Reporting of Observational Studies Explicitly Aiming to Emulate Randomized Trials

Hansford, Harrison J.; Cashin, Aidan G.; Jones, Matthew D.; Swanson, Sonja A.; Islam, Nazrul; Douglas, Susan R. G.; Rizzo, Rodrigo R. N.; Devonshire, Jack J.; Williams, Sam A.; Dahabreh, Issa J.; Dickerman, Barbra A.; Egger, Matthias; Garcia-Albeniz, Xabier; Golub, Robert M.; Lodi, Sara; Moreno-Betancur, Margarita; Pearson, Sallie-Anne; Schneeweiss, Sebastian; Sterne, Jonathan A. C.; Sharp, Melissa K.; Stuart, Elizabeth A.; Hernán, Miguel A.; Lee, Hopin; McAuley, James H.

doi:10.1001/jamanetworkopen.2023.36023

Cited by 17 publications

(8 citation statements)

References 277 publications

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“…This risk of bias can never be eliminated. A crucial advantage of our study design, however, was that it followed recommended methods of target trial emulation in prespecifying the population eligibility criteria, time zero, treatment comparisons, outcomes, and analyses 418485. In our main analysis we used an instrumental variable to further reduce the risk of residual confounding.…”

Section: Discussionmentioning

confidence: 99%

“…A crucial advantage of our study design, however, was that it followed recommended methods of target trial emulation in prespecifying the population eligibility criteria, time zero, treatment comparisons, outcomes, and analyses. 41 84 85 In our main analysis we used an instrumental variable to further reduce the risk of residual confounding. We were therefore able to provide useful evidence about the comparative effectiveness of these three treatments as they were prescribed in routine clinical practice for a diverse population of people with type 2 diabetes mellitus.…”

Section: Discussionmentioning

confidence: 99%

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Comparative effectiveness of second line oral antidiabetic treatments among people with type 2 diabetes mellitus: emulation of a target trial using routinely collected health data

Bidulka,

Lugo-Palacios,

Carroll

et al. 2024

BMJ

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Objective To compare the effectiveness of three commonly prescribed oral antidiabetic drugs added to metformin for people with type 2 diabetes mellitus requiring second line treatment in routine clinical practice. Design Cohort study emulating a comparative effectiveness trial (target trial). Setting Linked primary care, hospital, and death data in England, 2015-21. Participants 75 739 adults with type 2 diabetes mellitus who initiated second line oral antidiabetic treatment with a sulfonylurea, DPP-4 inhibitor, or SGLT-2 inhibitor added to metformin. Main outcome measures Primary outcome was absolute change in glycated haemoglobin A 1c (HbA 1c ) between baseline and one year follow-up. Secondary outcomes were change in body mass index (BMI), systolic blood pressure, and estimated glomerular filtration rate (eGFR) at one year and two years, change in HbA 1c at two years, and time to ≥40% decline in eGFR, major adverse kidney event, hospital admission for heart failure, major adverse cardiovascular event (MACE), and all cause mortality. Instrumental variable analysis was used to reduce the risk of confounding due to unobserved baseline measures. Results 75 739 people initiated second line oral antidiabetic treatment with sulfonylureas (n=25 693, 33.9%), DPP-4 inhibitors (n=34 464 ,45.5%), or SGLT-2 inhibitors (n=15 582, 20.6%). SGLT-2 inhibitors were more effective than DPP-4 inhibitors or sulfonylureas in reducing mean HbA 1c values between baseline and one year. After the instrumental variable analysis, the mean differences in HbA 1c change between baseline and one year were −2.5 mmol/mol (95% confidence interval (CI) −3.7 to −1.3) for SGLT-2 inhibitors versus sulfonylureas and −3.2 mmol/mol (−4.6 to −1.8) for SGLT-2 inhibitors versus DPP-4 inhibitors. SGLT-2 inhibitors were more effective than sulfonylureas or DPP-4 inhibitors in reducing BMI and systolic blood pressure. For some secondary endpoints, evidence for SGLT-2 inhibitors being more effective was lacking—the hazard ratio for MACE, for example, was 0.99 (95% CI 0.61 to 1.62) versus sulfonylureas and 0.91 (0.51 to 1.63) versus DPP-4 inhibitors. SGLT-2 inhibitors had reduced hazards of hospital admission for heart failure compared with DPP-4 inhibitors (0.32, 0.12 to 0.90) and sulfonylureas (0.46, 0.20 to 1.05). The hazard ratio for a ≥40% decline in eGFR indicated a protective effect versus sulfonylureas (0.42, 0.22 to 0.82), with high uncertainty in the estimated hazard ratio versus DPP-4 inhibitors (0.64, 0.29 to 1.43). Conclusions This emulation study of a target trial found that SGLT-2 inhibitors were more effective than sulfonylureas or DPP-4 inhibitors in lowering mean HbA 1c , BMI, and systolic blood pressure and in reducing the hazards of hospital admission for heart failure ( v DPP-4 inhibitors) and kidney disease progression ( v sulfonylureas), with no evidence of differences in other clinical endpoints.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Comparative effectiveness of second line oral antidiabetic treatments among people with type 2 diabetes mellitus: emulation of a target trial using routinely collected health data

