Allen datagraph error code 24/15/2023 ![]() We then examine how often graphs are used to display data in reports of ITS studies and the extent to which those graphs meet our recommendations (Section 3). We first provide principles and recommendations for graphing ITS data (Section 2). The aim of this research is to provide recommendations for the accurate display of ITS data in primary ITS studies and systematic reviews of ITS studies. Such re‐analyses are particularly important when reviewers wish to meta‐analyze the estimated effects, where consistency in the effect measures across studies, and estimates and their standard errors from correct analyses of time series data are required. ) or, when the reviewer wishes to estimate the effect of the interruption using a different effect measure to that reported in the paper (eg, an estimate of level change may be reported but a reviewer may be interested in a difference in slopes or the combined effect of level and slope change). ) where the data has been incorrectly analyzed (eg, when there has been no adjustment for autocorrelation-a common complication with time series data A re‐analysis may be required in the circumstance where effect estimates have been incompletely reported (ie, when an effect estimate is reported without a measure of precision For these reasons, visual displays of ITS data in both primary studies, and systematic reviews of ITS studies, are a valuable part of reporting.Ī further benefit of visually displaying data from an ITS study is that it allows systematic reviewers to extract the data (eg, using digitizing software) and undertake a re‐analysis. These features can be visually displayed, and in well‐designed graphs, the impacts of the interruption on the outcome will likely be evident. Statistical comparisons between the counterfactual and observed data at different points post interruption can be used to estimate the short‐ and long‐term effects of the interruption. By modeling data from the pre‐interruption period, the underlying secular trend can be established and extrapolated to the post‐interruption period, creating a counterfactual for what would have occurred in the absence of the interruption. In an ITS study, data on a group of individuals (eg, hospital, country) are collected at multiple time points both before and after the interruption. The ITS design inherently lends itself to a visual display. However, effective and accurate presentation of the data from ITS studies is needed to enable their inclusion in systematic reviews (including meta‐analysis) and to aid interpretation of the results from the review. This makes ITS studies a valuable design for inclusion in systematic reviews intended to inform policy decisions. The interruption could be planned, such as the roll out of a new health policy, or unplanned, such as an unintended environmental exposure. Interrupted time series (ITS) studies are a common design used in areas such as public health, health policy and health services research to examine the effects of an interruption on an outcome.
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