Cited by 373Health Policy Implementation ScienceStatistical Methods in Clinical TrialsAdvanced Causal Inference Techniques

The DOI matches its Crossref record; 64 of the 67 references it cites were found in a registry or an index.

Experimental and quasi-experimental designs in implementation research

Open access

Abstract

Implementation science is focused on maximizing the adoption, appropriate use, and sustainability of effective clinical practices in real world clinical settings. Many implementation science questions can be feasibly answered by fully experimental designs, typically in the form of randomized controlled trials (RCTs). Implementation-focused RCTs, however, usually differ from traditional efficacy- or effectiveness-oriented RCTs on key parameters. Other implementation science questions are more suited to quasi-experimental designs, which are intended to estimate the effect of an intervention in the absence of randomization. These designs include pre-post designs with a non-equivalent control group, interrupted time series (ITS), and stepped wedges, the last of which require all participants to receive the intervention, but in a staggered fashion. In this article we review the use of experimental designs in implementation science, including recent methodological advances for implementation studies. We also review the use of quasi-experimental designs in implementation science, and discuss the strengths and weaknesses of these approaches. This article is therefore meant to be a practical guide for researchers who are interested in selecting the most appropriate study design to answer relevant implementation science questions, and thereby increase the rate at which effective clinical practices are adopted, spread, and sustained.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.psychres.2019.06.027

Cites

64 of 64 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 67 references

Research works printed but not linked: 1

  1. Neumann, 2000, Replicating effective programs: HIV/AIDS prevention technology transfer, AIDS Educ. Prev., 12, 35

Cited by

Sorted by citations

Metadata

Authors
Miller, C. J., Smith, S. N., & Pugatch, M.
Affiliations
  • United States Department of Veterans Affairs (US)
  • University of Michigan (US)
Corresponding author
Miller, Christopher J.
Venue
Psychiatry Research
Publisher
Elsevier BV
Published
2020-01
Volume
283
Pages
112452
ISSN
0165-1781 (ISSN-L)
OpenAlex
W2951715721
Cited by
373
References
67
Citations by year
2026: 63 · 2025: 89 · 2024: 67 · 2023: 63 · 2022: 56 · 2021: 20 · 2020: 14
FWCI
4.18
Type
journal article
Language
en
Open access
Locations
  • Psychiatry Research (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • Europe PMC (PubMed Central) (acceptedVersion)
  • PubMed Central (acceptedVersion)
Funders
  • National Institute of Mental Health
  • U.S. Department of Veterans Affairs
Topics
  • Health Sciences › Health Professions › General Health Professions › Health Policy Implementation Science
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
  • Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
Keywords
implementation research, implementation science, experimental design, quasi-experimental design, randomized controlled trials, interrupted time series, stepped wedge design, pre-post design, research design selection, clinical adoption, intervention sustainability
MeSH terms
Implementation Science, Humans, Research Design, Randomized Controlled Trials as Topic, Control Groups, Biomedical Research

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review