Precision Health: How to Perform a Study —
A Structured Guide for Students
A precision health study assignment requires more than describing what precision health is. It requires you to select a defensible study design, identify appropriate data sources, specify variables across genomic, clinical, and social domains, address the ethical dimensions that are specific to this field, and produce a structured report or proposal that holds up under methodological scrutiny. This guide maps each of those tasks and tells you how to approach them — without completing the assignment for you.
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A “precision health study” assignment is not asking you to describe the concept of precision health. It is asking you to demonstrate that you understand how precision health research is conducted — how a research question is framed, how a study design is selected to answer it, how data is sourced and operationalised across multiple domains (genomic, clinical, environmental, social), how results are interpreted within the logic of personalisation, and how the ethical constraints specific to this field are addressed. Students who produce conceptual overviews without methodological specificity consistently score below students who demonstrate command of the research process itself.
The phrase “how to perform a study” in the assignment prompt is doing significant work. It is not asking for a literature review of precision health research. It is not asking for a summary of what precision medicine can do. It is asking you to show that you could design, execute, and report a study that fits within the precision health paradigm — or to critically appraise how an existing study did so. The distinction matters because the skills being assessed are methodological, not definitional.
Most assignments in this space take one of three forms: a study proposal (design a precision health study you would conduct), a secondary analysis plan (describe how you would analyse an existing dataset from a public repository), or a critical appraisal (evaluate the methodological rigour of a published precision health study). Each requires a different output, but all three require the same foundational understanding of how this type of research works. Clarify which form your assignment requires before you start writing.
Clarify the Assignment Type Before Proceeding
Read your assignment brief carefully and identify: (1) Are you designing an original study or working with existing data? (2) Is this a full study report, a protocol/proposal, or an appraisal of someone else’s study? (3) What data access do you actually have — or are you designing a study hypothetically? (4) What citation format is required, and are there minimum source requirements? The answers determine the entire structure of your output. A study proposal and a secondary analysis report are produced differently and assessed on different criteria.
Precision Health: Field Foundations You Must Demonstrate in Your Study
You cannot design a defensible precision health study without understanding what distinguishes this field from conventional population-based health research. Precision health rests on a core premise: that variation in biological, environmental, and behavioural factors across individuals means that one-size-fits-all interventions are suboptimal for many people — and that data integration across those domains can enable more targeted, effective, and equitable approaches to prevention, diagnosis, and treatment. Your study needs to operationalise that premise, not just cite it.
The Six Domains Precision Health Research Integrates — and What Each Requires in Your Study
A precision health study that draws only from genomic data is not fully realising the field’s scope. Understand which domains your study engages, and justify the selection.
Genomics and Molecular Biology
- Single nucleotide polymorphisms (SNPs), polygenic risk scores (PRS), pharmacogenomics, epigenomics, and microbiome data are all within scope
- Genome-wide association studies (GWAS) are the most common design for identifying genetic variants associated with disease risk
- Your study must specify which genomic variables are relevant to your research question and why — not include genetics generically
- Specify genotyping platform or sequencing approach (whole genome, whole exome, targeted panel) if designing primary data collection
- For secondary analysis, identify which genomic variables are available in your chosen dataset
Clinical and Biomarker Data
- Electronic health records (EHRs), biobank data, laboratory results, imaging data, and patient-reported outcomes are primary sources
- Biomarkers — measurable biological indicators of disease state, risk, or treatment response — are central to precision health study design
- Specify which biomarkers your study measures, at what time points, and using what assay or instrument
- Address data quality issues: EHR data is frequently incomplete, inconsistently coded, and subject to documentation bias
- Phenotyping (defining who has a condition and who does not) from clinical data requires explicit operational criteria
Environmental Exposures
- Exposome — the totality of environmental exposures across a lifetime — is increasingly integrated with genomic data in precision health research
- Air quality, chemical exposures, built environment features, neighbourhood characteristics, and access to green space are measurable environmental variables
- Geospatial data linkage (linking participant address to area-level environmental data) is a common methodological approach
- Distinguish between individual-level environmental measures and area-level proxies — the latter introduce ecological fallacy risk
- Gene-environment interaction (GxE) analysis is a key precision health methodology when both domains are present
Lifestyle and Behavioural Data
- Diet, physical activity, sleep, substance use, and stress are modifiable behavioural variables that interact with biological risk
- Wearable devices, ecological momentary assessment (EMA), and smartphone sensors provide continuous behavioural data at scale
