# Target Trial Emulation Protocol and Report: [Short title of the study]

> Protocol and report template for an observational study that estimates
> the causal effect of an intervention by explicitly emulating a
> hypothetical randomised trial (the target trial). The report follows the
> 21 items of the TARGET guideline (Transparent Reporting of
> Observational Studies Emulating a Target Trial; Cashin et al., JAMA
> 2025;334(12):1084-1093 and BMJ 2025;390:e087179), organised in six
> sections; item numbers appear in square brackets, for example [6c].
> TARGET covers emulations of a parallel-group, individually randomised
> target trial with adjustment for baseline confounders; general
> observational checklists such as STROBE and RECORD do not capture these
> target trial items. Replace every `[bracketed]` value
> and every row marked *Example*. This is a document format, not
> clinical, epidemiological or statistical advice; involve a
> statistician or epidemiologist with causal inference experience.

<!--
Guidance: How to use this template
- Write section 4 (the target trial protocol) before you look at
  outcome data. Specify the trial you would run if you could, then
  decide how your data emulate each component. This order is what
  distinguishes target trial emulation from an ordinary cohort analysis.
- The single most important design choice is time zero: eligibility,
  treatment assignment and the start of follow-up must coincide.
  Misalignment creates immortal time and selection biases that no
  statistical adjustment can repair.
- Name the causal estimand (for example an intention-to-treat or
  per-protocol effect) before choosing the estimator.
- Keep the bracketed TARGET item numbers and fill the official checklist
  from section 9. Register the protocol (section 8.2) before analysis
  where possible.
- Rows marked *Example* describe a fictional study by the fictional
  Larkfield Health Data Collaborative comparing initiation of two
  glucose-lowering drug classes, called class A and class B, in adults
  with type 2 diabetes. All numbers are invented.
- Never put identifying data in this file. Follow your data use
  agreements, ethics approval and local privacy law.
-->

## Document control

<!-- Guidance: Treat the protocol and report as one controlled document. Freeze version 1.0 of section 4 before outcome data are analysed and record every later change in the revision history and in section 4.10. -->

| Field | Value |
|---|---|
| Document ID | [TTE-ACRONYM-001] |
| Causal question | [Effect of initiating X versus Y on outcome Z in population P] |
| Data source(s) | [Database names and years] |
| Protocol registration | [Registry ID / OSF DOI / "not registered"] |
| Ethics reference | [Committee and number, or exemption] |
| Version | [0.1] |
| Status | [Draft protocol / Protocol frozen / Analysis / Report drafted / Approved] |
| Owner | [@principal-investigator] |
| Approvers / reviewers | [@study-statistician], [@clinical-lead], [@data-custodian] |
| Last updated | YYYY-MM-DD |
| Classification | [Confidential until publication] |

### Approvals

| Role | Name | Decision | Date |
|---|---|---|---|
| Principal investigator | [@name] | [Protocol frozen] | YYYY-MM-DD |
| Study statistician | [@name] | [Analysis plan approved] | YYYY-MM-DD |
| Clinical lead | [@name] | [Treatment strategies approved] | YYYY-MM-DD |
| Data custodian | [@name] | [Aggregate results approved for release] | YYYY-MM-DD |

### Revision history

| Version | Date | Author | Change summary |
|---|---|---|---|
| 0.1 | YYYY-MM-DD | [@name] | First draft of target trial protocol |
| 1.0 | YYYY-MM-DD | [@name] | Protocol frozen before outcome analysis |
| 1.1 | YYYY-MM-DD | [@name] | *Example:* Permissible refill gap changed from 60 to 90 days after feasibility counts (no outcome data seen), see DEV-01 |
| 2.0 | YYYY-MM-DD | [@name] | Report drafted |

## Contents

<!-- Guidance: Keep this list in sync with the headings below. -->

1. [Title and abstract](#1-title-and-abstract)
2. [Introduction](#2-introduction)
3. [Data sources](#3-data-sources)
4. [Target trial protocol and emulation](#4-target-trial-protocol-and-emulation)
5. [Design-related biases](#5-design-related-biases)
6. [Results](#6-results)
7. [Discussion](#7-discussion)
8. [Other information](#8-other-information)
9. [TARGET traceability](#9-target-traceability)
10. [Glossary and references](#10-glossary-and-references)

## 1. Title and abstract

<!-- Guidance: Say in the title or abstract that the study is an observational emulation of a target trial, so readers and indexers do not mistake it for a randomised trial. The abstract names the data source and summarises methods, assumptions, results and conclusions. -->

The title and abstract identify the design and summarise the study.

