Transparent reporting of observational studies emulating a target trial: the TARGET Statement
Bibliographic record
Abstract
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 two round online survey (August 2023 to March 2024; 18 expert participants from six countries) to assess the importance of candidate items selected from previous research and to identify additional items; (3) a three 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 six 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.767 | 0.897 |
| Meta-epidemiology (narrow) | 0.004 | 0.006 |
| Meta-epidemiology (broad) | 0.008 | 0.012 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.018 | 0.018 |
| Open science | 0.009 | 0.017 |
| Research integrity | 0.030 | 0.029 |
| Insufficient payload (model declined to judge) | 0.006 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".