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Many analyses of observational data are attempts to emulate a target trial. The emulation of the target trial may fail when researchers deviate from simple principles that guide the design and analysis of randomized experiments. We review a framework to describe and prevent biases, including immortal time bias, that result from a failure to align start of follow-up, specification of eligibility, and treatment assignment. We review some analytic approaches to avoid these problems in comparative effectiveness or safety research.

INTRODUCTION Emerging preclinical evidence suggests that semaglutide, a glucagon‐like peptide receptor agonist (GLP‐1RA) for type 2 diabetes mellitus (T2DM) and obesity, protects against neurodegeneration and neuroinflammation. However, real‐world evidence for its ability to protect against Alzheimer's disease (AD) is lacking. METHODS We conducted emulation target trials based on a nationwide database of electronic health records (EHRs) of 116 million US patients. Seven target trials were emulated among 1,094,761 eligible patients with T2DM who had no prior AD diagnosis by comparing semaglutide with seven other antidiabetic medications. First‐ever diagnosis of AD occurred within a 3‐year follow‐up period and was examined using Cox proportional hazards and Kaplan–Meier survival analyses. RESULTS Semaglutide was associated with significantly reduced risk for first‐time AD diagnosis, most strongly compared with insulin (hazard ratio [HR], 0.33 [95% CI: 0.21 to 0.51]) and most weakly compared with other GLP‐1RAs (HR, 0.59 [95% CI: 0.37 to 0.95]). Similar results were seen across obesity status, gender, and age groups. DISCUSSION These findings support further studies to assess semaglutide's potential in preventing AD. Highlights Semaglutide was associated with 40% to 70% reduced risks of first‐time AD diagnosis in T2DM patients compared to other antidiabetic medications, including other GLP‐1RAs. Semaglutide was associated with significantly lower AD‐related medication prescriptions. Similar reductions were seen across obesity status, gender, and age groups. Our findings provide real‐world evidence supporting the potential clinical benefits of semaglutide in mitigating AD initiation and development in patients with T2DM. These findings support further clinical trials to assess semaglutide's potential in delaying or preventing AD.

Comparisons between randomized trial analyses and observational analyses that attempt to address similar research questions have generated many controversies in epidemiology and the social sciences. There has been little consensus on when such comparisons are reasonable, what their implications are for the validity of observational analyses, or whether trial and observational analyses can be integrated to address effectiveness questions. Here, we consider methods for using observational analyses to complement trial analyses when assessing treatment effectiveness. First, we review the framework for designing observational analyses that emulate target trials and present an evidence map of its recent applications. We then review approaches for estimating the average treatment effect in the target population underlying the emulation, using observational analyses of the emulation data alone and using transportability analyses to extend inferences from a trial to the target population. We explain how comparing treatment effect estimates from the emulation against those from the trial can provide evidence on whether observational analyses can be trusted to deliver valid estimates of effectiveness—a process we refer to as benchmarking—and, in some cases, allow the joint analysis of the trial and observational data. We illustrate different approaches using a simplified example of a pragmatic trial and its emulation in registry data. We conclude that synthesizing trial and observational data—in transportability, benchmarking, or joint analyses—can leverage their complementary strengths to enhance learning about comparative effectiveness, through a process combining quantitative methods and epidemiologic judgments.

