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@article{turner_design_2020,
title = {Design characteristics and statistical methods used in interrupted time series studies evaluating public health interventions: a review},
volume = {122},
issn = {0895-4356, 1878-5921},
shorttitle = {Design characteristics and statistical methods used in interrupted time series studies evaluating public health interventions},
url = {https://www.jclinepi.com/article/S0895-4356(19)30724-3/abstract},
doi = {10.1016/j.jclinepi.2020.02.006},
abstract = {{\textless}h2{\textgreater}Abstract{\textless}/h2{\textgreater}{\textless}h3{\textgreater}Objectives{\textless}/h3{\textgreater}{\textless}p{\textgreater}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.{\textless}/p{\textgreater}{\textless}h3{\textgreater}Study Design and Setting{\textless}/h3{\textgreater}{\textless}p{\textgreater}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.{\textless}/p{\textgreater}{\textless}h3{\textgreater}Results{\textless}/h3{\textgreater}{\textless}p{\textgreater}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).{\textless}/p{\textgreater}{\textless}h3{\textgreater}Conclusion{\textless}/h3{\textgreater}{\textless}p{\textgreater}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.{\textless}/p{\textgreater}},
language = {English},
urldate = {2021-01-10},
journal = {Journal of Clinical Epidemiology},
author = {Turner, Simon L. and Karahalios, Amalia and Forbes, Andrew B. and Taljaard, Monica and Grimshaw, Jeremy M. and Cheng, Allen C. and Bero, Lisa and McKenzie, Joanne E.},
month = jun,
year = {2020},
pages = {1--11},
file = {Full Text PDF:C\:\\Users\\z9292540\\Zotero\\storage\\UPWEBFCX\\Turner et al. - 2020 - Design characteristics and statistical methods use.pdf:application/pdf;Snapshot:C\:\\Users\\z9292540\\Zotero\\storage\\IJIIBBI4\\abstract.html:text/html},
}
@article{hudson_methodology_2019,
title = {Methodology and reporting characteristics of studies using interrupted time series design in healthcare},
volume = {19},
issn = {1471-2288},
url = {https://doi.org/10.1186/s12874-019-0777-x},
doi = {10.1186/s12874-019-0777-x},
abstract = {Randomised controlled trials (RCTs) are considered the gold standard when evaluating the causal effects of healthcare interventions. When RCTs cannot be used (e.g. ethically difficult), the interrupted time series (ITS) design is a possible alternative. ITS is one of the strongest quasi-experimental designs. The aim of this methodological study was to describe how ITS designs were being used, the design characteristics, and reporting in the healthcare setting.},
number = {1},
urldate = {2021-01-10},
journal = {BMC Medical Research Methodology},
author = {Hudson, Jemma and Fielding, Shona and Ramsay, Craig R.},
month = jul,
year = {2019},
keywords = {Healthcare interventions, Interrupted time series, Quasi-experimental},
pages = {137},
file = {Full Text PDF:C\:\\Users\\z9292540\\Zotero\\storage\\XXNXZKQN\\Hudson et al. - 2019 - Methodology and reporting characteristics of studi.pdf:application/pdf;Snapshot:C\:\\Users\\z9292540\\Zotero\\storage\\95HUZ33G\\s12874-019-0777-x.html:text/html},
}
@article{turner_evaluation_2020,
title = {Evaluation of statistical methods used in the analysis of interrupted time series studies: a simulation study},
copyright = {© 2020, Posted by Cold Spring Harbor Laboratory. The copyright holder for this pre-print is the author. All rights reserved. The material may not be redistributed, re-used or adapted without the author's permission.},
shorttitle = {Evaluation of statistical methods used in the analysis of interrupted time series studies},
url = {https://www.medrxiv.org/content/10.1101/2020.10.12.20211706v1},
doi = {10.1101/2020.10.12.20211706},
abstract = {{\textless}h3{\textgreater}Abstract{\textless}/h3{\textgreater} {\textless}p{\textgreater}Interrupted time series (ITS) studies are frequently used to evaluate the effects of population-level interventions or exposures. To our knowledge, no studies have compared the performance of different statistical methods for this design. We simulated data to compare the performance of a set of statistical methods under a range of scenarios which included different level and slope changes, varying lengths of series and magnitudes of autocorrelation. We also examined the performance of the Durbin-Watson (DW) test for detecting autocorrelation. All methods yielded unbiased estimates of the level and slope changes over all scenarios. The magnitude of autocorrelation was