Why English? | Reporting CREWS Real-World Studies Through Data Provenance, Treatment Pathways, Confounding, Missingness, Outcomes and Transportability

ORIENTATION

What this guide covers

Current fact check

The live EQUATOR record lists CREWS under development. It was registered in 2019 and describes literature review, working groups, three Delphi rounds, a consensus meeting and pilot testing. STROBE is published guidance for observational studies, while current FDA real-world-evidence materials emphasise relevance, reliability, data provenance, completeness, linkage and fitness for the research question.

What this guide does not cover

This article focuses on transparent reporting of observational real-world studies. It does not declare a database fit for every purpose, remove confounding through wording, substitute for a protocol or statistical analysis plan, guarantee causal inference, or claim that the CREWS development project has issued final items.

Worked example

Every chapter returns to a fictional multi-clinic study comparing two routinely chosen rehabilitation pathways for adults with persistent shoulder pain. It is fictional, low-stakes and educational; it does not report a real school, clinical decision or guaranteed outcome.

Warm up with eduKate’s guides to explaining scientific evidence and asking better questions in class.

CHAPTER 1 OF 15

1. State the Real-World Question and Estimand

Back to contents

Why this move matters

Define population, treatment or exposure strategies, comparator, outcome, time horizon and the quantity the analysis aims to estimate.

Connect the care pathway to the data pathway

Define population, treatment or exposure strategies, comparator, outcome, time horizon and the quantity the analysis aims to estimate. Map clinical eligibility, treatment decisions, follow-up and outcomes to source systems, extraction windows, linkages, coding rules and analytic time zero.

Audit one patient-to-estimate trail

Worked example: The study asks about one-year functional improvement under two rehabilitation pathways among eligible clinic patients. Trace source records, cohort entry, treatment classification, confounders, missingness, censoring, outcome ascertainment, analysis and transportability.

Separate observation from causal interpretation

Real-world data record care as delivered; they do not automatically create a fair comparison. Design, timing, confounding, selection and measurement determine what an estimate can mean.

Respect the development boundary

A vague effectiveness question can hide several different comparisons. CREWS remains under development on its live EQUATOR record. STROBE, RECORD and current regulatory guidance are useful neighbours, not a substitute for a final CREWS checklist.

Student, parent and career transfer

Make one evidence card for “State the Real-World Question and Estimand”: source location, plain-language meaning, limit and next question. A parent or teacher can ask for one everyday explanation and one specialist explanation; both must preserve the same conditions. This habit supports epidemiology, medicine, nursing, pharmacy, health services research, biostatistics, data engineering, clinical informatics, regulatory science, policy analysis and evidence communication without promising a grade, admission, funding decision or job.

CHAPTER 2 OF 15

2. Describe Every Data Source and Its Original Purpose

Back to contents

Why this move matters

Name owners, systems, coverage, dates, populations, care settings, collection purpose, update cycle and variables contributed by each source.

Connect the care pathway to the data pathway

Name owners, systems, coverage, dates, populations, care settings, collection purpose, update cycle and variables contributed by each source. Map clinical eligibility, treatment decisions, follow-up and outcomes to source systems, extraction windows, linkages, coding rules and analytic time zero.

Audit one patient-to-estimate trail

Worked example: Clinical records provide assessments, scheduling data provide attendance and a registry provides longer-term outcomes. Trace source records, cohort entry, treatment classification, confounders, missingness, censoring, outcome ascertainment, analysis and transportability.

Separate observation from causal interpretation

Real-world data record care as delivered; they do not automatically create a fair comparison. Design, timing, confounding, selection and measurement determine what an estimate can mean.

Respect the development boundary

Routine data are shaped by service and billing needs. CREWS remains under development on its live EQUATOR record. STROBE, RECORD and current regulatory guidance are useful neighbours, not a substitute for a final CREWS checklist.

