Background: Interrupted time series analysis is increasingly used to evaluate the impact of large-scale health interventions. While segmented regression is a common approach, it is not always adequate, especially in the presence of seasonality and autocorrelation. An Autoregressive Integrated Moving Average (ARIMA) model is an alternative method that can accommodate these issues.Methods: We describe the underlying theory behind ARIMA models and how they can be used to evaluate population-level interventions, such as the introduction of health policies. We discuss how to select the shape of the impact, the model selection process, transfer functions, checking model fit, and interpretation of findings. We also provide R and SAS code to replicate our results.Results: We illustrate ARIMA modelling using the example of a policy intervention to reduce inappropriate prescribing. In January 2014, the Australian government eliminated prescription refills for the 25 mg tablet strength of quetiapine, an antipsychotic, to deter its prescribing for non-approved indications. We examine the impact of this policy intervention on dispensing of quetiapine using dispensing claims data.Conclusions: ARIMA modelling is a useful tool to evaluate the impact of large-scale interventions when other approaches are not suitable, as it can account for underlying trends, autocorrelation and seasonality and allows for flexible modelling of different types of impacts.

BACKGROUND: Sodium glucose cotransporter 2 inhibitors (SGLT2i), glucagon-like peptide-1 receptor agonists (GLP-1 RA), and the nonsteroidal mineralocorticoid receptor antagonist (ns-MRA) finerenone all individually reduce cardiovascular, kidney, and mortality outcomes in patients with type 2 diabetes and albuminuria. However, the lifetime benefits of combination therapy with these medicines are not known. METHODS: We used data from 2 SGLT2i trials (CANVAS [Canagliflozin Cardiovascular Assessment] and CREDENCE [Canagliflozin and Renal Events in Diabetes with Established Nephropathy Clinical Evaluation]), 2 ns-MRA trials (FIDELIO-DKD [Finerenone in Reducing Kidney Failure and Disease Progression in Diabetic Kidney Disease] and FIGARO-DKD [Efficacy and Safety of Finerenone in Subjects With Type 2 Diabetes Mellitus and the Clinical Diagnosis of Diabetic Kidney Disease]), and 8 GLP-1 RA trials to estimate the relative effects of combination therapy versus conventional care (renin-angiotensin system blockade and traditional risk factor control) on cardiovascular, kidney, and mortality outcomes. Using actuarial methods, we then estimated absolute risk reductions with combination SGLT2i, GLP-1 RA, and ns-MRA in patients with type 2 diabetes and at least moderately increased albuminuria (urinary albumin:creatinine ratio ≥30 mg/g) by applying estimated combination treatment effects to participants receiving conventional care in CANVAS and CREDENCE. RESULTS: Compared with conventional care, the combination of SGLT2i, GLP-1 RA, and ns-MRA was associated with a hazard ratio of 0.65 (95% CI, 0.55–0.76) for major adverse cardiovascular events (nonfatal myocardial infarction, nonfatal stroke, or cardiovascular death). The corresponding estimated absolute risk reduction over 3 years was 4.4% (95% CI, 3.0–5.7), with a number needed to treat of 23 (95% CI, 18–33). For a 50-year-old patient commencing combination therapy, estimated major adverse cardiovascular event–free survival was 21.1 years compared with 17.9 years for conventional care (3.2 years gained [95% CI, 2.1–4.3]). There were also projected gains in survival free from hospitalized heart failure (3.2 years [95% CI, 2.4–4.0]), chronic kidney disease progression (5.5 years [95% CI, 4.0–6.7]), cardiovascular death (2.2 years [95% CI, 1.2–3.0]), and all-cause death (2.4 years [95% CI, 1.4–3.4]). Attenuated but clinically relevant gains in event-free survival were observed in analyses assuming 50% additive effects of combination therapy, including for major adverse cardiovascular events (2.4 years [95% CI, 1.1–3.5]), chronic kidney disease progression (4.5 years [95% CI, 2.8–5.9]), and all-cause death (1.8 years [95% CI, 0.7–2.8]). CONCLUSIONS: In patients with type 2 diabetes and at least moderately increased albuminuria, combination treatment of SGLT2i, GLP-1 RA, and ns-MRA has the potential to afford relevant gains in cardiovascular and kidney event-free and overall survival.

The Pharmaceutical Benefits Scheme (PBS) is Australia’s national drug subsidy program. This paper provides a practical guide to researchers using PBS data to examine prescribed medicine use. Excerpts of the PBS data collection are available in a variety of formats. We describe the core components of four publicly available extracts (the Australian Statistics on Medicines, PBS statistics online, section 85 extract, under co-payment extract). We also detail common analytical challenges and key issues regarding the interpretation of utilisation using the PBS collection and its various extracts. Research using routinely collected data is increasing internationally. PBS data are a valuable resource for Australian pharmacoepidemiological and pharmaceutical policy research. A detailed knowledge of the PBS, the nuances of data capture, and the extracts available for research purposes are necessary to ensure robust methodology, interpretation, and translation of study findings into policy and practice.

Importance: When randomized trials are unavailable or not feasible, observational studies can be used to answer causal questions about the comparative effects of interventions by attempting to emulate a hypothetical pragmatic randomized trial (target trial). Published guidance to aid reporting of these studies is not available.Objective: To develop consensus-based guidance for reporting observational studies performed to estimate causal effects by explicitly emulating a target trial.Design, Setting, and Participants: The Transparent Reporting of Observational Studies Emulating a Target Trial (TARGET) guideline was developed using the Enhancing the Quality and Transparency of Health Research (EQUATOR) framework. The development included (1) a systematic review of reporting practices in published studies that had explicitly aimed to emulate a target trial; (2) a 2-round online survey (August 2023 to March 2024; 18 expert participants from 6 countries) to assess the importance of candidate items selected from previous research and to identify additional items; (3) a 3-day expert consensus meeting (June 2024; 18 panelists) to refine the scope of the guideline and draft the checklist; and (4) pilot of the draft checklist with stakeholders (n = 108; September 2024 to February 2025). The checklist was further refined based on feedback on successive drafts.Findings: The 21-item TARGET checklist is organized into 6 sections (abstract, introduction, methods, results, discussion, other information). TARGET provides guidance for reporting observational studies of interventions explicitly emulating a parallel group, individually randomized target trial, with adjustment for baseline confounders. Key recommendations are to (1) identify the study as an observational emulation of a target trial; (2) summarize the causal question and reason for emulating a target trial, (3) clearly specify the target trial protocol (ie, the causal estimand, identifying assumptions, data analysis plan) and how it was mapped to the observational data, and (4) report the estimate obtained for each causal estimand, its precision, and findings from additional analyses to assess the sensitivity of the estimates to assumptions, and design and analysis choices.Conclusions and Relevance: Application of the TARGET guideline recommendations aims to improve reporting transparency and peer review and help researchers, clinicians, and other readers interpret and apply the results.

There is wide variability in the use and adoption of recommendations generated by computerized clinical decision support systems (CDSSs) despite the

Computerised clinical decision support systems (CDSSs) are used widely to improve quality of care and patient outcomes. This systematic review evaluated the impact of CDSSs in targeting specific aspects of prescribing, namely initiating, monitoring and stopping therapy. We also examined the influence of clinical setting (institutional vs ambulatory care), system- or user-initiation of CDSS, multi-faceted vs stand alone CDSS interventions and clinical target on practice changes in line with the intent of the CDSS.