RITS: a toolbox for assessing complex interventions via interrupted time series models

Abstract

Background Various interacting and interdependent components comprise complex interventions. These components create difficulty in assessing the true impact of interventions designed to improve patient-centered outcomes. Interrupted time series (ITS) designs borrow from case-crossover designs and serve as quasi-experimental methodology able to retrospectively assess the impact of an intervention while accounting for temporal correlation. While ITS designs are aptly situated for studying the impacts of large-scale public health policies, existing ITS software implement rigid ITS methodology that often assume the pre- and post-intervention phases are fully differentiated (by a known change-point or set of time points) and do not allow for changes in both the mean functions and correlation structure. Results This article describes the Robust Interrupted Time Series (RITS) toolbox, a stand-alone user-friendly application researchers can use to implement flexible ITS models that estimate the lagged effect of an intervention on an outcome, level and trend changes, and post-intervention changes in the correlation structure, for single and multiple ITS. The RITS toolbox incorporates a formal test for the existence of a change in the outcome and estimates a change-point over a set of possible change-points defined by the researcher. In settings with multiple ITS, RITS provides a global over-all units change-point and allows for unit-specific changes in the mean functions and correlation structures. Conclusions The RITS toolbox is the first piece of software that allows researchers to use flexible ITS models that test for the existence of a change-point, estimate the change-point (if estimation is desired), and allow for changes in both the mean functions and correlation structures at the change point. RITS does not require any knowledge of a statistical (or otherwise) programming language, is freely available to the community, and may be downloaded and used on a local machine to ensure data protection.

Effect of an E-Prescription Intervention on the Adherence to Surgical Chemoprophylaxis Duration in Cardiac Surgery: A Single Centre Experience

“…All comparisons were two-sided, and the level of statistical significance was set at p < 0.05. For the statistical analysis IBM SPSS Statistics ® version 26 was used, except for the ITS analysis for which we have used the RITS toolbox [38].…”

Differences in inpatient performance of public general hospitals following implementation of a points-counting payment based on diagnosis-related group: a robust multiple interrupted time series study in Wenzhou, China

“…Interrupted time series has become a core study design for the evaluation of health policies 17. This study used a robust multiple interrupted time series (R-MITS) model, which has several advantages over the traditional designs of interrupted time series (ITS) 35–37. Above all, R-MITS borrows information from all microsystems to estimate the global change-point; that is, it determines the time point at which the effect of the intervention initiates for the entire health system, rather than removing some points as decided by the study team 36.…”

“…Above all, R-MITS borrows information from all microsystems to estimate the global change-point; that is, it determines the time point at which the effect of the intervention initiates for the entire health system, rather than removing some points as decided by the study team 36. An intervention to increase the stability and predictability of an outcome may have decreased outcome variability and increased autocorrelation 37. Given that decreasing outcome variability can contribute to less extreme values while increasing autocorrelation can contribute to a more predictable outcome, it is important to capture alongside mean differences in variability and autocorrelation.…”

Auswirkung der DRG-Abrechnung auf die Krankenhausverweildauer bei Patienten mit Epilepsie

“…Time-Series Analysis [TSA]) der Daten erfolgte mittels der frei verfügbaren RITS-Toolbox (Robust Interrupted Time Series Toolbox) der KAUST Biostatistics Research Group (King Abdullah University of Science and Technology, Thuwal, Saudi Arabien, https://biostatistics-kaust.github.io/robust_time_series_toolbox/ ) [ 5 ]. Eine Zeitreihenanalyse ist ein quasi-experimentelles Analyseverfahren, das entwickelt wurde, um Rückschlüsse auf die Wirksamkeit einer Intervention zu ziehen, wobei gleichzeitig die komplexe zeitliche Abhängigkeit innerhalb eines analysierten Systems berücksichtigt wird [ 3 – 5 ]. Bei einer unterbrochenen Zeitreihenanalyse (Interrupted Time Series Analysis [ITSA]) wird hierbei von einem Ereignispunkt ausgegangen, welcher dem System entweder vorgegeben werden kann oder näherungsweise durch die ITSA selbst bestimmt wird.…”

“…Die zeitliche Varianz der Trendwechsel ist sicherlich zu einem gewissen Maß durch die genutzte Methode und die Datenbasis zu erklären. Da die Fallzahlen nur jährlich und nicht monatlich oder sogar täglich vorliegen, können diese zu einer Verzerrung des Endergebnisses führen [ 3 – 5 ]. Andererseits wäre auch die prolongierte Umsetzung der DRG-Einführung in Deutschland eine weitere mögliche Erklärung.…”