Student, parent and career transfer

Make one evidence card for “Describe Every Data Source and Its Original Purpose”: source location, plain-language meaning, limit and next question. A parent or teacher can ask for one everyday explanation and one specialist explanation; both must preserve the same conditions. This habit supports epidemiology, medicine, nursing, pharmacy, health services research, biostatistics, data engineering, clinical informatics, regulatory science, policy analysis and evidence communication without promising a grade, admission, funding decision or job.

CHAPTER 3 OF 15

3. Report Data Provenance, Transformations and Linkage

Back to contents

Why this move matters

Trace extraction, transfer, common models, coding maps, deduplication, linkage keys, match quality, versioning and quality checks.

Connect the care pathway to the data pathway

Trace extraction, transfer, common models, coding maps, deduplication, linkage keys, match quality, versioning and quality checks. Map clinical eligibility, treatment decisions, follow-up and outcomes to source systems, extraction windows, linkages, coding rules and analytic time zero.

Audit one patient-to-estimate trail

Worked example: The team maps local codes to one analytic vocabulary and reports deterministic and probabilistic linkage separately. Trace source records, cohort entry, treatment classification, confounders, missingness, censoring, outcome ascertainment, analysis and transportability.

Separate observation from causal interpretation

Real-world data record care as delivered; they do not automatically create a fair comparison. Design, timing, confounding, selection and measurement determine what an estimate can mean.

Respect the development boundary

A high linkage rate can still produce systematic mismatch. CREWS remains under development on its live EQUATOR record. STROBE, RECORD and current regulatory guidance are useful neighbours, not a substitute for a final CREWS checklist.

Student, parent and career transfer

Make one evidence card for “Report Data Provenance, Transformations and Linkage”: source location, plain-language meaning, limit and next question. A parent or teacher can ask for one everyday explanation and one specialist explanation; both must preserve the same conditions. This habit supports epidemiology, medicine, nursing, pharmacy, health services research, biostatistics, data engineering, clinical informatics, regulatory science, policy analysis and evidence communication without promising a grade, admission, funding decision or job.

CHAPTER 4 OF 15

4. Demonstrate Fitness for the Specific Question

Back to contents

Why this move matters

Explain completeness, timeliness, plausibility, consistency, validity and population coverage for the variables central to the decision.

Connect the care pathway to the data pathway

Explain completeness, timeliness, plausibility, consistency, validity and population coverage for the variables central to the decision. Map clinical eligibility, treatment decisions, follow-up and outcomes to source systems, extraction windows, linkages, coding rules and analytic time zero.

Audit one patient-to-estimate trail

Worked example: Shoulder function has acceptable coverage after one clinic changes software, but occupation is incomplete and treated cautiously. Trace source records, cohort entry, treatment classification, confounders, missingness, censoring, outcome ascertainment, analysis and transportability.

Separate observation from causal interpretation

Real-world data record care as delivered; they do not automatically create a fair comparison. Design, timing, confounding, selection and measurement determine what an estimate can mean.

Respect the development boundary

A database can be strong for utilisation and weak for symptoms. CREWS remains under development on its live EQUATOR record. STROBE, RECORD and current regulatory guidance are useful neighbours, not a substitute for a final CREWS checklist.

Student, parent and career transfer

Make one evidence card for “Demonstrate Fitness for the Specific Question”: source location, plain-language meaning, limit and next question. A parent or teacher can ask for one everyday explanation and one specialist explanation; both must preserve the same conditions. This habit supports epidemiology, medicine, nursing, pharmacy, health services research, biostatistics, data engineering, clinical informatics, regulatory science, policy analysis and evidence communication without promising a grade, admission, funding decision or job.

CHAPTER 5 OF 15

5. Define Eligibility and Cohort Entry Reproducibly

Back to contents

Why this move matters

State diagnosis or phenotype, look-back, washout, prior treatment, exclusions, index event and how repeated eligibility episodes are handled.