**Title:** [Initiating X versus Y] and [outcome] in [population]: a
target trial emulation using [data source]

| Heading | Content | TARGET |
|---|---|---|
| Design | Observational study emulating a target trial | [1a] |
| Data source | [Name, country, years] | [1b] |
| Target trial and emulation | [Eligibility, strategies, time zero, outcome, estimand] | [1a] |
| Key assumptions | [No unmeasured baseline confounding, positivity, consistency] | [1c] |
| Results | *Example:* Among 31,381 eligible adults (12,406 initiating class A, 18,975 initiating class B), the weighted 5-year risk of hospitalisation for heart failure was 6.2% with class A and 7.4% with class B: risk difference -1.2 percentage points (95% CI -1.8 to -0.6), risk ratio 0.84 (0.76 to 0.92) | [1c] |
| Conclusions | [Interpretation proportionate to the assumptions] | [1c] |

## 2. Introduction

<!-- Guidance: Explain the evidence gap, state the causal question in one sentence and justify why an emulation (rather than a randomised trial) is appropriate. Cite any randomised trials that inform the design or provide a benchmark. -->

The introduction states what is unknown and why this emulation helps.

### 2.1 Background [2]

[Summarise what is known, the gap and why a causal answer matters for
decisions.]

### 2.2 Causal question [3]

| Element | Content |
|---|---|
| Population | *Example:* Adults aged 40 to 79 with type 2 diabetes on metformin who need a second-line drug |
| Strategies compared | Initiate class A versus initiate class B |
| Outcome | Hospitalisation for heart failure |
| Time horizon | 5 years |
| Effect of interest | [Intention-to-treat effect / per-protocol effect] |

### 2.3 Why emulate a target trial [4]

[Explain why a randomised trial is unavailable, unfeasible, too slow or
unethical, why the data are suitable for the emulation, and which trials
informed the design or could serve as a benchmark.]

*Example:* Head-to-head trials of class A versus class B are not
available for this population. A placebo-controlled trial of class A
reported a hazard ratio for heart failure hospitalisation that the team
will use to check whether the emulation of a similar comparison
reproduces the trial result (benchmarking, section 4.9).

## 3. Data sources

<!-- Guidance: Describe each data source well enough for readers to judge whether it can capture eligibility, treatment, outcomes and confounders. State why the data were originally collected, since this shapes their completeness. -->

This section describes where the data come from [5].

| Item | Detail |
|---|---|
| Source and purpose of collection | *Example:* Linked primary care records, hospital admissions and pharmacy dispensing collected for clinical care and reimbursement |
| Type of data | [Electronic health records / claims / registry / cohort study] |
| Location and setting | [Country, region, care setting] |
| Calendar period | [YYYY-MM-DD to YYYY-MM-DD] |
| Linkage | [Method, linkage rate, who performed it] |
| Coding systems | [Diagnosis, procedure and drug code systems with versions] |
| Validation of key variables | [References or local validation studies] |
| Data access | [Data custodian, approval reference] |

## 4. Target trial protocol and emulation

<!-- Guidance: This is the core of the study. For every component, describe the target trial first (column 2), then how the observational data emulate it (column 3). TARGET item 6 covers the target trial protocol and item 7 covers its emulation; the sub-items a to h follow the same eight components. Common pitfall: describing only the emulation, which hides the compromises made. -->

The table summarises the protocol of the hypothetical target trial and
its emulation. Sections 4.1 to 4.8 give the detail.

| Component | Target trial [6a to 6h] | Emulation with the data [7a to 7h] |
|---|---|---|
| Eligibility criteria | *Example:* Adults 40 to 79, type 2 diabetes, on metformin for 6 months or more, no prior use of class A or B, no heart failure | Same criteria applied from records at each eligible date; diagnosis and dispensing codes in appendix B |
| Treatment strategies | Initiate class A; initiate class B; continue unless contraindicated | Initiation defined by first dispensing; adherence from refill records |
| Assignment | Random, unblinded | Assumed exchangeable conditional on baseline confounders in 4.7 |
| Follow-up | From randomisation to outcome, death, loss or 5 years | From first dispensing (time zero) to outcome, death, deregistration or 5 years |
| Outcome | Hospitalisation for heart failure | First admission with a primary heart failure code, positive predictive value [x]% in validation |
| Causal contrasts | Intention-to-treat and per-protocol effects | Observational analogue of intention-to-treat; per-protocol by censoring at deviation plus weighting |
| Identifying assumptions | Randomisation ensures exchangeability | Conditional exchangeability, positivity, consistency, correct model specification |
| Analysis plan | Risk curves, 5-year risk difference and ratio | Pooled logistic regression with inverse probability weights, bootstrap CIs |

