Simon L Turner1, Amalia Karahalios1, Andrew B Forbes1, Monica Taljaard2, Jeremy M Grimshaw3, Allen C Cheng4, Lisa Bero5, Joanne E McKenzie6. 1. School of Public Health and Preventive Medicine, Monash University, 533 St. Kilda Road, Melbourne, Victoria 3004, Australia. 2. Clinical Epidemiology Program, Ottawa Hospital Research Institute, 1053 Carling Avenue, Ottawa, Ontario, Canada; School of Epidemiology, Public Health and Preventive Medicine, University of Ottawa, 75 Laurier Avenue Eeast, Ottawa, Ontario, Canada. 3. Clinical Epidemiology Program, Ottawa Hospital Research Institute, 1053 Carling Avenue, Ottawa, Ontario, Canada; School of Epidemiology, Public Health and Preventive Medicine, University of Ottawa, 75 Laurier Avenue Eeast, Ottawa, Ontario, Canada; Department of Medicine, University of Ottawa, Roger Guindon Hall, 451 Smyth Roadd, Ottawa, Ontario, Canada. 4. School of Public Health and Preventive Medicine, Monash University, 533 St. Kilda Road, Melbourne, Victoria 3004, Australia; Infection Prevention and Healthcare Epidemiology Unit, Alfred Health, 55 Commercial Road, Melbourne, Victoria 3004, Australia. 5. Faculty of Pharmacy and Charles Perkins Centre, The University of Sydney, John Hopkins Dr, Camperdown NSW, Sydney, New South Wales 2006, Australia. 6. School of Public Health and Preventive Medicine, Monash University, 533 St. Kilda Road, Melbourne, Victoria 3004, Australia. Electronic address: joanne.mckenzie@monash.edu.
Abstract
OBJECTIVES: Interrupted time series (ITS) designs are frequently used in public health to examine whether an intervention or exposure has influenced health outcomes. Few reviews have been undertaken to examine the design characteristics, statistical methods, and completeness of reporting of published ITS studies. STUDY DESIGN AND SETTING: We used stratified random sampling to identify 200 ITS studies that evaluated public health interventions or exposures from PubMed (2013-2017). Study characteristics, details of statistical models and estimation methods used, effect metrics, and parameter estimates were extracted. From the 200 studies, 230 time series were examined. RESULTS: Common statistical methods used were linear regression (31%, 72/230) and autoregressive integrated moving average (19%, 43/230). In 17% (40/230) of the series, we could not determine the statistical method used. Autocorrelation was acknowledged in 63% (145/230) of the series. An estimate of the autocorrelation coefficient was given for only 1% of the series (3/230). Measures of precision were reported for 63% of effect measures (541/852). CONCLUSION: Many aspects of the design, methods, analysis, and reporting of ITS studies can be improved, particularly description of the statistical methods and approaches to adjust for and estimate autocorrelation. More guidance on the conduct and reporting of ITS studies is needed to improve this study design.
OBJECTIVES: Interrupted time series (ITS) designs are frequently used in public health to examine whether an intervention or exposure has influenced health outcomes. Few reviews have been undertaken to examine the design characteristics, statistical methods, and completeness of reporting of published ITS studies. STUDY DESIGN AND SETTING: We used stratified random sampling to identify 200 ITS studies that evaluated public health interventions or exposures from PubMed (2013-2017). Study characteristics, details of statistical models and estimation methods used, effect metrics, and parameter estimates were extracted. From the 200 studies, 230 time series were examined. RESULTS: Common statistical methods used were linear regression (31%, 72/230) and autoregressive integrated moving average (19%, 43/230). In 17% (40/230) of the series, we could not determine the statistical method used. Autocorrelation was acknowledged in 63% (145/230) of the series. An estimate of the autocorrelation coefficient was given for only 1% of the series (3/230). Measures of precision were reported for 63% of effect measures (541/852). CONCLUSION: Many aspects of the design, methods, analysis, and reporting of ITS studies can be improved, particularly description of the statistical methods and approaches to adjust for and estimate autocorrelation. More guidance on the conduct and reporting of ITS studies is needed to improve this study design.
Authors: Elizabeth Korevaar; Amalia Karahalios; Andrew B Forbes; Simon L Turner; Steve McDonald; Monica Taljaard; Jeremy M Grimshaw; Allen C Cheng; Lisa Bero; Joanne E McKenzie Journal: F1000Res Date: 2020-02-12
Authors: Margaret M Sugg; Jennifer D Runkle; Sarah N Hajnos; Shannon Green; Kurt D Michael Journal: Sci Total Environ Date: 2021-09-21 Impact factor: 10.753
Authors: Simon L Turner; Amalia Karahalios; Andrew B Forbes; Monica Taljaard; Jeremy M Grimshaw; Joanne E McKenzie Journal: BMC Med Res Methodol Date: 2021-06-26 Impact factor: 4.615