- Self-reported behavioural data carries social desirability and recall bias — specify how your study addresses this
- Actigraphy and accelerometry provide objective physical activity and sleep data but require device provision and participant compliance
- Specify measurement instruments (validated questionnaires: IPAQ for physical activity, PSQI for sleep quality) rather than generic “lifestyle survey”
Social Determinants of Health
- Income, education, occupation, housing, food security, social support, and discrimination are social determinants that interact with biological risk
- Precision health that integrates social determinants moves beyond biological reductionism — this is the distinction between precision medicine and precision health
- Area deprivation indices (ADI), neighbourhood-level SES measures, and individual-level social screening tools (e.g., PRAPARE) operationalise social data
- Address the risk that precision health approaches can inadvertently amplify health inequities if social determinants are ignored or treated as confounders rather than targets
Digital Health and Informatics
- Machine learning, natural language processing, and AI-driven risk stratification are increasingly part of precision health study methodology
- Federated learning approaches allow analysis across distributed datasets without centralising sensitive individual-level data
- Patient portals, mobile health apps, and telemedicine platforms generate longitudinal patient-generated health data (PGHD)
- Algorithm bias is a documented precision health problem — models trained on non-representative populations perform poorly when applied to underrepresented groups
- If your study uses a predictive model, address training data composition, validation approach, and fairness metrics explicitly
Most coursework precision health studies will not integrate all six domains. Your task is to identify which domains are relevant to your specific research question, justify that selection, and demonstrate that you understand the data requirements, methodological implications, and limitations that come with each domain you include. A study that claims to be “precision health” but only includes genomic variables without engaging with environmental or social context is not demonstrating command of the field’s full scope — and a good study report will acknowledge that limitation explicitly rather than leaving it implicit.
The “Precision” in Precision Health Must Be Operationalised
Your study is not a precision health study just because it mentions genomics. The precision has to be demonstrated in the design: how does your study use individual-level variation in biological, environmental, or social data to produce a finding or recommendation that is more specific than what a population-average approach would generate? If your study could be conducted without any individual-level biological data, it is not a precision health study — it is an epidemiology study. Identify the specific mechanism by which your design achieves precision, and make that explicit in your research question and methods section.
Framing Your Research Question — the Step That Determines Every Downstream Decision
Every methodological decision in your study flows from your research question. A poorly framed question produces a study that cannot be evaluated for success or failure, because there is no clear criterion for what the study is trying to establish. A well-framed precision health research question specifies the population, the exposure or intervention, the outcome, and the mechanism of individualisation — the element that makes it a precision health question rather than a general health research question.
A research question that could be answered with population-average data is not a precision health research question. Precision requires that individual-level variation in some biological, environmental, or social variable is doing meaningful work in the analysis.
— The test to apply to your own research question before finalising it| Question Element | What It Specifies | Example in a Precision Health Context |
|---|---|---|
| Population (P) | Who are the participants? Defined by diagnosis, demographic characteristics, genetic profile, or risk category — not just “adults” or “patients” | Adults aged 40–65 with a polygenic risk score in the top quartile for type 2 diabetes and no current diagnosis |
| Exposure / Intervention (E/I) | What variable, intervention, or exposure is being examined? Must be operationalised — not just “lifestyle modification” but a specific, measurable exposure | Participation in a genotype-informed dietary intervention programme compared with standard dietary guidelines |
| Comparison (C) | What is the comparator — the group or condition against which the exposure or intervention is evaluated? | Standard dietary counselling without genetic risk information |
| Outcome (O) | What is being measured? Primary and secondary outcomes, specified with the measurement tool and time point | HbA1c level at 12 months (primary); fasting glucose, BMI, and self-reported dietary adherence at 6 and 12 months (secondary) |
| Precision element | What individual-level variation is being used to differentiate the study from a population-average approach? This is the element that makes it a precision health study | Polygenic risk score stratification — the intervention is informed by and evaluated against individual genetic diabetes risk, not population average risk |
| Time frame (T) | Over what period is the study conducted? Follow-up duration must be matched to the biological plausibility of detecting the outcome | 12-month follow-up with assessments at baseline, 6 months, and 12 months |
When you write your research question, test it against two criteria. First: is the question answerable — does it specify what data you would need to answer it, and is that data obtainable in principle? Second: is the precision element doing meaningful work — would removing the individual-level biological or social variable from the design collapse the study into a conventional one? If the answer to the second question is yes, you need to make the precision element more central to the design, not peripheral to it.