Connect the care pathway to the data pathway

State diagnosis or phenotype, look-back, washout, prior treatment, exclusions, index event and how repeated eligibility episodes are handled. Map clinical eligibility, treatment decisions, follow-up and outcomes to source systems, extraction windows, linkages, coding rules and analytic time zero.

Audit one patient-to-estimate trail

Worked example: Adults enter on the first qualifying assessment after a six-month clean period with no prior pathway exposure. Trace source records, cohort entry, treatment classification, confounders, missingness, censoring, outcome ascertainment, analysis and transportability.

Separate observation from causal interpretation

Real-world data record care as delivered; they do not automatically create a fair comparison. Design, timing, confounding, selection and measurement determine what an estimate can mean.

Respect the development boundary

Loose cohort entry can mix prevalent and new users. CREWS remains under development on its live EQUATOR record. STROBE, RECORD and current regulatory guidance are useful neighbours, not a substitute for a final CREWS checklist.

Student, parent and career transfer

Make one evidence card for “Define Eligibility and Cohort Entry Reproducibly”: source location, plain-language meaning, limit and next question. A parent or teacher can ask for one everyday explanation and one specialist explanation; both must preserve the same conditions. This habit supports epidemiology, medicine, nursing, pharmacy, health services research, biostatistics, data engineering, clinical informatics, regulatory science, policy analysis and evidence communication without promising a grade, admission, funding decision or job.

CHAPTER 6 OF 15

6. Align Time Zero, Treatment Assignment and Follow-Up

Back to contents

Why this move matters

Make eligibility, treatment classification and the start of outcome follow-up coincide or explain the design used to prevent immortal-time bias.

Connect the care pathway to the data pathway

Make eligibility, treatment classification and the start of outcome follow-up coincide or explain the design used to prevent immortal-time bias. Map clinical eligibility, treatment decisions, follow-up and outcomes to source systems, extraction windows, linkages, coding rules and analytic time zero.

Audit one patient-to-estimate trail

Worked example: Treatment strategy is assigned at the first rehabilitation visit, and follow-up starts that day. Trace source records, cohort entry, treatment classification, confounders, missingness, censoring, outcome ascertainment, analysis and transportability.

Separate observation from causal interpretation

Real-world data record care as delivered; they do not automatically create a fair comparison. Design, timing, confounding, selection and measurement determine what an estimate can mean.

Respect the development boundary

Using future information to define exposure can create guaranteed survival time. CREWS remains under development on its live EQUATOR record. STROBE, RECORD and current regulatory guidance are useful neighbours, not a substitute for a final CREWS checklist.

Student, parent and career transfer

Make one evidence card for “Align Time Zero, Treatment Assignment and Follow-Up”: source location, plain-language meaning, limit and next question. A parent or teacher can ask for one everyday explanation and one specialist explanation; both must preserve the same conditions. This habit supports epidemiology, medicine, nursing, pharmacy, health services research, biostatistics, data engineering, clinical informatics, regulatory science, policy analysis and evidence communication without promising a grade, admission, funding decision or job.

CHAPTER 7 OF 15

7. Describe Real Treatment Pathways and Changes

Back to contents

Why this move matters

Report initial choice, dose or intensity, switching, discontinuation, adherence, co-interventions and whether analysis is initiation-based, as-treated or per-protocol.

Connect the care pathway to the data pathway

Report initial choice, dose or intensity, switching, discontinuation, adherence, co-interventions and whether analysis is initiation-based, as-treated or per-protocol. Map clinical eligibility, treatment decisions, follow-up and outcomes to source systems, extraction windows, linkages, coding rules and analytic time zero.

Audit one patient-to-estimate trail

Worked example: The report distinguishes the pathway chosen at entry from later switching and missed sessions. Trace source records, cohort entry, treatment classification, confounders, missingness, censoring, outcome ascertainment, analysis and transportability.

Separate observation from causal interpretation

Real-world data record care as delivered; they do not automatically create a fair comparison. Design, timing, confounding, selection and measurement determine what an estimate can mean.

