Scoping review

The literature search focused on identifying precision medicine publications using the term ‘precision medicine’ and associated proxy nomenclature, among other keywords and phrases (Supplementary Methods). The search identified 2,679 publications, of which 13 were excluded owing to duplication. The remaining 2,666 papers were screened, of which 47 were randomly selected (through computer-generated, random-number sequence) for full text review and quality assessment. The summary (count and percentage) of each quality assessment item across all papers and the quality assessment results for each paper are shown in Supplementary Tables 2 and 3. This quality assessment yielded a median score of 6 (interquartile range = 4–7) with none of the papers achieving a positive quality evaluation across all 11 items (Fig. 1).

Fig. 1: Distribution of manuscripts by the total quality assessment scores.

Median scores of 47 published precision medicine manuscripts randomly selected for full text review and quality assessment through computer-generated, random-number sequence. IQR, Interquartile range.

A summary of the itemized evidence reporting quality is shown in Supplementary Table 2. Most abstracts (81%) reported findings relevant to the four pillars of precision medicine (prevention, diagnosis, treatment and/or prognosis) and provided sufficient detail in the methods sections to determine whether the study was designed to test hypotheses on precision medicine (77%), details about participant eligibility (75%) and descriptions of standard reporting definitions (70%). The items that were less frequently reported were the description of patient and public involvement and engagement (PPIE) in determining the impact and utility of precision medicine (15%), the inclusion of the term ‘precision medicine’ in the title or abstract (17%), the reporting of measures of discriminative or predictive accuracy (23%), the description of the approach used to control risk of false-positive reporting (28%), the reporting of effect estimates with 95% confidence intervals and units underlying effect estimates (57%) and the reporting of a statistical test for comparisons of subgroups (for example, interaction test) (60%).

Stakeholder surveyDelphi panel demographics

Of the 23 Delphi panelists, 22 (96%) completed Delphi survey 1, 18 (78%) and attended the full-panel consensus meeting and 22 (96%) completed Delphi survey 2. All panelists engaged in further extensive dialog around key topics through online communication.

Delphi results

The initial checklist in Delphi survey 1 contained 68 items. After Delphi survey 1 and the full-panel consensus meeting, 2 items were added, resulting in 70 items in Delphi survey 2. At the Consensus meeting, it was determined that the checklist should be used together with existing relevant checklists. These include the CONSORT (Consolidated Standards of Reporting Trials)17 and STROBE (Strengthening the Reporting of Observational Studies in Epidemiology)18 checklists for interventional trials and observational studies, respectively. This led to a recommendation to remove items covered in established checklists (Supplementary Fig. 1). The scoring from Delphi survey 1, Delphi survey 2 and notes from the Consensus meetings are as shown in Supplementary Table 4. After Delphi survey 2, the consensus was to retain 25 items across 6 core categories.

Guidelines finalization

The executive oversight committee reviewed the panel scores and free-text comments from all the rounds of Delphi surveys to determine the final checklist items and wording. The group discussed five items with inconsistent consensus (between 70% and 80% consensus), resulting in the removal of one item because it overlapped conceptually with another item (17b and 17g in Supplementary Table 4). It was also determined that ‘health equity’ should be included as an overarching theme, thereby encouraging users of the checklist to consider this topic more broadly when describing precision medicine research. This resulted in removal of two items.

The final checklist comprised 23 items that the executive oversight committee concluded are unique and essential for reporting standards in precision medicine. The final BePRECISE checklist is presented in Table 1, with a downloadable version of the checklist available online (https://www.be-precise.org, and https://www.equator-network.org/reporting-guidelines/).

Table 1 BePRECISE reporting guidelines for precision medicine researchExplanation of checklist Items

The checklist and the explanation of each item are presented in Table 1. The BePRECISE checklist is intended to complement existing guidelines such as CONSORT17, STROBE18 and PRISMA (Preferred Reporting System for Systematic Reviews and Meta-Analyses)19.

These reporting guidelines use the terms ‘precision medicine’ and ‘personalized medicine’ as defined in the ‘Second international consensus report on gaps and opportunities for the clinical translation of precision diabetes medicine’3, as follows:

‘Precision medicine focuses on minimizing errors and improving accuracy in medical decisions and health recommendations. It seeks to maximize efficacy, cost-effectiveness, safety, access for those in need and compliance compared with contemporary evidence-based medicine. Precision medicine emphasizes tailoring diagnostics or therapeutics (prevention or treatment) to subgroups of populations sharing similar characteristics.’

Personalized medicine refers to ‘the use of a person’s own data to objectively gauge the efficacy, safety, and tolerability of therapeutics, and, subjectively, to tailor health recommendations and/or medical decisions to the individual’s preferences, circumstances, and capabilities’.

Accordingly, personalized medicine can be viewed as being nested within the broader concept of precision medicine.

