 post-timepoints
Exact intervention year/month: April 2026 (fictional)

Reason for this entry: Assumed seasonality coverage, not universal required counts.

External factors

Check staffing, opening hours and user composition. Simultaneous booking and staffing changes limit separate effects.

Reason for this entry: Track structural changes beyond policy.

Consider comparison groups

Check similarly defined monthly waiting at nearby hubs. Avoid comparators exposed to booking spillovers.

Reason for this entry: Assess comparison-series validity.

Analysis checklist

☐ Not checked: Assessed pre/post counts against variability, seasonality, dependence and precision
☐ Not checked: Checked concurrent policy changes or recorded influences
☐ Not checked: Checked ACF and Durbin-Watson autocorrelation
☐ Not checked: Checked seasonality and adjusted if needed
☐ Not checked: Plotted pre/post changes
☑ Checked: Explicitly plotted extrapolated counterfactual
☐ Not checked: Conducted controlled ITS where data available

Reason for this entry: An illustration showing a counterfactual formula. Acquisition and diagnostics are undone; 8 timepoints are not universally sufficient.

Common errors in judgment

Compare only immediately adjacent months

How to revise: Examine trends/seasonality over sufficient before/after series

Reason: Temporary fluctuations are easily mistaken for effects.

Assume more timepoints resolve concurrent policies

How to revise: Record concurrent changes and consider comparison series/evidence

Reason: Simultaneous factors cannot be separated by count alone.

Editorial and checking information

Content updated: 2026-09-13Source checked: 2026-09-13 (includes AI cross-checking)No documented human reviewIncludes fictional examplesEditorial policy and scope of checking Cite this page Report a correction

References and checked sections

Interrupted time series regression for the evaluation of public health interventions: a tutorial
James Lopez Bernal, Steven Cummins, and Antonio Gasparrini · 2017
Section consulted
The interrupted time series design / Step 4: regression analysis
How it relates to the text
Predefine effect shapes and examine autocorrelation, seasonality and time-varying confounding.
Source checked: 2026-09-13 (includes AI cross-checking)

How to cite this page

EvalCompass Editorial Team. “Interrupted time-series design and diagnostics worksheet”. EvalCompass, updated 2026-09-13. https://evalcompass.jun-nakatani.com/en/worksheets/its/

Add the date you accessed the page. Text created by this site is available under CC BY 4.0. Quotations, external materials, and bundled fonts have their own terms of use. When reusing content, credit the source, link to the license, and indicate any changes.

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Change history

2026-09-13 — Checked sources for definitions and conditions and added fictional examples, judgment rationales and completed entries for every field. Revised unsupported historical or uptake claims. Reviewed procedures and cautions, adjusting fixed counts, thresholds and institutional generalizations. Preserved field positions, types and counts while revising fixed conditions and thresholds.