Avoid Research Questions That Are Too Broad to Be Answered
“What is the role of genetics in personalised medicine?” is not a research question — it is a topic. “Does pharmacogenomic CYP2C19 genotyping improve clopidogrel response outcomes in patients undergoing percutaneous coronary intervention compared with standard antiplatelet dosing, as measured by major adverse cardiovascular events at 12 months?” is a research question. The difference is operationalisation: every term is defined, every variable is specified, and the answer is in principle obtainable from a study of defined design. Your research question should pass the same standard. If your question cannot be answered by any specific study design you can describe, it is not yet a research question.
Selecting a Study Design — Matching the Design to the Question
Study design selection in precision health research follows the same logic as in all health research: the design must be capable of answering the research question, must be feasible given the data available, and must control for the sources of bias most relevant to the question. The additional consideration in precision health is that the design must accommodate multi-domain data integration — and many traditional epidemiological designs require adaptation to handle the dimensionality and heterogeneity of precision health data.
Genome-Wide Association Study (GWAS)
Used when the research question concerns identifying genetic variants associated with a disease or trait. Requires large sample sizes (typically thousands to hundreds of thousands) and is observational. Standard output is a Manhattan plot of statistically significant SNP associations. Appropriate for: mapping genetic architecture of a condition, generating polygenic risk scores. Not appropriate for: causal inference, intervention evaluation, or questions that integrate social determinants without additional methodology.
Cohort Study with Biobank Linkage
Longitudinal design following a defined population over time, with biological samples collected at baseline and linked to health outcomes. The UK Biobank, All of Us (NIH), and HUNT Study are examples of such cohorts. Appropriate for: gene-environment interaction studies, risk stratification, incident disease prediction. Requires addressing time-varying confounding, loss to follow-up, and the difference between discovery and validation samples.
Randomised Controlled Trial with Genomic Stratification
The highest evidence level for intervention questions. Participants are randomised, and analysis is stratified by genomic subgroup — either pre-specified or exploratory. Examples include pharmacogenomic trials and genotype-guided lifestyle intervention trials. Appropriate for: causal inference about genotype-specific treatment effects. Requires adequate power within each genomic subgroup, which typically demands large overall sample sizes.
Mendelian Randomisation (MR)
Uses genetic variants as instrumental variables to estimate the causal effect of a modifiable exposure on an outcome — addressing confounding that limits observational studies. Appropriate for: causal inference questions about exposures that cannot be randomly assigned (e.g., BMI, cholesterol levels, smoking). Requires three core assumptions about the instrument (relevance, independence, exclusion restriction) that must be explicitly addressed and tested.
EHR-Based Retrospective Analysis
Uses electronic health record data — often linked to biobank or genomic data — to conduct retrospective analyses at scale. Appropriate for: pharmacovigilance, adverse event identification, real-world treatment effectiveness, phenotyping studies. Requires explicit handling of data quality issues: missing data, coding inconsistency, and selection bias from the clinical population that generates EHR data (the clinical population is not the general population).
N-of-1 (Single-Subject) Trial
A crossover design in which a single individual serves as their own control, receiving alternating interventions in randomised order. Directly aligned with the precision health principle of individualisation. Appropriate for: evaluating personalised treatment effects in a single patient, particularly in chronic conditions with stable disease. Limitations include lack of generalisability, carry-over effects, and the need for the outcome to be reversible across intervention periods.
After selecting a design, justify it explicitly in your methods section. State why this design answers your research question and what alternative designs were considered and rejected — and why. This demonstrates methodological reasoning, not just design selection. Also address the design’s primary threats to validity: internal validity (confounding, bias, reverse causation) and external validity (to whom the findings generalise, and whether that population is representative of those who would most benefit from the precision health approach).
