Your browser doesn't support javascript.
loading
Creating effective interrupted time series graphs: Review and recommendations.
Turner, Simon L; Karahalios, Amalia; Forbes, Andrew B; Taljaard, Monica; Grimshaw, Jeremy M; Korevaar, Elizabeth; Cheng, Allen C; Bero, Lisa; McKenzie, Joanne E.
Afiliação
  • Turner SL; School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.
  • Karahalios A; School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.
  • Forbes AB; School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.
  • Taljaard M; Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada.
  • Grimshaw JM; School of Epidemiology and Public Health, University of Ottawa, Ottawa, Ontario, Canada.
  • Korevaar E; Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada.
  • Cheng AC; School of Epidemiology and Public Health, University of Ottawa, Ottawa, Ontario, Canada.
  • Bero L; Department of Medicine, University of Ottawa, Ottawa, Ontario, Canada.
  • McKenzie JE; School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.
Res Synth Methods ; 12(1): 106-117, 2021 Jan.
Article em En | MEDLINE | ID: mdl-32657532
INTRODUCTION: Interrupted Time Series (ITS) studies may be used to assess the impact of an interruption, such as an intervention or exposure. The data from such studies are particularly amenable to visual display and, when clearly depicted, can readily show the short- and long-term impact of an interruption. Further, well-constructed graphs allow data to be extracted using digitizing software, which can facilitate their inclusion in systematic reviews and meta-analyses. AIM: We provide recommendations for graphing ITS data, examine the properties of plots presented in ITS studies, and provide examples employing our recommendations. METHODS AND RESULTS: Graphing recommendations from seminal data visualization resources were adapted for use with ITS studies. The adapted recommendations cover plotting of data points, trend lines, interruptions, additional lines and general graph components. We assessed whether 217 graphs from recently published (2013-2017) ITS studies met our recommendations and found that 130 graphs (60%) had clearly distinct data points, 100 (46%) had trend lines, and 161 (74%) had a clearly defined interruption. Accurate data extraction (requiring distinct points that align with axis tick marks and labels that allow the points to be interpreted) was possible in only 72 (33%) graphs. CONCLUSION: We found that many ITS graphs did not meet our recommendations and could be improved with simple changes. Our proposed recommendations aim to achieve greater standardization and improvement in the display of ITS data, and facilitate re-use of the data in systematic reviews and meta-analyses.
Assuntos
Palavras-chave

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Análise de Séries Temporais Interrompida / Visualização de Dados Tipo de estudo: Systematic_reviews Limite: Humans Idioma: En Revista: Res Synth Methods Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Austrália

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Análise de Séries Temporais Interrompida / Visualização de Dados Tipo de estudo: Systematic_reviews Limite: Humans Idioma: En Revista: Res Synth Methods Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Austrália