underestimated by all methods, however, restricted maximum likelihood (REML) yielded the least biased estimates. Underestimation of autocorrelation led to standard errors that were too small and coverage less than the nominal 95\%. All methods performed better with longer time series, except for ordinary least squares (OLS) in the presence of autocorrelation and Newey-West for high values of autocorrelation. The DW test for the presence of autocorrelation performed poorly except for long series and large autocorrelation. From the methods evaluated, OLS was the preferred method in series with fewer than 12 points, while in longer series, REML was preferred. The DW test should not be relied upon to detect autocorrelation, except when the series is long. Care is needed when interpreting results from all methods, given confidence intervals will generally be too narrow. Further research is required to develop better performing methods for ITS, especially for short series.{\textless}/p{\textgreater}},
language = {en},
urldate = {2021-05-17},
journal = {medRxiv},
author = {Turner, Simon L. and Forbes, Andrew B. and Karahalios, Amalia and Taljaard, Monica and McKenzie, Joanne E.},
month = oct,
year = {2020},
pages = {2020.10.12.20211706},
file = {Full Text PDF:C\:\\Users\\z9292540\\Zotero\\storage\\VFHZIRTK\\Turner et al. - 2020 - Evaluation of statistical methods used in the anal.pdf:application/pdf;Snapshot:C\:\\Users\\z9292540\\Zotero\\storage\\RXTZ8PMS\\2020.10.12.html:text/html},
}
@article{turner_comparison_2020,
title = {Comparison of {Six} {Statistical} {Methods} for {Interrupted} time {Series} {Studies}: {Empirical} {Evaluation} of 190 {Published} {Series}},
shorttitle = {Comparison of {Six} {Statistical} {Methods} for {Interrupted} time {Series} {Studies}},
url = {https://dx.doi.org/10.21203/rs.3.rs-118335/v1},
doi = {10.21203/rs.3.rs-118335/v1},
language = {en},
urldate = {2021-05-17},
journal = {BMC Medical Research Methodology (preprint)},
author = {Turner, Simon L. and Karahalios, Amalia and Forbes, Andrew B. and Taljaard, Monica and Grimshaw, Jeremy M. and McKenzie, Joanne E.},
month = dec,
year = {2020},
file = {Full Text PDF:C\:\\Users\\z9292540\\Zotero\\storage\\93RSFIRY\\2020 - Comparison of Six Statistical Methods for Interrup.pdf:application/pdf;Snapshot:C\:\\Users\\z9292540\\Zotero\\storage\\EPLE2PXX\\v1.html:text/html},
}
@article{lazarus_har_2018,
title = {{HAR} {Inference}: {Recommendations} for {Practice}},
volume = {36},
issn = {0735-0015},
shorttitle = {{HAR} {Inference}},
url = {https://doi.org/10.1080/07350015.2018.1506926},
doi = {10.1080/07350015.2018.1506926},
abstract = {The classic papers by Newey and West (1987) and Andrews (1991) spurred a large body of work on how to improve heteroscedasticity- and autocorrelation-robust (HAR) inference in time series regression. This literature finds that using a larger-than-usual truncation parameter to estimate the long-run variance, combined with Kiefer-Vogelsang (2002, 2005) fixed-b critical values, can substantially reduce size distortions, at only a modest cost in (size-adjusted) power. Empirical practice, however, has not kept up. This article therefore draws on the post-Newey West/Andrews literature to make concrete recommendations for HAR inference. We derive truncation parameter rules that choose a point on the size-power tradeoff to minimize a loss function. If Newey-West tests are used, we recommend the truncation parameter rule S = 1.3T1/2 and (nonstandard) fixed-b critical values. For tests of a single restriction, we find advantages to using the equal-weighted cosine (EWC) test, where the long run variance is estimated by projections onto Type II cosines, using ν = 0.4T2/3 cosine terms; for this test, fixed-b critical values are, conveniently, tν or F. We assess these rules using first an ARMA/GARCH Monte Carlo design, then a dynamic factor model design estimated using a 207 quarterly U.S. macroeconomic time series.},
number = {4},
urldate = {2021-05-17},
journal = {Journal of Business \& Economic Statistics},
author = {Lazarus, Eben and Lewis, Daniel J. and Stock, James H. and Watson, Mark W.},
month = oct,
year = {2018},
keywords = {HAC, Heteroscedasticity- and autocorrelation-robust estimation, Long-run variance.},
pages = {541--559},
file = {Full Text PDF:C\:\\Users\\z9292540\\Zotero\\storage\\E7MLJFAI\\Lazarus et al. - 2018 - HAR Inference Recommendations for Practice.pdf:application/pdf;Snapshot:C\:\\Users\\z9292540\\Zotero\\storage\\LZLZUVCE\\07350015.2018.html:text/html},
}