Evaluation of statistical methods used in the analysis of interrupted time series studies: a simulation study.
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ABSTRACT: Background
Interrupted time series (ITS) studies are frequently used to evaluate the effects of population-level interventions or exposures. However, examination of the performance of statistical methods for this design has received relatively little attention.Methods
We simulated continuous 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 lag-1 autocorrelation. We also examined the performance of the Durbin-Watson (DW) test for detecting autocorrelation.Results
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.Conclusions
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.
SUBMITTER: Turner SL
PROVIDER: S-EPMC8403376 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description: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.
| S-EPMC10946504 | biostudies-literature
Project description:BackgroundThe Interrupted Time Series (ITS) is a quasi-experimental design commonly used in public health to evaluate the impact of interventions or exposures. Multiple statistical methods are available to analyse data from ITS studies, but no empirical investigation has examined how the different methods compare when applied to real-world datasets.MethodsA random sample of 200 ITS studies identified in a previous methods review were included. Time series data from each of these studies was sought. Each dataset was re-analysed using six statistical methods. Point and confidence interval estimates for level and slope changes, standard errors, p-values and estimates of autocorrelation were compared between methods.ResultsFrom the 200 ITS studies, including 230 time series, 190 datasets were obtained. We found that the choice of statistical method can importantly affect the level and slope change point estimates, their standard errors, width of confidence intervals and p-values. Statistical significance (categorised at the 5% level) often differed across the pairwise comparisons of methods, ranging from 4 to 25% disagreement. Estimates of autocorrelation differed depending on the method used and the length of the series.ConclusionsThe choice of statistical method in ITS studies can lead to substantially different conclusions about the impact of the interruption. Pre-specification of the statistical method is encouraged, and naive conclusions based on statistical significance should be avoided.
| S-EPMC8235830 | biostudies-literature
Project description:BackgroundThe Interrupted Time Series (ITS) is a robust design for evaluating public health and policy interventions or exposures when randomisation may be infeasible. Several statistical methods are available for the analysis and meta-analysis of ITS studies. We sought to empirically compare available methods when applied to real-world ITS data.MethodsWe sourced ITS data from published meta-analyses to create an online data repository. Each dataset was re-analysed using two ITS estimation methods. The level- and slope-change effect estimates (and standard errors) were calculated and combined using fixed-effect and four random-effects meta-analysis methods. We examined differences in meta-analytic level- and slope-change estimates, their 95% confidence intervals, p-values, and estimates of heterogeneity across the statistical methods.ResultsOf 40 eligible meta-analyses, data from 17 meta-analyses including 282 ITS studies were obtained (predominantly investigating the effects of public health interruptions (88%)) and analysed. We found that on average, the meta-analytic effect estimates, their standard errors and between-study variances were not sensitive to meta-analysis method choice, irrespective of the ITS analysis method. However, across ITS analysis methods, for any given meta-analysis, there could be small to moderate differences in meta-analytic effect estimates, and important differences in the meta-analytic standard errors. Furthermore, the confidence interval widths and p-values for the meta-analytic effect estimates varied depending on the choice of confidence interval method and ITS analysis method.ConclusionsOur empirical study showed that meta-analysis effect estimates, their standard errors, confidence interval widths and p-values can be affected by statistical method choice. These differences may importantly impact interpretations and conclusions of a meta-analysis and suggest that the statistical methods are not interchangeable in practice.
| S-EPMC10858609 | biostudies-literature
Project description:INTRODUCTION:An interrupted time series (ITS) design is an important observational design used to examine the effects of an intervention or exposure. This design has particular utility in public health where it may be impracticable or infeasible to use a randomised trial to evaluate health system-wide policies, or examine the impact of exposures (such as earthquakes). There have been relatively few studies examining the design characteristics and statistical methods used to analyse ITS designs. Further, there is a lack of guidance to inform the design and analysis of ITS studies.This is the first study in a larger project that aims to provide tools and guidance for researchers in the design and analysis of ITS studies. The objectives of this study are to (1) examine and report the design characteristics and statistical methods used in a random sample of contemporary ITS studies examining public health interventions or exposures that impact on health-related outcomes, and (2) create a repository of time series data extracted from ITS studies. Results from this study will inform the remainder of the project which will investigate the performance of a range of commonly used statistical methods, and create a repository of input parameters required for sample size calculation. METHODS AND ANALYSIS:We will collate 200 ITS studies evaluating public health interventions or the impact of exposures. ITS studies will be identified from a search of the bibliometric database PubMed between the years 2013 and 2017, combined with stratified random sampling. From eligible studies, we will extract study characteristics, details of the statistical models and estimation methods, effect metrics and parameter estimates. Further, we will extract the time series data when available. We will use systematic review methods in the screening, application of inclusion and exclusion criteria, and extraction of data. Descriptive statistics will be used to summarise the data. ETHICS AND DISSEMINATION:Ethics approval is not required since information will only be extracted from published studies. Dissemination of the results will be through peer-reviewed publications and presentations at conferences. A repository of data extracted from the published ITS studies will be made publicly available.