The exclusion of pregnant women and infants from many randomized controlled trials (RCTs) has left critical gaps in medication safety, complicating clinical decision-making during these sensitive life stages. This commentary explores target trial emulation using real-world data as a robust alternative for advancing medication safety research when RCTs are not feasible.OBJECTIVESThe exclusion of pregnant women and infants from many randomized controlled trials (RCTs) has left critical gaps in medication safety, complicating clinical decision-making during these sensitive life stages. This commentary explores target trial emulation using real-world data as a robust alternative for advancing medication safety research when RCTs are not feasible.Target trial emulation replicates the design principles of RCTs within observational data, accounting for the dynamic nature of medication exposure across gestational stages and adjusting for time-varying confounders. While challenges such as unmeasured confounding, selection bias, and violations of positivity assumptions remain, this method provides crucial insights to address current evidence gaps.METHODSTarget trial emulation replicates the design principles of RCTs within observational data, accounting for the dynamic nature of medication exposure across gestational stages and adjusting for time-varying confounders. While challenges such as unmeasured confounding, selection bias, and violations of positivity assumptions remain, this method provides crucial insights to address current evidence gaps.Information on medication exposure effects will be obtained, which will inform safer medication guidelines in pregnancy and infancy. Future research integrating artificial intelligence-driven tools, open science practices, and robust data governance frameworks will further strengthen the reliability and impact of target trial emulation. Multinational collaboration and data sharing across diverse sources will accelerate the generation of evidence, ultimately advancing medication safety.RESULTSInformation on medication exposure effects will be obtained, which will inform safer medication guidelines in pregnancy and infancy. Future research integrating artificial intelligence-driven tools, open science practices, and robust data governance frameworks will further strengthen the reliability and impact of target trial emulation. Multinational collaboration and data sharing across diverse sources will accelerate the generation of evidence, ultimately advancing medication safety.Target trial emulation, leveraging real-world data, is a promising alternative when traditional clinical trials are not feasible. This approach promotes safer medication use and improves health outcomes for mothers and infants.CONCLUSIONTarget trial emulation, leveraging real-world data, is a promising alternative when traditional clinical trials are not feasible. This approach promotes safer medication use and improves health outcomes for mothers and infants.Many clinical trials exclude pregnant women and infants, leaving critical gaps in understanding medication safety during pregnancy and early life. Target trial emulation, which applies clinical trial principles to real-world data, offers a promising alternative when traditional trials are not feasible. This method allows researchers to study how medications affect pregnant women and babies at different stages of pregnancy while also considering factors that change over time. While there are still challenges, like unmeasured factors and bias remain, target trial emulation helps fill these knowledge gaps. Future advancements, including AI, Open Science, enhanced data sharing, and international collaboration, can further enhance this method's ability to improve the safety of medications for mothers and infants worldwide.PLAIN LANGUAGE SUMMARYMany clinical trials exclude pregnant women and infants, leaving critical gaps in understanding medication safety during pregnancy and early life. Target trial emulation, which applies clinical trial principles to real-world data, offers a promising alternative when traditional trials are not feasible. This method allows researchers to study how medications affect pregnant women and babies at different stages of pregnancy while also considering factors that change over time. While there are still challenges, like unmeasured factors and bias remain, target trial emulation helps fill these knowledge gaps. Future advancements, including AI, Open Science, enhanced data sharing, and international collaboration, can further enhance this method's ability to improve the safety of medications for mothers and infants worldwide.

Target trial emulation (TTE) is a powerful framework for addressing causal questions using observational data. By explicitly designing analyses to mimic a hypothetical randomized trial, TTE enables researchers to more precisely define their research questions, leading to more clinically meaningful conclusions. Its forward-looking design also helps limit common biases, such as immortal time bias and selection bias. Understanding TTE principles is essential not only for researchers working with observational data but also for clinicians who aim to critically interpret the growing number of TTE studies, as well as studies addressing causal questions without explicit use of the TTE framework. In this review, using the timing of switch from controlled to assisted ventilation as a key example, we outline the core assumptions underpinning valid causal inference in TTE: consistency, conditional exchangeability, and positivity. We discuss practical challenges in dynamic critical care settings, including defining a meaningful time zero, handling grace periods, and selecting and properly adjusting for confounders. We also discuss caveats, such as TTE’s applicability to non-modifiable interventions, limited applicability for intention-to-treat effects, and the need for high-resolution longitudinal data. Finally, we provide a visual summary linking each trial component to key indicators of high-quality emulation.

We aim to investigate the implementation of Target Trial Emulation (TTE) for causal inference, involving research topics, frequently used strategies, and issues indicating the need for future improvements. We performed a scoping review by following the Joanna Briggs Institute (JBI) guidance and Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist. A health research–focused librarian searched multiple medical databases, and two independent reviewers completed screening and extraction within covidence review management software. Our search resulted in 1,240 papers, of which 96 papers were eligible for data extraction. Results show a significant increase in the use of TTE in 2018 and 2021. The study topics varied and focused primarily on cancer, cardiovascular and cerebrovascular diseases, and infectious diseases. However, not all papers specified well all three critical components for generating robust causal evidence: time-zero, random assignment simulation, and comparison strategy. Some common issues were observed from retrieved papers, and key limitations include residual confounding, limited generalizability, and a lack of reporting guidance that need to be improved. Uneven adherence to the TTE framework exists, and future improvements are needed to progress applications using causal inference with observational data. •Target trial emulation (TTE) was used in various observational studies, with the most frequent topic being cancer research (22.9%), followed by cardiovascular disease (15.6%).•Time-zero, random assignment emulation, and contrast strategies are critical for guaranteeing the quality of causal evidence generated from a TTE study, but not all papers using the TTE framework identified all three elements.•The unavailability of variables, which contributed to residual confounding, was the most frequently mentioned limitation of the research applying TTE to observational data.•There is no reporting guidance for TTE research, which poses a challenge in ensuring the implementation quality of TTE and the resulting causal evidence.