### 4.1 Eligibility criteria [6a, 7a]

| ID | Criterion | Target trial | How measured in the data | Look-back window |
|---|---|---|---|---|
| EL-01 | Age | 40 to 79 years | Date of birth | At time zero |
| EL-02 | Diagnosis | Type 2 diabetes | [Code list] | Any time before |
| EL-03 | Background therapy | Metformin for 6 months or more | Dispensing records | 6 months |
| EL-04 | Prior use of strategies | None | Dispensing records | *Example:* 12 months (new-user design) |
| EL-05 | Exclusion | Heart failure, dialysis | [Code lists] | Any time before |

All criteria must be assessable using information available on or
before time zero only.

### 4.2 Treatment strategies [6b, 7b]

| Strategy | Target trial definition | Data definition |
|---|---|---|
| S-A | Initiate class A at baseline, continue during follow-up unless a clinical reason to stop | First dispensing of class A; continuation defined by refills with gaps of no more than [90] days (the permissible gap) |
| S-B | Initiate class B at baseline, continue likewise | First dispensing of class B, same continuation rule |

State what is allowed during follow-up (add-on therapy, switching for
side effects) and how it is captured.

### 4.3 Assignment procedures [6c, 7c]

[In the target trial, individuals are randomly assigned to a strategy
at baseline. Describe how the emulation assigns each person to a
strategy, for example by the drug dispensed at time zero, and which
baseline variables are needed to make the groups exchangeable.]

### 4.4 Follow-up period [6d, 7d]

| Item | Target trial | Emulation |
|---|---|---|
| Start (time zero) | Randomisation | *Example:* Date of first dispensing, the date on which eligibility is confirmed and the strategy is assigned |
| End | Outcome, death, loss to follow-up, administrative end at 5 years | Same; loss defined as leaving the practice or end of data |
| Permissible gap or grace period (if used) | [Not needed / defined] | *Example:* A refill gap of more than 90 days counts as stopping; used only for the per-protocol analysis (EST-02). If a strategy allows initiation within a grace period after time zero, describe cloning, censoring and weighting here |
| Sequential trials (if used) | Not applicable | [Monthly eligible dates; a person can enter several trials; robust variance] |

### 4.5 Outcomes [6e, 7e]

| ID | Outcome | Target trial definition | Data definition | Ascertainment |
|---|---|---|---|---|
| OUT-01 | *Example:* Hospitalisation for heart failure | First hospital admission for heart failure | Primary diagnosis code [list] | Hospital episode linkage |
| OUT-02 | All-cause death | Death from any cause | Death registry | Registry linkage |
| OUT-03 | [Negative control outcome] | [Outcome not plausibly affected by treatment] | [Codes] | [Source] |

### 4.6 Causal contrasts and estimands [6f, 7f]

| ID | Estimand | Population | Contrast | Summary measure |
|---|---|---|---|---|
| EST-01 | Intention-to-treat effect | All eligible | Initiating S-A versus S-B, regardless of later adherence | 5-year risk difference and risk ratio |
| EST-02 | Per-protocol effect | All eligible | Initiating and adhering to S-A versus S-B | 5-year risk difference and risk ratio |

With $Y^{a}$ the potential outcome under strategy $a$, the 5-year risk
difference and risk ratio for EST-01 are:

$$
\text{RD} = \Pr\left(Y^{A}=1\right) - \Pr\left(Y^{B}=1\right), \qquad
\text{RR} = \frac{\Pr\left(Y^{A}=1\right)}{\Pr\left(Y^{B}=1\right)}
$$