Respect the development boundary

Routine care rarely follows a single stable label. CREWS remains under development on its live EQUATOR record. STROBE, RECORD and current regulatory guidance are useful neighbours, not a substitute for a final CREWS checklist.

Student, parent and career transfer

Make one evidence card for “Describe Real Treatment Pathways and Changes”: source location, plain-language meaning, limit and next question. A parent or teacher can ask for one everyday explanation and one specialist explanation; both must preserve the same conditions. This habit supports epidemiology, medicine, nursing, pharmacy, health services research, biostatistics, data engineering, clinical informatics, regulatory science, policy analysis and evidence communication without promising a grade, admission, funding decision or job.

CHAPTER 8 OF 15

8. Define Outcomes, Windows and Ascertainment

Back to contents

Why this move matters

Give outcome construct, code or instrument, source, validation, timing window, competing events, repeated measures and assessor context.

Connect the care pathway to the data pathway

Give outcome construct, code or instrument, source, validation, timing window, competing events, repeated measures and assessor context. Map clinical eligibility, treatment decisions, follow-up and outcomes to source systems, extraction windows, linkages, coding rules and analytic time zero.

Audit one patient-to-estimate trail

Worked example: Function is measured with the same instrument in a prespecified window, while emergency visits come from linked records. Trace source records, cohort entry, treatment classification, confounders, missingness, censoring, outcome ascertainment, analysis and transportability.

Separate observation from causal interpretation

Real-world data record care as delivered; they do not automatically create a fair comparison. Design, timing, confounding, selection and measurement determine what an estimate can mean.

Respect the development boundary

An outcome may be absent because it was not measured, not because it did not occur. CREWS remains under development on its live EQUATOR record. STROBE, RECORD and current regulatory guidance are useful neighbours, not a substitute for a final CREWS checklist.

Student, parent and career transfer

Make one evidence card for “Define Outcomes, Windows and Ascertainment”: source location, plain-language meaning, limit and next question. A parent or teacher can ask for one everyday explanation and one specialist explanation; both must preserve the same conditions. This habit supports epidemiology, medicine, nursing, pharmacy, health services research, biostatistics, data engineering, clinical informatics, regulatory science, policy analysis and evidence communication without promising a grade, admission, funding decision or job.

CHAPTER 9 OF 15

9. Choose Confounders From a Causal Story

Back to contents

Why this move matters

Explain the causal diagram or clinical reasoning, measurement windows, proxies, unavailable factors and why variables are adjusted, stratified or excluded.

Connect the care pathway to the data pathway

Explain the causal diagram or clinical reasoning, measurement windows, proxies, unavailable factors and why variables are adjusted, stratified or excluded. Map clinical eligibility, treatment decisions, follow-up and outcomes to source systems, extraction windows, linkages, coding rules and analytic time zero.

Audit one patient-to-estimate trail

Worked example: Baseline severity, work demands and prior care inform adjustment, while post-treatment attendance is not treated as a baseline confounder. Trace source records, cohort entry, treatment classification, confounders, missingness, censoring, outcome ascertainment, analysis and transportability.

Separate observation from causal interpretation

Real-world data record care as delivered; they do not automatically create a fair comparison. Design, timing, confounding, selection and measurement determine what an estimate can mean.

Respect the development boundary

Adjusting for a mediator or collider can increase bias. CREWS remains under development on its live EQUATOR record. STROBE, RECORD and current regulatory guidance are useful neighbours, not a substitute for a final CREWS checklist.

Student, parent and career transfer

Make one evidence card for “Choose Confounders From a Causal Story”: source location, plain-language meaning, limit and next question. A parent or teacher can ask for one everyday explanation and one specialist explanation; both must preserve the same conditions. This habit supports epidemiology, medicine, nursing, pharmacy, health services research, biostatistics, data engineering, clinical informatics, regulatory science, policy analysis and evidence communication without promising a grade, admission, funding decision or job.

