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Interrupted time series (ITS) studies are frequently used to examine the impact of population-level interventions or exposures. Systematic reviews with meta-analyses including ITS designs may inform public health and policy decision-making. Re-analysis of ITS may be required for inclusion in meta-analysis. While publications of ITS rarely provide raw data for re-analysis, graphs are often included, from which time series data can be digitally extracted. However, the accuracy of effect estimates calculated from data digitally extracted from ITS graphs is currently unknown. Forty-three ITS with available datasets and time series graphs were included. Time series data from each graph was extracted by four researchers using digital data extraction software. Data extraction errors were analysed. Segmented linear regression models were fitted to the extracted and provided datasets, from which estimates of immediate level and slope change (and associated statistics) were calculated and compared across the datasets. Although there were some data extraction errors of time points, primarily due to complications in the original graphs, they did not translate into important differences in estimates of interruption effects (and associated statistics). Using digital data extraction to obtain data from ITS graphs should be considered in reviews including ITS. Including these studies in meta-analyses, even with slight inaccuracy, is likely to outweigh the loss of information from non-inclusion.

Interrupted time series (ITS) are often meta-analysed to inform public health and policy decisions but examination of the statistical methods for ITS analysis and meta-analysis in this context is limited. We simulated meta-analyses of ITS studies with continuous outcome data, analysed the studies using segmented linear regression with two estimation methods [ordinary least squares (OLS) and restricted maximum likelihood (REML)], and meta-analysed the immediate level- and slope-change effect estimates using fixed-effect and (multiple) random-effects meta-analysis methods. Simulation design parameters included varying series length; magnitude of lag-1 autocorrelation; magnitude of level- and slope-changes; number of included studies; and, effect size heterogeneity. All meta-analysis methods yielded unbiased estimates of the interruption effects. All random effects meta-analysis methods yielded coverage close to the nominal level, irrespective of the ITS analysis method used and other design parameters. However, heterogeneity was frequently overestimated in scenarios where the ITS study standard errors were underestimated, which occurred for short series or when the ITS analysis method did not appropriately account for autocorrelation. The performance of meta-analysis methods depends on the design and analysis of the included ITS studies. Although all random effects methods performed well in terms of coverage, irrespective of the ITS analysis method, we recommend the use of effect estimates calculated from ITS methods that adjust for autocorrelation when possible. Doing so will likely to lead to more accurate estimates of the heterogeneity variance.

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.

To determine whether targeted sampling (TS), which omits biopsy of triple- normal lymph nodes (LNs) on positron emission tomography, computed tomography, and endobronchial ultrasound (EBUS), is noninferior to systematic sampling (SS) of mediastinal LNs during EBUS for staging of patients with early-stage non–small cell lung cancer (NSCLC). Patients who are clinical nodal (cN)0-N1 with suspected NSCLC eligible for EBUS based on positron emission tomography/computed tomography were enrolled in this prospective, multicenter trial. During EBUS, all patients underwent TS and then crossed over to SS, whereby at least 3 mediastinal LN stations (4R, 4L, 7) were routinely sampled. Gold standard of comparison was pathologic results. Based on the previous feasibility trial, a noninferiority margin of 6% was established for difference in missed nodal metastasis (MNM) incidence between TS and SS. The McNemar test on paired proportions was used to determine MNM incidence for each sampling method. Analysis was per-protocol using a level of significance of P <.05. Between November 2020 and April 2022, 91 patients were enrolled at 6 high-volume Canadian tertiary care centers. A total of 256 LNs underwent TS and SS. Incidence of MNM was 0.78% in SS and 2.34% in TS, with an absolute difference of 1.56% (95% confidence interval, −0.003% to 4.1%; P =.13). This falls within the noninferiority margin. A total of 6/256 LNs from 4 patients who were not sampled by TS were found to be malignant when sampled by SS. In high-volume thoracic endosonography centers, TS is not inferior to SS in nodal staging of early-stage NSCLC. This results in change of clinical management for a minority of patients. [Display omitted] [ABSTRACT FROM AUTHOR]