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Interrupted time series regression for the evaluation of public health interventions

A tutorial

Dados Bibliográficos

ID23345495
AutoresJamie Lopez Bernal (0000-0002-1301-5653, London School of Hygiene & Tropical Medicine, autor correspondente), James Lopez Bernal, Steven Cummin (0000-0002-3957-4357, London School of Hygiene & Tropical Medicine), Antonio Gasparrini (0000-0002-2271-3568, London School of Hygiene & Tropical Medicine)
Ano2016
Volume46
Fascículo1
Páginasdyw098
Data de publicação2016-06-09
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoInternational Journal of Epidemiology (JOURNAL)
Identificadores do periódicoISSN: 0300-5771 • E-ISSN: 1464-3685
EditoraOxford University Press (OUP) (PUBLISHER)
DOI10.1093/ije/dyw098
PMID27283160
OpenAlexW2416547532
IdiomaEN
Citações recebidas377
Referências citadas22

Interrupted time series (ITS) analysis is a valuable study design for evaluating the effectiveness of population-level health interventions that have been implemented at a clearly defined point in time. It is increasingly being used to evaluate the effectiveness of interventions ranging from clinical therapy to national public health legislation. Whereas the design shares many properties of regression-based approaches in other epidemiological studies, there are a range of unique features of time series data that require additional methodological considerations. In this tutorial we use a worked example to demonstrate a robust approach to ITS analysis using segmented regression. We begin by describing the design and considering when ITS is an appropriate design choice. We then discuss the essential, yet often omitted, step of proposing the impact model a priori. Subsequently, we demonstrate the approach to statistical analysis including the main segmented regression model. Finally we describe the main methodological issues associated with ITS analysis: over-dispersion of time series data, autocorrelation, adjusting for seasonal trends and controlling for time-varying confounders, and we also outline some of the more complex design adaptations that can be used to strengthen the basic ITS design.

A priori and a posteriori · Clinical study design · Econometrics · Interrupted time series · Machine learning · Psychological intervention · Public health · Regression · Regression analysis · Statistics · Time series · Advanced Causal Inference Techniques · Computer Science · Health Systems, Economic Evaluations, Quality of Life · Mathematics · Medicine · Statistical Methods and Bayesian Inference

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Obras citantes distintas377
Citações por ano37,7
Intervalo de citações2016 - 2026 (11)
Velocidade de citaçãocurrent
Altamente citadoSim
Tipos de citaçãoNeutras: 152
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