CHAPTER 10 OF 15

10. Report Missingness and Measurement Change

Back to contents

Why this move matters

Show missingness by source, time and group; distinguish structural absence from unknown values; report imputation and sensitivity to missing-not-at-random mechanisms.

Connect the care pathway to the data pathway

Show missingness by source, time and group; distinguish structural absence from unknown values; report imputation and sensitivity to missing-not-at-random mechanisms. Map clinical eligibility, treatment decisions, follow-up and outcomes to source systems, extraction windows, linkages, coding rules and analytic time zero.

Audit one patient-to-estimate trail

Worked example: Occupation is missing more often at one clinic after a form redesign, so the pattern is reported and tested. Trace source records, cohort entry, treatment classification, confounders, missingness, censoring, outcome ascertainment, analysis and transportability.

Separate observation from causal interpretation

Real-world data record care as delivered; they do not automatically create a fair comparison. Design, timing, confounding, selection and measurement determine what an estimate can mean.

Respect the development boundary

One overall completeness percentage can hide differential gaps. CREWS remains under development on its live EQUATOR record. STROBE, RECORD and current regulatory guidance are useful neighbours, not a substitute for a final CREWS checklist.

Student, parent and career transfer

Make one evidence card for “Report Missingness and Measurement Change”: source location, plain-language meaning, limit and next question. A parent or teacher can ask for one everyday explanation and one specialist explanation; both must preserve the same conditions. This habit supports epidemiology, medicine, nursing, pharmacy, health services research, biostatistics, data engineering, clinical informatics, regulatory science, policy analysis and evidence communication without promising a grade, admission, funding decision or job.

CHAPTER 11 OF 15

11. Handle Selection, Censoring and Competing Events

Back to contents

Why this move matters

Describe entry into the database, loss of coverage, transfer, death, treatment switching, informative follow-up and the assumptions behind weighting or censoring.

Connect the care pathway to the data pathway

Describe entry into the database, loss of coverage, transfer, death, treatment switching, informative follow-up and the assumptions behind weighting or censoring. Map clinical eligibility, treatment decisions, follow-up and outcomes to source systems, extraction windows, linkages, coding rules and analytic time zero.

Audit one patient-to-estimate trail

Worked example: People moving outside the network are censored with a sensitivity analysis using linked outcome data where available. Trace source records, cohort entry, treatment classification, confounders, missingness, censoring, outcome ascertainment, analysis and transportability.

Separate observation from causal interpretation

Real-world data record care as delivered; they do not automatically create a fair comparison. Design, timing, confounding, selection and measurement determine what an estimate can mean.

Respect the development boundary

Administrative end of data differs from clinical loss to follow-up. CREWS remains under development on its live EQUATOR record. STROBE, RECORD and current regulatory guidance are useful neighbours, not a substitute for a final CREWS checklist.

Student, parent and career transfer

Make one evidence card for “Handle Selection, Censoring and Competing Events”: source location, plain-language meaning, limit and next question. A parent or teacher can ask for one everyday explanation and one specialist explanation; both must preserve the same conditions. This habit supports epidemiology, medicine, nursing, pharmacy, health services research, biostatistics, data engineering, clinical informatics, regulatory science, policy analysis and evidence communication without promising a grade, admission, funding decision or job.

CHAPTER 12 OF 15

12. Report the Analysis and Diagnostics Completely

Back to contents

Why this move matters

Name models, coding, weights, matching, balance, overlap, positivity checks, variance methods, clustering, repeated measures and software.

Connect the care pathway to the data pathway

Name models, coding, weights, matching, balance, overlap, positivity checks, variance methods, clustering, repeated measures and software. Map clinical eligibility, treatment decisions, follow-up and outcomes to source systems, extraction windows, linkages, coding rules and analytic time zero.

Audit one patient-to-estimate trail

Worked example: Propensity weighting is accompanied by balance plots, effective sample size and areas of poor overlap. Trace source records, cohort entry, treatment classification, confounders, missingness, censoring, outcome ascertainment, analysis and transportability.