### 4.7 Identifying assumptions [6g, 7g]

| Assumption | Meaning | How it is addressed |
|---|---|---|
| Conditional exchangeability | No unmeasured confounding given the baseline covariates | *Example:* Covariates chosen with a DAG: age, sex, HbA1c, eGFR, BMI, blood pressure, prior cardiovascular disease, statin use, deprivation, calendar year |
| Positivity | Every covariate pattern has people on both strategies | Inspect weights and propensity score overlap; trim or restrict if needed |
| Consistency | Strategies are well defined and the data version matches | Drug class defined by dispensing, dose ignored |
| No measurement error | Treatment, outcome and covariates correctly recorded | Validation references in section 3 |

```mermaid
flowchart LR
    L["Baseline confounders L (age, HbA1c, eGFR, CVD)"] --> A["Strategy at time zero (A or B)"]
    L --> Y["Heart failure hospitalisation"]
    A --> Y
    U["Unmeasured factors U (frailty)"] -.-> A
    U -.-> Y
```

The dashed arrows show the unmeasured confounding that the
exchangeability assumption rules out; section 4.9 tests how sensitive
the result is to it.

### 4.8 Statistical analysis plan [6h, 7h]

| Item | Method |
|---|---|
| Estimator for EST-01 | *Example:* Inverse probability of treatment weighting; weighted pooled logistic regression on months of follow-up to estimate standardised risk curves |
| Estimator for EST-02 | Censor at deviation from the assigned strategy; inverse probability of censoring weights using time-varying covariates |
| Confidence intervals | Nonparametric bootstrap, 500 samples |
| Missing data | [Multiple imputation of baseline covariates, 20 data sets; outcome assumed complete through linkage] |
| Weight diagnostics | Mean, range and 99th percentile of weights; standardised mean differences below 0.1 after weighting |
| Software | [R / Stata / SAS with versions and packages] |

The stabilised treatment weight for person $i$ with strategy $A_i$ and
baseline covariates $L_i$ is:

$$
SW_i = \frac{\Pr\left(A = A_i\right)}{\Pr\left(A = A_i \mid L = L_i\right)}
$$

### 4.9 Additional and sensitivity analyses [7h]

| ID | Analysis | Purpose |
|---|---|---|
| SA-01 | E-value for the main risk ratio | Strength of unmeasured confounding needed to explain away the estimate |
| SA-02 | Negative control outcome OUT-03 | Detect residual confounding |
| SA-03 | Permissible refill gap 60 versus 90 days | Sensitivity of EST-02 to the definition of stopping |
| SA-04 | Benchmark against the placebo-controlled trial result | Check that the emulation can reproduce a known trial finding |
| SA-05 | Complete-case analysis | Sensitivity to the missing data model |

### 4.10 Deviations from the frozen protocol

| ID | Date | Component | Change | Reason | Outcome data seen? |
|---|---|---|---|---|---|
| DEV-01 *(Example)* | YYYY-MM-DD | 4.2 and 4.4 permissible refill gap | 60 to 90 days | Feasibility counts (no outcomes) showed that many continuing users had a refill gap between 61 and 90 days | No |

## 5. Design-related biases

<!-- Guidance: Check the design against the common self-inflicted biases before analysis. Each one comes from a mismatch between eligibility, assignment and time zero. -->

Most serious biases in observational treatment studies come from the
design, not from confounding. Check each one below.

| Bias | How it arises | Prevention in this design |
|---|---|---|
| Immortal time bias | Follow-up starts before the strategy is assigned, so one group cannot have the outcome during a period | Time zero equals the date of assignment and eligibility |
| Prevalent user bias | Current users who survived early treatment are compared with new users | New-user design with a 12-month washout (EL-04) |
| Selection by post-baseline events | Eligibility depends on what happens after time zero, such as adherence | All criteria assessed on or before time zero |
| Time-varying confounding | Adherence depends on factors that also affect the outcome | Inverse probability of censoring weights for EST-02 |

The timeline shows correct alignment for one participant.

```mermaid
flowchart LR
    W["Look-back window (12 months): check eligibility"] --> T0["Time zero: eligible, strategy assigned, follow-up starts"]
    T0 --> F["Follow-up (up to 5 years)"]
    F --> E["End: outcome, death, loss or 5 years"]
```

## 6. Results

<!-- Guidance: Report numbers at each stage, baseline characteristics by strategy before and after weighting, follow-up, missing data, outcome counts, and both absolute and relative effects with precision for each estimand. -->

This section reports who entered the emulated trial and what was found.