| S-EPMC6352832 | biostudies-literature
Project description:Simulation studies are computer experiments that involve creating data by pseudo-random sampling. A key strength of simulation studies is the ability to understand the behavior of statistical methods because some "truth" (usually some parameter/s of interest) is known from the process of generating the data. This allows us to consider properties of methods, such as bias. While widely used, simulation studies are often poorly designed, analyzed, and reported. This tutorial outlines the rationale for using simulation studies and offers guidance for design, execution, analysis, reporting, and presentation. In particular, this tutorial provides a structured approach for planning and reporting simulation studies, which involves defining aims, data-generating mechanisms, estimands, methods, and performance measures ("ADEMP"); coherent terminology for simulation studies; guidance on coding simulation studies; a critical discussion of key performance measures and their estimation; guidance on structuring tabular and graphical presentation of results; and new graphical presentations. With a view to describing recent practice, we review 100 articles taken from Volume 34 of Statistics in Medicine, which included at least one simulation study and identify areas for improvement.
| S-EPMC6492164 | biostudies-literature
Project description:BackgroundComparing metabolic profiles under different biological perturbations has become a powerful approach to investigating the functioning of cells. The profiles can be taken as single snapshots of a system, but more information is gained if they are measured longitudinally over time. The results are short time series consisting of relatively sparse data that cannot be analyzed effectively with standard time series techniques, such as autocorrelation and frequency domain methods. In this work, we study longitudinal time series profiles of glucose consumption in the yeast Saccharomyces cerevisiae under different temperatures and preconditioning regimens, which we obtained with methods of in vivo nuclear magnetic resonance (NMR) spectroscopy. For the statistical analysis we first fit several nonlinear mixed effect regression models to the longitudinal profiles and then used an ANOVA likelihood ratio method in order to test for significant differences between the profiles.ResultsThe proposed methods are capable of distinguishing metabolic time trends resulting from different treatments and associate significance levels to these differences. Among several nonlinear mixed-effects regression models tested, a three-parameter logistic function represents the data with highest accuracy. ANOVA and likelihood ratio tests suggest that there are significant differences between the glucose consumption rate profiles for cells that had been--or had not been--preconditioned by heat during growth. Furthermore, pair-wise t-tests reveal significant differences in the longitudinal profiles for glucose consumption rates between optimal conditions and heat stress, optimal and recovery conditions, and heat stress and recovery conditions (p-values <0.0001).ConclusionWe have developed a nonlinear mixed effects model that is appropriate for the analysis of sparse metabolic and physiological time profiles. The model permits sound statistical inference procedures, based on ANOVA likelihood ratio tests, for testing the significance of differences between short time course data under different biological perturbations.
| S-EPMC3114728 | biostudies-literature
Project description:BackgroundOperative vaginal birth is a common procedure used to expedite birth after full cervical dilatation where there is a clinical need to do so (15% of births in the UK in 2016). The acquisition of skills for operative vaginal birth is dependent on the exposure of junior obstetricians to situations in which they can undertake directly supervised learning from senior accouchers. The Royal College of Obstetricians and Gynaecologists has recently introduced the first structured course in operative vaginal birth. To date, there have been no attempts to determine the clinical impact of a structured training package for operative vaginal birth.MethodsThe STROBE study is a quasi-experimental before-after interrupted time-series study of the effect of simulation training in operative vaginal birth for obstetricians on clinical outcomes of women and babies following operative vaginal birth. Similar to a stepped-wedge design, the intervention will be gradually implemented in all participating units but at different time periods. The primary outcome is failed operative vaginal birth with the first intended instrument. Secondary maternal outcomes are; use of second instrument to achieve operative vaginal birth, caesarean section, episiotomy, perineal trauma (1st, 2nd, 3rd, 4th degree tear), cervical tear requiring suturing, general anaesthesia and estimated blood loss. Secondary neonatal outcomes are; Apgar score at one, five, and ten minutes, Umbilical artery pH, shoulder dystocia, admission to Neonatal Intensive Care Unit and death within 28 days of birth. The analysis will be intention-to-treat (per unit) on the primary and secondary outcomes. The STROBE study received approval from the Health Research Authority and is sponsored by North Bristol NHS Trust. Results will be published in an open-access peer-reviewed medical journal within one year of completion of data gathering.DiscussionThe STROBE study will help establish our understanding of the effectiveness of locally-delivered simulation training for operative vaginal birth. Robust evidence supporting the effectiveness of such an approach would add weight to the argument supporting regular, local training for junior obstetricians in operative vaginal birth.Trial registrationISRCTN11760611 05/03/2018 (retrospectively registered).
