Prospective observational cohort
How do outcomes develop in participants followed forward?
When to use this project
Use a prospective observational cohort when participants can be enrolled and measurements collected forward under a protocol while exposures and care are observed. Plan measurement quality and follow-up before the outcomes of interest occur.
How it differs
Compared with a retrospective cohort, prospective collection can tailor measurements but requires recruitment and retention. It is not a randomized trial: the study does not randomly allocate the exposure. Causal comparisons still require explicit assumptions and confounding control.
Decisions before you start
- Define enrollment, baseline and start of risk time.
- Distinguish usual-care exposure decisions from interventions assigned by the research protocol.
- Prespecify visit windows, outcome capture, consent, loss to follow-up and repeated-measure analysis.
How the flows work together
Each flow below links to its diagram, steps and tables. Flow order organizes the project; it does not imply that all work is strictly sequential. Data assessment, analysis planning and protocol refinement may inform one another.
1. Cohort overview
Overview · Question through prospective study reporting
Use this overview to state the purpose and link to detailed decisions. Keep a versioned protocol and decision register, and add evidence references for the rationale.
2. Recruitment and enrollment
Selection · Track approach consent and enrollment
Document who could be approached and why some people did not enroll. Reconcile mutually exclusive statuses at the same cutoff; pending recruitment is not a final exclusion. Consent and screening order depend on the approved protocol.
3. Endpoints and measurements
Design · Define exposures outcomes and assessments
Prospective observation allows planned measurements but does not randomize care. Define instruments, outcome windows and exposure updates consistently across visits and sites.
4. Follow-up and disposition
Conduct · Track visits losses and completion
Missed measurements, withdrawal, loss of contact, treatment cessation and death have different meanings. Retain timing and reasons, and match the handling of each event to the estimand.
5. Data preparation and finalization
Data · Resolve queries and derive datasets
Freeze a reproducible version after resolving data queries and documenting derivations. A frozen dataset may still have limitations and missing values; amendments require a new version and an audit trail.
6. Analysis and missingness plan
Statistics · Specify estimates and attrition handling
Repeated measurements from one person are dependent. Address measurement timing, attrition and clustering, and distinguish descriptive trajectories from causal exposure comparisons.
Equations and statistical interpretation
These explanations describe notation and assumptions. Flow does not calculate these values.
Survival function
T is time from the specified origin to the event, and S(t) is the probability of remaining event-free beyond t.
Estimation requires a censoring assumption appropriate to the method. For a particular cause in the presence of competing events, use a cumulative incidence estimand rather than interpreting one minus cause-specific Kaplan–Meier survival as its risk.
Published example
Doll et al. (2004), Mortality in relation to smoking: 50 years’ observations on male British doctors
Follow how exposure questionnaires and long-term mortality follow-up define a prospective cohort. Its exposure comparisons also require consideration of confounding and changes in smoking over time.
This publication illustrates a study design, not an endorsement of every method or a reproduction of its results. Follow its protocol, supplement and update history when studying the example.
Hypothetical scenario
A clinic network will follow adults newly diagnosed with diabetes for two years to describe changes in kidney function under routine care.
- Cohort overview: Define the eligible diagnosis cohort and two-year outcomes.
- Recruitment and enrollment: Record approached, screened and enrolled people, including reasons for nonparticipation.
- Endpoints and measurements: Specify baseline kidney function, exposure updates, assays and assessment windows.
- Follow-up and disposition: Track visits, withdrawal, deaths and missed measurements without treating them as equivalent.
- Data preparation and finalization: Resolve queries and version derived kidney-function measures before analysis.
- Analysis and missingness plan: Describe trajectories and uncertainty with a justified plan for repeated measurements and informative dropout.
Deliver a populated protocol, decision log, data specification, and planned outputs. Enter numerical findings only after analysis and verification. Keep decisions made before seeing results distinguishable from later amendments.
Sources
- STROBE checklist for cohort studies
- HMA–EMA Catalogue of RWD studies: registration and protocol guidance
- Hernán and Robins: Causal Inference—What If
Source check: 2026-09-17. Record the version of each reporting or appraisal tool used in your actual protocol.
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