Power and Sample Size: Why You Must Address This Even in a Proposal
Precision health studies frequently involve subgroup analyses — testing whether an effect differs by genomic stratum, biomarker level, or risk category. Subgroup analyses require that each subgroup is adequately powered independently of the overall study. A study with 500 participants that stratifies by four genomic risk categories has approximately 125 participants per stratum — which is typically underpowered to detect subgroup-specific effects of realistic magnitude. Your study proposal must include a power calculation or, if you cannot provide one formally, a discussion of the sample size requirements and how they would be met. Stating “a sample size of 200 participants will be recruited” without any power justification is a methodological gap that costs marks.
Data Sources, Variable Specification, and the Integration Challenge
Precision health studies are data-intensive by design. The field’s integrative premise — combining genomic, clinical, environmental, behavioural, and social data for the same individuals — creates specific data challenges that your study must address: data linkage across heterogeneous sources, missing data patterns that are not random, variable harmonisation across datasets, and the curse of dimensionality when the number of variables approaches or exceeds the number of participants.
Major Public Precision Health Data Repositories
- All of Us (NIH, USA) — longitudinal cohort integrating EHR, genomics, survey, wearable, and environmental data; open access tier available for summary statistics
- UK Biobank — 500,000-participant prospective cohort with genotyping, imaging, and linked EHR data; requires approved access application
- dbGaP (NCBI) — repository of GWAS and genomic study data; controlled access for individual-level data, open access for summary-level data
- TCGA (The Cancer Genome Atlas) — multi-omic cancer genomics dataset covering 33 cancer types; open access for most data tiers
- NHANES — cross-sectional survey with clinical examination, laboratory, and genomic components; publicly available for secondary analysis
- 1000 Genomes Project — reference population genomic dataset; appropriate for ancestry analyses and variant frequency comparisons
- PharmGKB — curated database of pharmacogenomic relationships; useful for pharmacogenomics study design and literature support
Key Variable Categories to Specify in Your Methods
- Exposure variables — the independent variable(s) your study examines; must be operationalised with measurement instrument, scale, and time point
- Outcome variables — primary and secondary outcomes, specified with measurement tool, clinically meaningful threshold, and assessment schedule
- Genomic variables — specific SNPs, polygenic risk scores, gene panels, or expression signatures; specify how they were measured and validated
- Covariates / confounders — age, sex, ancestry, socioeconomic position, medication use, comorbidities; must be explicitly listed and justified
- Effect modifiers — variables that change the direction or magnitude of the exposure-outcome association across subgroups; central to precision health analysis
- Mediators — variables through which the exposure affects the outcome; relevant when your question concerns mechanism, not just association
- Missing data variables — specify the expected missingness pattern and how it will be handled (complete case, multiple imputation, sensitivity analysis)
Handling Multi-Omic Data Integration
When your study integrates data across multiple biological levels — genomics, transcriptomics, proteomics, metabolomics — the analytical challenge multiplies. Multi-omic integration requires explicit decisions about the analytical strategy: are you using a staged approach (analyse each omic layer separately, then integrate findings), a joint modelling approach (simultaneous analysis across layers), or a data-driven dimensionality reduction approach such as MOFA (Multi-Omics Factor Analysis)? Each has assumptions and limitations that must be disclosed.
For a coursework assignment, you are not expected to conduct multi-omic analysis from scratch. You are expected to demonstrate that you understand what the data integration challenge is, what approaches exist to address it, and which approach your study uses and why. If your study is a secondary analysis of a dataset that already has multi-omic data processed and harmonised, identify that and cite the data processing protocol used in the original study.
Ancestry, Population Stratification, and Representativeness
Precision health research has a well-documented representativeness problem: the vast majority of GWAS data has historically been generated from participants of European ancestry, meaning polygenic risk scores and genomic findings derived from those studies have reduced accuracy when applied to individuals of non-European ancestry. Your study must address this. If you are using existing genomic data, report the ancestry composition of the dataset and acknowledge the limitations this places on the generalisability of your findings. If you are designing a primary data collection study, address how you will ensure ancestry diversity in recruitment. Treating population stratification as only a statistical nuisance (a confounder to control for) rather than an equity issue is a common and significant oversight in precision health study designs.
Ethics in Precision Health Research — Not a Section, a Thread
Precision health research raises ethical issues that are more complex and more specific than those in conventional clinical research — because it deals with data types (genomic, longitudinal, multi-domain) that are uniquely sensitive, and because its stated purpose (individualisation) creates specific obligations around what findings mean for individuals and their families. Ethics in your study is not a paragraph added at the end. It is a dimension of every methodological decision you make.