Separate observation from causal interpretation

Real-world data record care as delivered; they do not automatically create a fair comparison. Design, timing, confounding, selection and measurement determine what an estimate can mean.

Respect the development boundary

A successful model run does not prove assumptions hold. CREWS remains under development on its live EQUATOR record. STROBE, RECORD and current regulatory guidance are useful neighbours, not a substitute for a final CREWS checklist.

Student, parent and career transfer

Make one evidence card for “Report the Analysis and Diagnostics Completely”: source location, plain-language meaning, limit and next question. A parent or teacher can ask for one everyday explanation and one specialist explanation; both must preserve the same conditions. This habit supports epidemiology, medicine, nursing, pharmacy, health services research, biostatistics, data engineering, clinical informatics, regulatory science, policy analysis and evidence communication without promising a grade, admission, funding decision or job.

CHAPTER 13 OF 15

13. Use Sensitivity and Negative-Control Analyses Purposefully

Back to contents

Why this move matters

Connect each analysis to a named threat such as residual confounding, outcome misclassification, exposure grace periods, missingness or unmeasured care outside the network.

Connect the care pathway to the data pathway

Connect each analysis to a named threat such as residual confounding, outcome misclassification, exposure grace periods, missingness or unmeasured care outside the network. Map clinical eligibility, treatment decisions, follow-up and outcomes to source systems, extraction windows, linkages, coding rules and analytic time zero.

Audit one patient-to-estimate trail

Worked example: The study varies the exposure window and uses a negative-control outcome unlikely to be affected by rehabilitation choice. Trace source records, cohort entry, treatment classification, confounders, missingness, censoring, outcome ascertainment, analysis and transportability.

Separate observation from causal interpretation

Real-world data record care as delivered; they do not automatically create a fair comparison. Design, timing, confounding, selection and measurement determine what an estimate can mean.

Respect the development boundary

A long list of analyses can become decorative if threats are not named. CREWS remains under development on its live EQUATOR record. STROBE, RECORD and current regulatory guidance are useful neighbours, not a substitute for a final CREWS checklist.

Student, parent and career transfer

Make one evidence card for “Use Sensitivity and Negative-Control Analyses Purposefully”: source location, plain-language meaning, limit and next question. A parent or teacher can ask for one everyday explanation and one specialist explanation; both must preserve the same conditions. This habit supports epidemiology, medicine, nursing, pharmacy, health services research, biostatistics, data engineering, clinical informatics, regulatory science, policy analysis and evidence communication without promising a grade, admission, funding decision or job.

CHAPTER 14 OF 15

14. Present Absolute Results, Uncertainty and Heterogeneity

Back to contents

Why this move matters

Report counts, person-time, baseline risk, absolute and relative estimates, confidence intervals, subgroup rationale and the scale of clinically meaningful difference.

Connect the care pathway to the data pathway

Report counts, person-time, baseline risk, absolute and relative estimates, confidence intervals, subgroup rationale and the scale of clinically meaningful difference. Map clinical eligibility, treatment decisions, follow-up and outcomes to source systems, extraction windows, linkages, coding rules and analytic time zero.

Audit one patient-to-estimate trail

Worked example: The report gives adjusted one-year improvement probabilities and their difference, not only a ratio. Trace source records, cohort entry, treatment classification, confounders, missingness, censoring, outcome ascertainment, analysis and transportability.

Separate observation from causal interpretation

Real-world data record care as delivered; they do not automatically create a fair comparison. Design, timing, confounding, selection and measurement determine what an estimate can mean.

Respect the development boundary

Statistical precision is not clinical importance. CREWS remains under development on its live EQUATOR record. STROBE, RECORD and current regulatory guidance are useful neighbours, not a substitute for a final CREWS checklist.