### 6.1 Participant flow [8]

All counts are *Example* values: 64,530 - 33,149 = 31,381 eligible and
assigned; 12,406 + 18,975 = 31,381.

```mermaid
flowchart TD
    A["Assessed for eligibility (n = 64,530)"] --> B["Not eligible (n = 33,149)"]
    A --> C["Eligible and assigned (n = 31,381)"]
    C --> D["Initiated class A (n = 12,406)"]
    C --> E["Initiated class B (n = 18,975)"]
```

| Reason not eligible | n |
|---|---|
| [Reason] | [n] |
| [Reason] | [n] |
| Total | [33,149] |

### 6.2 Baseline characteristics [9]

| Characteristic | Class A, unweighted | Class B, unweighted | SMD unweighted | SMD weighted |
|---|---|---|---|---|
| Age, years, mean (SD) | [value] | [value] | [0.21] | [0.02] |
| HbA1c, mmol/mol, mean (SD) | [value] | [value] | [value] | [value] |
| Prior cardiovascular disease, n (%) | [value] | [value] | [value] | [value] |
| [Confounder] | [value] | [value] | [value] | [value] |

### 6.3 Follow-up [10]

[Median (IQR) follow-up by strategy, total person-years, and the
number ending follow-up for each reason: outcome, death, loss, end of
data, administrative end.]

### 6.4 Missing data [11]

| Variable | Missing in class A, n (%) | Missing in class B, n (%) | Handling |
|---|---|---|---|
| [HbA1c] | [n (%)] | [n (%)] | [Multiple imputation] |
| [BMI] | [n (%)] | [n (%)] | [Multiple imputation] |

### 6.5 Outcome frequency [12]

| Strategy | Participants | Events (crude) | Crude 5-year proportion |
|---|---|---|---|
| Class A | *Example:* 12,406 | 720 | 5.8% |
| Class B | 18,975 | 1,499 | 7.9% |

Crude proportions ignore censoring and confounding; they describe the
data and are not effect estimates.

### 6.6 Effect estimates [13]

| Estimand | Weighted 5-year risk A | Weighted 5-year risk B | Risk difference (95% CI) | Risk ratio (95% CI) |
|---|---|---|---|---|
| EST-01 | *Example:* 6.2% | 7.4% | -1.2 percentage points (-1.8 to -0.6) | 0.84 (0.76 to 0.92) |
| EST-02 | [value] | [value] | [value] | [value] |

*Example:* A risk difference of -1.2 percentage points corresponds to
about 83 people initiating class A instead of class B for one fewer
hospitalisation over 5 years (1 / 0.012 = 83.3).

### 6.7 Additional analyses [14]

| ID | Result | Interpretation |
|---|---|---|
| SA-01 | [E-value for RR and for the CI limit] | [Strength of confounding needed] |
| SA-02 | [Estimate for negative control outcome] | [Close to null suggests limited residual confounding] |
| SA-03 | [EST-02 estimate with a 60-day permissible gap] | [Robust or sensitive] |
| SA-04 | [Emulated versus trial estimate] | [Agreement] |
| SA-05 | [Complete-case estimate] | [Comparison] |

## 7. Discussion

<!-- Guidance: Interpret the estimates in light of the identifying assumptions and the ways the emulation fell short of the target trial. Avoid stronger causal language than the assumptions support. -->

The discussion interprets the findings and their limits.

### 7.1 Interpretation [15]

[Summarise the key results for each estimand, compare them with
randomised trials and other observational studies, and state what they
mean for the decision the causal question was meant to inform.]

### 7.2 Limitations [16]

- [Components where the emulation departs from the target trial, for
  example no blinding or a grace period not present in the trial]
- [Plausibility of conditional exchangeability, positivity and
  consistency, with the evidence from section 6.7]
- [Measurement error in treatment, outcome or confounders]
- [Generalisability of the eligible population to the people who face
  the decision]

## 8. Other information

<!-- Guidance: TARGET asks for ethics, registration, data and code availability, funding and conflicts of interest. Fill each item even when the answer is "none". -->

Transparency information for readers:

### 8.1 Ethics [17]

[Name of the approving committee and reference number, or the basis of
exemption.]

### 8.2 Registration and protocol [18]

[Whether the protocol was registered or published, when (relative to
data access and analysis) and where.]

### 8.3 Data, code and materials [19]

| Item | Availability |
|---|---|
| Data | [Access route through the data custodian, conditions] |
| Code | [Repository and commit or DOI] |
| Code lists | [Appendix B or repository] |

### 8.4 Funding [20]

[Funders, grant numbers and the role of each funder in design, analysis
and reporting.]

### 8.5 Conflicts of interest [21]

[Financial and non-financial interests of each author.]