Informed Consent and Genomic Data
Genomic data is heritable, potentially identifying, and carries implications for biological relatives who did not consent. Broad consent (agreeing to unspecified future uses) is increasingly used in biobank research, but its adequacy is contested — particularly for uses not foreseeable at the time of consent. Your study must specify what form of consent was or will be used, whether it was broad or study-specific, and how participants will be re-contacted if the study generates findings with personal health implications.
Return of Incidental Findings
Genomic sequencing frequently identifies variants with health implications beyond the study’s scope — variants associated with cancer risk, cardiovascular disease, or other serious conditions that the participant did not ask to know about. Your study needs a return-of-findings policy: which categories of incidental findings will be disclosed, to whom, in what format, with what support? The ACMG (American College of Medical Genetics) maintains a list of reportable secondary findings; engaging with this framework in your methods demonstrates field knowledge.
Data Privacy and Re-identification Risk
Genomic data is not truly anonymisable — individuals can be re-identified from genomic data alone, even in aggregated datasets, given sufficient reference data. Your study must address: de-identification approach (which standard — HIPAA Safe Harbor, Expert Determination, or ISO 27701?), data storage security, access controls, data sharing plan, and the specific re-identification risks posed by the data types you are using. Stating “data will be kept confidential” without technical specificity is insufficient.
Equity and the Risk of Amplifying Disparities
Precision health tools — risk scores, predictive algorithms, genotype-guided interventions — perform best in populations similar to those used in their development. When those development populations are predominantly of European ancestry and high socioeconomic status, deploying precision health tools in more diverse populations can produce inaccurate or harmful results. Your study must address who benefits and who may be harmed by the precision health approach it evaluates, and whether the study population is representative enough to support the claims being made.
IRB and Regulatory Approval
Any study involving human participants or identifiable human data requires IRB (Institutional Review Board) approval in the US, or equivalent ethics committee review in other jurisdictions. For secondary analysis of existing datasets, confirm whether the original study’s IRB approval covers the specific use you are proposing, or whether a new application is required. Data use agreements (DUAs) for public repositories have specific conditions that must be met. State the approval status and any conditions in your study protocol.
Genetic Discrimination
In the US, the Genetic Information Nondiscrimination Act (GINA) prohibits discrimination in health insurance and employment based on genetic information — but does not cover life insurance, disability insurance, or long-term care insurance. Participants in precision health research may face risks outside GINA’s scope. Informed consent must disclose these risks. If your study involves disclosure of genetic risk information to participants, address the psychological impact and the support resources available — particularly for high-penetrance findings.
In your study write-up, ethical considerations should not be confined to a single section labelled “Ethics.” They should appear wherever they are relevant: in the methods section when you discuss consent and data collection; in the data section when you discuss storage and access; in the results/analysis section when you discuss the return-of-findings protocol; and in the discussion when you address equity implications and generalisability. A study that treats ethics as a box to check at the end has not understood how ethics operates in this field.
How to Structure Your Precision Health Study Paper or Proposal
The structure of a precision health study paper follows the standard scientific manuscript format — but each section has specific requirements in the precision health context that differ from a general health research paper. Understanding what each section must contain for this type of study will prevent the most common structural errors: methods sections that describe data collection without specifying analytical approach, discussion sections that list limitations without connecting them to the study’s conclusions, and ethics sections that are generic rather than study-specific.
Establish the clinical or public health problem your study addresses. Explain why a precision health approach is appropriate — what individual-level variation makes a population-average approach insufficient. Summarise existing evidence to identify the gap your study fills. End with a clearly stated research question or objective and a brief statement of the study design. The introduction should justify the precision health approach specifically, not just health research in general.
The most technically demanding section. Must cover: study design and justification; participant selection criteria (inclusion/exclusion); data sources for each domain integrated; variable operationalisation for exposure, outcome, covariates, and genomic variables; sample size and power calculation; statistical analysis plan (primary analysis, subgroup analyses, sensitivity analyses); and missing data handling strategy. For a proposal, write in future tense. For a completed study, write in past tense. Report with STROBE, CONSORT, or SPIRIT checklist as appropriate.