Bidulka,

Lugo-Palacios,

Carroll

et al. 2024

BMJ

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“…Adoption of the target trial framework has led to greater compatibility between historically conflicting results of RCTs and observational studies. 2 Yet, opinions remain divided on some aspects of design and analysis between these two study paradigms. The control of false positive or type I error rates is increasingly recognized as critical to RCTs with multiple treatment arms, outcomes, or subgroups.…”

Section: Main Textmentioning

confidence: 99%

“…Adoption of the target trial framework has led to greater compatibility between historically conflicting results of RCTs and observational studies. 2…”

Section: The Foundationmentioning

confidence: 99%

Statistical pitfalls of multiple exposures in causal observational studies and tools to address them

McIntyre,

Wiener,

Davies Smith

2024

Preprint

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The Table 2 Fallacy is an interpretation error commonly encountered in medical research. This error occurs when coefficient estimates in multivariable regression models, apart from that of the primary exposure, are interpreted as valid total effects on the outcome. Causal diagrams, notably directed acyclic graphics (DAGs), can be used to avoid this error. DAGs can be used to identify sets of covariates that, when adjusted for, allow for unbiased estimation and correct interpretation of the total effect for each primary exposure. However, the risk of a false positive finding, or the type I error rate, increases when inference is conducted for multiple associations of interest. While multiple comparison procedures are recognized as a critical feature of randomized controlled trial (RCT) design and analysis, opinions remain divided on their use within observational studies. Target trial emulation is an increasingly used framework that aligns the design and analysis of causal observational studies with that of RCTs. This article demonstrates that when multiple causal relationships are of primary interest to an observational study, causal diagrams can be used in conjunction with multiple comparison procedures to simultaneously avoid the Table 2 Fallacy and control the risk of spurious findings. The proposed framework should further align these two study types and assist in ensuring that results are reproducible. Methods are demonstrated by simulating a hypothetical causal mechanism using the dagR library.

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“…Causal inference from observational data can be seen as an attempt to emulate a pragmatic randomized trial—the target trial—that would answer the question of interest. The approach for emulating a target trial has 2 steps: 1) specify the protocol of the target trial, and 2) emulate the target trial using observational data and appropriate methods [ 10 – 13 ]. The target trial framework helps articulate a precise causal question, assists with discussions about trade-offs regarding study design, and facilitates comparisons with randomized trials that aim to ask the same question as the observational study.…”

Section: The Observational Analysismentioning

confidence: 99%

Prospective benchmarking of an observational analysis in the SWEDEHEART registry against the REDUCE-AMI randomized trial

Matthews,

Dahebreh,

MacDonald

et al. 2024

Eur J Epidemiol

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Prospective benchmarking of an observational analysis against a randomized trial increases confidence in the benchmarking process as it relies exclusively on aligning the protocol of the trial and the observational analysis, while the trials findings are unavailable. The Randomized Evaluation of Decreased Usage of Betablockers After Myocardial Infarction (REDUCE-AMI, ClinicalTrials.gov ID: NCT03278509) trial started recruitment in September 2017 and results are expected in 2024. REDUCE-AMI aimed to estimate the effect of long-term use of beta blockers on the risk of death and myocardial following a myocardial infarction with preserved left ventricular systolic ejection fraction. We specified the protocol of a target trial as similar as possible to that of REDUCE-AMI, then emulated the target trial using observational data from Swedish healthcare registries. Had everyone followed the treatment strategy as specified in the target trial protocol, the observational analysis estimated a reduction in the 5-year risk of death or myocardial infarction of 0.8 percentage points for beta blockers compared with no beta blockers; effects ranging from an absolute reduction of 4.5 percentage points to an increase of 2.8 percentage points in the risk of death or myocardial infarction were compatible with our data under conventional statistical criteria. Once results of REDUCE-AMI are published, we will compare the results of our observational analysis against those from the trial. If this prospective benchmarking is successful, it supports the credibility of additional analyses using these observational data, which can rapidly deliver answers to questions that could not be answered by the initial trial. If benchmarking proves unsuccessful, we will conduct a “postmortem” analysis to identify the reasons for the discrepancy. Prospective benchmarking shifts the investigator focus away from an endeavour to use observational data to obtain similar results as a completed randomized trial, to a systematic attempt to align the design and analysis of the trial and the observational analysis.

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Reporting of Observational Studies Explicitly Aiming to Emulate Randomized Trials

Cited by 17 publications

References 277 publications

Comparative effectiveness of second line oral antidiabetic treatments among people with type 2 diabetes mellitus: emulation of a target trial using routinely collected health data

Comparative effectiveness of second line oral antidiabetic treatments among people with type 2 diabetes mellitus: emulation of a target trial using routinely collected health data

Statistical pitfalls of multiple exposures in causal observational studies and tools to address them

Prospective benchmarking of an observational analysis in the SWEDEHEART registry against the REDUCE-AMI randomized trial

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