Background When conducting randomised controlled trials is impractical, an alternative is to carry out an observational study. However, making valid causal inferences from observational data is challenging because of the risk of several statistical biases. In 2016 Hernán and Robins put forward the ‘target trial framework’ as a guide to best design and analyse observational studies whilst preventing the most common biases. This framework consists of (1) clearly defining a causal question about an intervention, (2) specifying the protocol of the hypothetical trial, and (3) explaining how the observational data will be used to emulate it. Methods The aim of this scoping review was to identify and review all explicit attempts of trial emulation studies across all medical fields. Embase, Medline and Web of Science were searched for trial emulation studies published in English from database inception to February 25, 2021. The following information was extracted from studies that were deemed eligible for review: the subject area, the type of observational data that they leveraged, and the statistical methods they used to address the following biases: (A) confounding bias, (B) immortal time bias, and (C) selection bias. Results The search resulted in 617 studies, 38 of which we deemed eligible for review. Of those 38 studies, most focused on cardiology, infectious diseases or oncology and the majority used electronic health records/electronic medical records data and cohort studies data. Different statistical methods were used to address confounding at baseline and selection bias, predominantly conditioning on the confounders ( N  = 18/49, 37%) and inverse probability of censoring weighting ( N  = 7/20, 35%) respectively. Different approaches were used to address immortal time bias, assigning individuals to treatment strategies at start of follow-up based on their data available at that specific time ( N  = 21, 55%), using the sequential trial emulations approach ( N  = 11, 29%) or the cloning approach ( N  = 6, 16%). Conclusion Different methods can be leveraged to address (A) confounding bias, (B) immortal time bias, and (C) selection bias. When working with observational data, and if possible, the ‘target trial’ framework should be used as it provides a structured conceptual approach to observational research.

\"Target trial emulation\" has been proposed as an observational method to answer comparative effectiveness questions, but it has rarely been attempted concurrently with a randomized clinical trial (RCT). We tested the hypothesis that blinded analysts applying target trial emulation to existing observational data could predict the results of an RCT. PreVent (Preventing Hypoxemia with Manual Ventilation during Endotracheal Intubation) was a multicenter RCT examining the effects of positive-pressure ventilation during tracheal intubation on oxygen saturation and severe hypoxemia. Analysts unaware of PreVent's results used patient-level data from three previous trials evaluating airway management interventions to emulate PreVent's eligibility criteria, randomization procedure, and statistical analysis. After PreVent's release, results of this blinded observational analysis were compared with those of the RCT. Difference-in-differences estimates for comparison of treatment effects between the observational analysis and the PreVent trial are reported on the absolute scale. Using observational data, we were able to emulate PreVent's randomization procedure to produce balanced groups for comparison. The lowest oxygen saturation during intubation was higher in the positive-pressure ventilation group than the no positive-pressure ventilation group in the observational analysis (  = 360; mean difference = 1.8%; 95% confidence interval [CI] = -1.0 to 4.6) and in the PreVent trial (  = 401; mean difference = 3.9%; 95% CI = 1.4 to 6.4), though the observational analysis could not exclude no difference. Difference-in-differences estimates comparing treatment effects showed reasonable agreement for lowest oxygen saturation between the observational analysis and the PreVent trial (mean difference = -2.1%; 95% CI = -5.9 to 1.7). Positive-pressure ventilation resulted in lower rates of severe hypoxemia in both the observational analysis (risk ratio = 0.60; 95% CI = 0.38 to 0.93) and in the PreVent trial (risk ratio = 0.48; 95% CI = 0.30 to 0.77). The absolute reduction in the incidence of severe hypoxemia with positive-pressure ventilation was similar in the observational analysis (9.4%) and the PreVent trial (12.0%), though the difference between these estimates had wide CIs (mean difference = 2.5%; 95% CI = -8.0 to 13.6%). Applying target trial emulation methods to existing observational data for the evaluation of a novel intervention produced results similar to those of a randomized trial. These findings support the use of target trial emulation for comparative effectiveness research.

Observational analyses for causal inference often rely on real world data collected for purposes other than research. A frequent goal of these observational analyses is to use the data to emulate a hypothetical randomized experiment, i.e., the target trial, that mimics the design features of a true experiment, including a clear definition of time zero with synchronization of treatment assignment and determination of eligibility. We review a recent observational analysis that explicitly emulated a target trial of screening colonoscopy using insurance claims from U.S. Medicare. We then compare this explicit emulation with alternative, simpler observational analyses that do not synchronize treatment assignment and eligibility determination at time zero and/or do not allow for repeated eligibility. This empirical comparison suggests that lack of an explicit emulation of the target trial leads to biased estimates, and shows that allowing for repeated eligibility increases the statistical efficiency of the estimates.