## 9. TARGET traceability

<!-- Guidance: Use this table to fill the official TARGET checklist, which is published as an editable document on the TARGET website. Give the section here and the page in the manuscript. -->

| Item | Section of TARGET | Topic (short) | Section in this file | Page |
|---|---|---|---|---|
| 1a to 1c | Abstract | Identification, data source, summary | 1 | [p] |
| 2 | Introduction | Background | 2.1 | [p] |
| 3 | Introduction | Causal question | 2.2 | [p] |
| 4 | Introduction | Rationale for emulation | 2.3 | [p] |
| 5 | Methods | Data sources | 3 | [p] |
| 6a to 6h | Methods | Target trial protocol components | 4, 4.1 to 4.8 | [p] |
| 7a to 7h | Methods | Emulation of each component | 4, 4.1 to 4.8 | [p] |
| 8 | Results | Participant flow | 6.1 | [p] |
| 9 | Results | Baseline characteristics | 6.2 | [p] |
| 10 | Results | Follow-up | 6.3 | [p] |
| 11 | Results | Missing data | 6.4 | [p] |
| 12 | Results | Outcome frequency | 6.5 | [p] |
| 13 | Results | Effect estimates | 6.6 | [p] |
| 14 | Results | Additional analyses | 6.7 | [p] |
| 15 | Discussion | Interpretation | 7.1 | [p] |
| 16 | Discussion | Limitations | 7.2 | [p] |
| 17 | Other information | Ethics | 8.1 | [p] |
| 18 | Other information | Registration | 8.2 | [p] |
| 19 | Other information | Data and code | 8.3 | [p] |
| 20 | Other information | Funding | 8.4 | [p] |
| 21 | Other information | Conflicts of interest | 8.5 | [p] |

## 10. Glossary and references

<!-- Guidance: Define the causal inference terms used above for co-authors and reviewers from other disciplines. -->

### 10.1 Glossary

| Term | Meaning in this template |
|---|---|
| Target trial | The randomised trial that would answer the causal question if it could be run |
| Time zero | The moment when eligibility is met, a strategy is assigned and follow-up starts |
| Estimand | The precise quantity the study aims to estimate |
| Intention-to-treat effect | The effect of being assigned to (here, initiating) a strategy, regardless of later adherence |
| Per-protocol effect | The effect of following the strategy as specified in the protocol |
| Cloning, censoring and weighting | A method for strategies that cannot be distinguished at time zero, such as grace periods |
| E-value | The minimum strength of association an unmeasured confounder would need with both treatment and outcome to explain away an estimate |
| SMD | Standardised mean difference, a balance measure |

### 10.2 Methodological references

- Cashin AG, Hansford HJ, Hernán MA, Swanson SA, et al. Transparent
  reporting of observational studies emulating a target trial: the
  TARGET Statement. BMJ. 2025;390:e087179.
  https://doi.org/10.1136/bmj-2025-087179 (also JAMA.
  2025;334(12):1084-1093, https://doi.org/10.1001/jama.2025.13350)
- Hernán MA, Robins JM. Using big data to emulate a target trial when a
  randomized trial is not available. Am J Epidemiol.
  2016;183(8):758-764. https://doi.org/10.1093/aje/kwv254
- Hernán MA, Wang W, Leaf DE. Target trial emulation: a framework for
  causal inference from observational data. JAMA.
  2022;328(24):2446-2447. https://doi.org/10.1001/jama.2022.21383

Related templates in this library: `science/observational-study`
(STROBE report), `science/preregistration` (study registration),
`academic/stats-analysis-plan` (statistical analysis plan),
`academic/stats-logistic-regression` (regression reporting),
`science/trial-protocol` (protocol for a real randomised trial) and
`science/prediction-model-report` (prediction rather than causal
questions).

## Standard and licence note

Structure aligned with the TARGET guideline (Transparent Reporting of
Observational Studies Emulating a Target Trial), published
simultaneously in JAMA 2025;334(12):1084-1093 and BMJ
2025;390:e087179, listed by the EQUATOR Network at
https://www.equator-network.org/reporting-guidelines/22462/. The TARGET
checklist is published by the TARGET group under CC BY-ND 4.0, so this
template does not reproduce or adapt its wording: it uses only the item
numbers and short topic names, with original explanatory text. Official
checklist: TARGET group, https://target-guideline.org. Checked in
October 2026; check the current version before use. This is a document
format, not clinical, epidemiological or statistical advice. Template
licence: MIT.