Equity and PPIE (E1–E4)

Equity, diversity and inclusivity considerations and the involvement of patients and public is a crosscutting theme in this checklist. Where relevant, papers should include a description of how equity has been considered, including diversity and inclusivity of study participants, and whether there was PPIE. Cohort selection biases and probable risks when extrapolating the study’s results to other populations should be clearly described.

The selection of participants should consider racial, ethnic, ancestral, geographic and sociodemographic characteristics20, and include an explanation for the inclusion or exclusion of groups that are typically under-represented in clinical research (E1 and E2). Race and ethnicity are social constructs but, as they are categories recognized by some government and health authorities in contexts that are relevant to precision medicine, we have elected to retain inclusion of these somewhat controversial terms here.

PPIE in any part of the study should be described, including but not limited to design, conduct and reporting (E3).

Where possible, and ideally with guidance from those with lived experience, the potential impact of the research findings on the target population(s) should be discussed (E4). Consider co-writing these aspects with PPIE representatives.

Title and abstract (1.1–1.4)

In the title and/or abstract, the term ‘precision medicine’ should be included to highlight that the research is relevant to precision medicine (1.1). Given that precision medicine is an approach that can be used in several research contexts, the study design (for example, randomized clinical trial (RCT), retrospective observational) and the research question should be stated clearly (1.2). Use of the terms ‘prevention’, ‘diagnostics’, ‘treatment’ or ‘prognostics’ is needed to highlight which pillar of precision medicine the study concerns3 (1.3). To ensure transparency about generalizability and/or applicability of the findings to a specific population or subgroup, the study population must be described (1.4).

Background and objectives (2.1–2.2)

The background should clearly describe the rationale for the chosen precision medicine approach, including the context and prior work that led to it and the specific hypothesis being tested (2.1). To provide the reader with greater context, papers should also state the nature and objective of the precision medicine study as ‘etiological’, ‘discovery’, ‘predictive’ and/or ‘confirmatory’ (2.2).

Methods (general)

Although this reporting guide focuses on clarifying elements of papers that are germane to precision medicine, authors are strongly encouraged to ensure that methods also adhere to other appropriate reporting guidelines (for example, CONSORT and STROBE), with the overarching goal of ensuring that the study protocol described therein could, in principle, be accurately reproduced by third-party investigators.

Methods (3.1–3.7)

Methods should describe the aspects of a study design relating to precision medicine in such detail that the design can be understood and replicated (3.1). The rationale for the choice of primary outcome should be clearly stated (3.2).

To enable readers to assess bias and interpret the study findings, this section should state how the participants were identified and enrolled in the study (4.1) and (if applicable) how a subset of a broader group of participants was selected from an existing study (3.3). Any markers used for stratification or prediction should be explicitly stated with an explanation of how the marker(s) was(were) chosen (3.4).

The sample size and how it was derived should be described, for example, following a priori power calculations, or if the sample size was limited primarily by availability or cost, and any implications that this might have for type 2 error (3.5). Authors should also describe attempts to minimize false-positive discovery, especially when multiple testing has occurred (3.5).

If any replication and/or validation analyses were undertaken, a clear description should be given of the approach, including whether these analyses were planned and relevant datasets identified before or after conclusion of primary analyses (3.6), in addition to justification for the sample size and choice of replication cohort (3.7).

Results (4.1–4.4)

The number of participants in the study should be provided, along with a table of baseline characteristics (4.1). If the analysis involves comparison (rather than discovery) of subgroups, the baseline characteristics and numbers of participants should be provided by the subgroup.

Results from any statistical tests done should be reported. Any comparisons of subgroups should include appropriate test statistics, which may include tests of interaction and heterogeneity, and in cluster analyses tests of probability for cluster assignment (for example, relative entropy statistic) (4.2).

Key findings should be benchmarked against current reference standards or practice, if they exist, so that the reader can determine the likely benefit of translating the study’s findings into clinical practice. This may include, for example, the comparison of the new and existing approaches using tests of discriminative (cross-sectional) or predictive (prospective) accuracy, or estimation of net reclassification or changes in numbers needed to treat. If benchmarking has not been done, a clear explanation should be given (4.3).

If validation and/or replication analyses were undertaken, the results of all such attempts at analyses should be clearly described (4.4).

Discussion (5.1–5.2)

The paper should include a balanced and nuanced discussion of any limitations to the interpretation and/or implementation of the reported findings. The limitations section should consider biases that might prevent fair and equitable generalization of the study’s findings to other populations, particularly to groups that are under-represented within the published literature. Authors are also encouraged to consider other potential biases that might arise with stratified and subgroup analyses (5.1).

If there is a direct clinical implication of the study’s findings, authors should describe how their findings might be applied in clinical practice. This might, for example, include an explanation of how any algorithms, technologies or risk markers that stem directly from the research might benefit clinical practice.