| S-EPMC6444670 | biostudies-literature
Project description:BackgroundA classic methodology used in evaluating the impact of health policy interventions is interrupted time-series (ITS) analysis, applying a quasi-experimental design that uses both pre- and post-policy data without randomization. In this paper, we took a simulation-based approach to estimating intervention effects under different assumptions.MethodsEach of the simulated mortality rates contained a linear time trend, seasonality, autoregressive, and moving-average terms. The simulations of the policy effects involved three scenarios: 1) immediate-level change only, 2) immediate-level and slope change, and 3) lagged-level and slope change. The estimated effects and biases of these effects were examined via three matched generalized additive mixed models, each of which used two different approaches: 1) effects based on estimated coefficients (estimated approach), and 2) effects based on predictions from models (predicted approach). The robustness of these two approaches was further investigated assuming misspecification of the models.ResultsWhen one simulated dataset was analyzed with the matched model, the two analytical approaches produced similar estimates. However, when the models were misspecified, the number of deaths prevented, estimated using the predicted vs. estimated approaches, were very different, with the predicted approach yielding estimates closer to the real effect. The discrepancy was larger when the policy was applied early in the time-series.ConclusionEven when the sample size appears to be large enough, one should still be cautious when conducting ITS analyses, since the power also depends on when in the series the intervention occurs. In addition, the intervention lagged effect needs to be fully considered at the study design stage (i.e., when developing the models).
| S-EPMC9429656 | biostudies-literature
Project description:BACKGROUND:When randomisation is not possible, interrupted time series (ITS) design has increasingly been advocated as a more robust design to evaluating health system quality improvement (QI) interventions given its ability to control for common biases in healthcare QI. However, there is a potential risk of producing misleading results when this rather robust design is not used appropriately. We performed a methodological systematic review of the literature to investigate the extent to which the use of ITS has followed best practice standards and recommendations in the evaluation of QI interventions. METHODS:We searched multiple databases from inception to June 2018 to identify QI intervention studies that were evaluated using ITS. There was no restriction on date, language and participants. Data were synthesised narratively using appropriate descriptive statistics. The risk of bias for ITS studies was assessed using the Cochrane Effective Practice and Organisation of Care standard criteria. The systematic review protocol was registered in PROSPERO (registration number: CRD42018094427). RESULTS:Of 4061 potential studies and 2028 unique records screened for inclusion, 120 eligible studies assessed eight QI strategies and were from 25 countries. Most studies were published since 2010 (86.7%), reported data using monthly interval (71.4%), used ITS without a control (81%) and modelled data using segmented regression (62.5%). Autocorrelation was considered in 55% of studies, seasonality in 20.8% and non-stationarity in 8.3%. Only 49.2% of studies specified the ITS impact model. The risk of bias was high or very high in 72.5% of included studies and did not change significantly over time. CONCLUSIONS:The use of ITS in the evaluation of health system QI interventions has increased considerably over the past decade. However, variations in methodological considerations and reporting of ITS in QI remain a concern, warranting a need to develop and reinforce formal reporting guidelines to improve its application in the evaluation of health system QI interventions.
| S-EPMC7559052 | biostudies-literature
Project description:This study aimed to assess the impact of the Zika epidemic on the registration of birth defects in Brazil. We used an interrupted time series analysis design to identify changes in the trends in the registration of congenital anomalies. We obtained monthly data from Brazilian Live Birth Information System and used two outcome definitions: 1) rate of congenital malformation of the brain and eye (likely to be affected by Zika and its complications) 2) rate of congenital malformation not related to the brain or eye unlikely to be causally affected by Zika. The period between maternal infection with Zika and diagnosis of congenital abnormality attributable to the infection is around six months. We therefore used September 2015 as the interruption point in the time series, six months following March 2015 when cases of Zika started to increase. For the purposes of this analysis, we considered the period from January 2010 to September 2015 to be "pre-Zika event," and the period from just after September 2015 to December 2017 to be "post-Zika event." We found that immediately after the interruption point, there was a great increase in the notification rate of congenital anomalies of 14.9/10,000 live births in the brain and eye group and of 5.2/10,000 live births in the group not related with brain or eye malformations. This increase in reporting was in all regions of the country (except in the South) and especially in the Northeast. In the period "post-Zika event", unlike the brain and eye group which showed a monthly decrease, the group without brain or eye malformations showed a slow but significant increase (relative to the pre-Zika trend) of 0.2/10,000 live births. These findings suggest an overall improvement in the registration of birth malformations, including malformations that were not attributed to Zika, during and after the Zika epidemic.
| S-EPMC6776346 | biostudies-literature