Present findings in the order established by the analysis plan — not in the order that tells the most compelling story. For a precision health study: report sample characteristics including ancestry composition and attrition; report primary analysis results with effect size, confidence intervals, and p-values; report subgroup or stratified analyses that constitute the “precision” component; report sensitivity analyses. Tables and figures must be self-explanatory. Do not interpret in the results section — save interpretation for the discussion.
Interpret findings in the context of the research question and existing literature. Address: what the results mean for precision health practice; whether the precision element of the design produced findings different from a population-average approach would have; limitations with specific reference to the threats to validity most relevant to this design; generalisability with attention to population representativeness; and implications for equity. The discussion should not simply restate the results — it should explain what they mean and for whom.
The ethics section (if separate from methods) should cover IRB approval status, consent approach, data privacy and re-identification measures, return-of-findings policy, and equity implications. The conclusion should be a brief, precise summary of what the study found or proposes, what its key contribution is, and what the next steps are. It should not introduce new content. For a proposal, the conclusion states why the proposed study is feasible, ethical, and significant.
Pre-Submission Checklist for Your Precision Health Study
- Your research question specifies a population, exposure or intervention, comparator, outcome, and precision element — and cannot be answered without individual-level biological or social data
- Your study design is named, defined, and justified — including why alternative designs were not selected
- All variables (exposure, outcome, covariates, genomic, social) are operationalised with specific measurement instruments, scales, and time points
- Your data source is identified with access pathway, approval status, and the specific data available within it
- A power calculation or sample size justification is included
- Your analysis plan specifies primary analysis, subgroup analyses, and sensitivity analyses separately
- Missing data strategy is addressed — not assumed to be absent
- Population stratification and ancestry representativeness are addressed
- All six ethical dimensions are addressed (consent, return of findings, privacy, equity, IRB, genetic discrimination risk)
- Limitations section addresses threats to internal and external validity specific to the design you chose
- Reporting checklist (STROBE, CONSORT, or SPIRIT) is applied and referenced
- All sources are cited according to the required format (APA, AMA, Vancouver) with no citation-result mismatches
Strong vs. Weak Approaches — What the Difference Looks Like on the Page
The difference between these two passages is entirely specificity. The strong version demonstrates knowledge of where the data lives (All of Us), which specific genomic tool is being applied (a named, validated PRS), how the outcome is measured (ICD-10 codes with a quality threshold), and which confounders are controlled and why. Every sentence does methodological work. The weak version demonstrates awareness that a methods section exists and contains certain elements — but provides no information about how those elements are operationalised in this specific study. Instructors grading on a methodological rigour rubric distinguish these two levels immediately and consistently.
Use Reporting Guidelines as Your Methods Section Framework
For observational studies: the STROBE checklist (Strengthening the Reporting of Observational Studies in Epidemiology) provides a 22-item list of what a well-reported cohort, case-control, or cross-sectional study should include. For randomised trials: CONSORT. For study protocols: SPIRIT. Work through the relevant checklist item by item when drafting your methods section. Every item you cannot complete reveals a gap in your design that needs to be addressed — either by adding the missing element or by explaining in your limitations why it was not feasible. These checklists are available free at equator-network.org and are widely recognised by instructors in health research courses.