Student, parent and career transfer

Make one evidence card for “Present Absolute Results, Uncertainty and Heterogeneity”: source location, plain-language meaning, limit and next question. A parent or teacher can ask for one everyday explanation and one specialist explanation; both must preserve the same conditions. This habit supports epidemiology, medicine, nursing, pharmacy, health services research, biostatistics, data engineering, clinical informatics, regulatory science, policy analysis and evidence communication without promising a grade, admission, funding decision or job.

CHAPTER 15 OF 15

15. Discuss Transportability, Governance and Reuse

Back to contents

Why this move matters

Compare study and target populations, settings and periods; report privacy, approvals, data access, code, conflicts, stakeholder involvement and what future validation is needed.

Connect the care pathway to the data pathway

Compare study and target populations, settings and periods; report privacy, approvals, data access, code, conflicts, stakeholder involvement and what future validation is needed. Map clinical eligibility, treatment decisions, follow-up and outcomes to source systems, extraction windows, linkages, coding rules and analytic time zero.

Audit one patient-to-estimate trail

Worked example: The conclusion applies to participating clinics and identifies what must be checked before another health system adopts it. Trace source records, cohort entry, treatment classification, confounders, missingness, censoring, outcome ascertainment, analysis and transportability.

Separate observation from causal interpretation

Real-world data record care as delivered; they do not automatically create a fair comparison. Design, timing, confounding, selection and measurement determine what an estimate can mean.

Respect the development boundary

Large multi-site data do not guarantee national representativeness. CREWS remains under development on its live EQUATOR record. STROBE, RECORD and current regulatory guidance are useful neighbours, not a substitute for a final CREWS checklist.

Student, parent and career transfer

Make one evidence card for “Discuss Transportability, Governance and Reuse”: source location, plain-language meaning, limit and next question. A parent or teacher can ask for one everyday explanation and one specialist explanation; both must preserve the same conditions. This habit supports epidemiology, medicine, nursing, pharmacy, health services research, biostatistics, data engineering, clinical informatics, regulatory science, policy analysis and evidence communication without promising a grade, admission, funding decision or job.

PRACTICE LAB

Build a CREWS real-world study reporting decision trail

Student route

1. Choose one original report inside this guide’s scope.
2. Copy its objective and underline actor, action, context, evidence and intended decision.
3. Draw the specific reasoning chain used in this article.
4. Mark each step reported, inferred, contested or unknown.
5. Rewrite the conclusion so it preserves the narrowest important limitation.

Parent or teacher route

Ask the learner to explain how English helps us report a crews real-world study so its data origins, clinical pathway, comparison, uncertainty and limits can be followed from records to decision twice: once to a classmate and once to a method specialist. Praise a precise correction and a well-stated unknown; both show disciplined attention to evidence.

Early-career route

Create a one-page CREWS real-world study reporting audit with source link, date checked, scope, method logic, decisive wording, contrary evidence, limit and next action. A colleague should be able to continue without guessing.

Continue with eduKate’s guides to reading course and career information and giving useful feedback in group projects.

USEFUL NEXT READING

Continue with a related guide

Read the PRISMA-S literature-search guide for a related but different reader job. Return here when the question is specifically how to report a crews real-world study so its data origins, clinical pathway, comparison, uncertainty and limits can be followed from records to decision. Use the linked guide for its separate reader task, and this guide for the question defined here.

For English practice across research, school and career communication, visit the Singapore English Tuition Centre learning site.

FREQUENTLY ASKED QUESTIONS

Questions students and parents often ask

Is CREWS final?

No. EQUATOR still lists the project under development.

Are real-world data the same as real-world evidence?

Real-world data are records from routine contexts; evidence is the result of analysing suitable data with a defensible design.

Can adjustment remove all confounding?

No. It depends on measured variables, correct design and assumptions, with residual confounding often remaining.

Why is time zero important?

Misalignment between eligibility, treatment and follow-up can create serious bias.

Does a large sample guarantee validity?

No. Large samples can estimate a biased comparison very precisely.

What makes results transportable?

Similarity of populations, care pathways, measurement and effect mechanisms, supported by explicit evidence and limits.