ArticleInternational journal of epidemiology2023
A comparison of quasi-experimental methods with data before and after an intervention: an introduction for epidemiologists and a simulation study.
Article in International journal of epidemiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
36 citing papers in PubMed.
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- Impact of a Contextualized Workplace Intervention in a Latino Population on Reducing Cardiovascular Risk and Its Associated Factors.Journal of clinical medicine · 2026Article
- Epigenetic plasticity and chemoresistance in cancer: mechanisms, biomarkers, and translational opportunities for real-world evidence.Frontiers in cell and developmental biology · 2026Review
- Demand-Matched Self-Efficacy and Perceived User Incivility in Healthcare: A Two-Wave Study and an Exploratory Training Intervention.Journal of nursing management · 2026Article
- Changes in out-of-home food purchasing following the introduction of England's calorie labelling regulations: a population-level controlled interrupted time series analysis.BMJ public health · 2026Article
- Impact of the compact county medical service community policy on healthcare resource allocation and healthcare service utilization in village clinics: a difference-in-differences analysis in rural Sichuan, China.Frontiers in public health · 2026Article
- Impacts of the Pacific Northwest Heat Dome on preterm birth rates in Oregon and Washington State.medRxiv : the preprint server for health sciences · 2025Article
- Interrupted Time Series Analysis in Environmental Epidemiology: A Review of Traditional and Novel Modeling Approaches.Current environmental health reports · 2025Review
- The 2025 Los Angeles Wildfires and Outpatient Acute Health Care Utilization.JAMA health forum · 2025Article
- Advancing equitable and sustainable urban health.Nature medicine · 2025Review
- Two-stage interrupted time series analysis with machine learning: evaluating the health effects of the 2018 wildfire smoke event in San Francisco County as a case study.American journal of epidemiology · 2025Article
- Investigating the Aliso Canyon gas blowout disaster and adverse birth outcomes: A quasiexperimental approach.Science advances · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
backgroundAs the interest in and use of quasi-experimental methods to evaluate impacts of health policies have dramatically increased in the epidemiological literature, we set out this study to (i) systematically compare several quasi-experimental methods that use data before and after an intervention and contrast their performance within a simulation framework while providing a brief overview of the methods; and (ii) discuss challenges that could arise from using these methods as well as directions for future research in the context of epidemiological applications.
methodsWe considered single-group designs [pre-post and interrupted time series (ITS)] and multiple-group designs [controlled interrupted time series/difference-in-differences, synthetic control methods (SCMs): traditional SCMs and generalized SCMs]. We assessed performance based on bias and root mean squared error.
resultsWe identified settings in which each method failed to provide unbiased estimates. We found that, among the methods investigated, when data for multiple time points and for multiple control groups are available (multiple-group designs), data-adaptive methods such as the generalized SCM were generally less biased than other methods evaluated in our study. In addition, when all of the included units have been exposed to treatment (single-group designs) and data for a sufficiently long pre-intervention period are available, then the ITS performs very well, provided the underlying model is correctly specified.
conclusionsWhen using a quasi-experimental method using data before and after an intervention, epidemiologists should strive to use, whenever feasible, data-adaptive methods that nest alternative identifying assumptions including relaxing the parallel trend assumption (e.g. generalized SCMs).
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