The Most Common Errors in Precision Health Study Assignments — and How to Correct Them
| # | The Error | Why It Costs Marks | The Fix |
|---|---|---|---|
| 1 | Writing a definition essay instead of a study | A paper that spends three of its five pages defining precision health, describing its history, and reviewing what other studies have found has not performed a study — it has produced a literature review. The assignment asks you to demonstrate methodological competence, not definitional knowledge. Definition and background are necessary but should occupy no more than 20–25% of the total paper. | Allocate your word count deliberately: introduction (15–20%), methods (30–35%), results or analysis (20–25%), discussion and ethics (20–25%). If your methods section is shorter than your introduction, the balance is wrong. The methods section is where you demonstrate that you understand how the study is conducted — it should be the most detailed section in the paper. |
| 2 | Treating genomics as a synonym for precision health | A study that only includes genomic variables without addressing environmental, behavioural, or social dimensions is a genomics study, not a precision health study. The field’s integrative premise — that biological, environmental, and social data must be combined to achieve meaningful individualisation — is a core conceptual requirement, not an optional add-on. Ignoring non-genomic domains suggests the student understands precision medicine but not precision health. | Identify at least two domains beyond genomics that your study integrates. Justify the selection in the introduction: why does your research question require both genomic and, for example, environmental data? If your study genuinely only involves genomics, frame it as a precision medicine study rather than a precision health study, and acknowledge the scope limitation explicitly in the discussion. |
| 3 | Vague variable specification — “lifestyle factors will be measured” | “Lifestyle factors will be measured” is not a methods statement. It tells a reader nothing about what was measured, how, with what instrument, at what frequency, or with what reliability. A methods section that uses placeholders instead of operationalised variables cannot be replicated, critiqued, or assessed for appropriateness. Vague variable specification is the most consistent distinguishing feature between low-scoring and high-scoring precision health study papers. | For every variable in your study, complete this sentence: “X was measured using [instrument/assay/questionnaire] at [time point(s)], producing values on a [scale/unit], with a minimum detectable value of [threshold] and reliability established by [source].” If you cannot complete that sentence, you have not operationalised the variable yet. Go back to the literature and identify how other studies in this area have measured the same variable. |
| 4 | Omitting population stratification and ancestry from the design | Population stratification — the confounding of genetic association signals by ancestry differences across study participants — is a fundamental methodological concern in genomic research. A study that does not address this demonstrates a gap in foundational genomic epidemiology knowledge. Similarly, a study that does not report the ancestry composition of its sample cannot support claims about the generalisability of genomic findings to diverse populations. | In your methods section, specify how population stratification will be controlled — typically through inclusion of principal components of ancestry (PCAs) as covariates in regression analyses. Report the ancestry composition of the study sample in your participant characteristics table. In the limitations, address how the ancestry composition of the sample limits or supports the generalisability of your findings. |
| 5 | A generic ethics paragraph that does not engage with precision health-specific concerns | “This study will follow ethical guidelines and participant data will be kept confidential” demonstrates awareness that ethics exist but not understanding of what the specific ethical challenges of precision health research are. Genomic data re-identification risk, return of incidental findings, genetic discrimination, broad consent, and equity in genomic research are all precision health-specific ethical issues that a competent study should address. Generic ethics statements score in the lowest rubric band. | Address each of the six ethical dimensions identified in Section 6 of this guide, with specific reference to how your study handles each one. Use named frameworks: the Belmont Report’s three principles (respect for persons, beneficence, justice), ACMG guidelines on secondary findings, GINA’s scope and limitations, FAIR data principles for genomic data sharing. Showing that you know the frameworks signals field competence. |
| 6 | No discussion of statistical analysis plan or analytical approach | Identifying a dataset and naming variables is half the methods section. The other half is specifying what you will do with those variables analytically. A methods section that describes data collection but not data analysis cannot be evaluated for whether the analysis matches the research question. “Statistical analysis was performed using SPSS” tells a reader nothing about what statistical tests were applied, to what variables, with what covariates, and with what approach to multiple comparisons. | Your analysis plan should specify: the primary statistical test or model and why it matches the outcome type (logistic regression for binary outcomes, linear regression for continuous, Cox proportional hazards for time-to-event); which covariates are included in adjusted models and why; how subgroup or stratified analyses will be conducted; whether and how multiple comparison correction (Bonferroni, FDR) will be applied; and what sensitivity analyses will be run to test assumption robustness. |
FAQs: Precision Health — How to Perform a Study
What a Strong Precision Health Study Demonstrates
A strong precision health study — whether a proposal, a secondary analysis, or a critical appraisal — demonstrates three things simultaneously: that you understand what distinguishes precision health from conventional health research (individualisation through multi-domain data integration), that you can operationalise that understanding into a specific, replicable study design with named data sources and specified variables, and that you can engage with the ethical complexity that is inherent to working with genomic and linked personal data. A study that demonstrates all three passes. A study that demonstrates only the first — that you know what precision health is — typically does not.
The most common failure mode is description substituting for design. Describing what precision health can do is not the same as showing how a study in this field is conducted. The methods section is where that distinction is visible, and it is where the marks are distributed. Invest your time there: in specifying variables, justifying design choices, addressing analytical strategy, and engaging with the ethical obligations that are specific to this field and not generic to all health research.
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