# Observational Study Report: [Short title of the study]

> Report template for cohort, case-control and cross-sectional studies.
> The sections follow the topics of the STROBE Statement (title and
> abstract, introduction, methods, results, discussion, other
> information), written in original words for this collection. Use it with
> the official STROBE checklist for your design from strobe-statement.org.
> This is a reporting format, not clinical, epidemiological or statistical
> advice. Replace every `[bracketed]` value and every row marked *Example*.

<!--
Guidance: How to use this template
- Choose the design first (cohort, case-control or cross-sectional) and
  delete the rows that do not apply. Several items differ by design.
- Fill sections 3.4 (variables) and 3.8 (statistical methods) before
  writing results; they define every number you will report.
- Report both unadjusted and adjusted estimates, say which confounders
  were adjusted for and why they were chosen.
- Use a STROBE extension if one fits (RECORD for routinely collected data,
  STROBE-MR, STREGA for genetic association studies).
- Never include identifying data. Follow local privacy law (for example
  HIPAA or GDPR) and your ethics approval and data-use agreements.
-->

## Document control

<!-- Guidance: Track the manuscript or study report as a controlled document. -->

| Field | Value |
|---|---|
| Document ID | [OBS-STUDY-001] |
| Study design | [Cohort / Case-control / Cross-sectional] |
| Protocol or registration | [Registry, OSF link or "not registered"] |
| Ethics reference | [REC / IRB number or exemption] |
| Version | [0.1] |
| Status | [Draft / In review / Approved] |
| Owner | [@principal-investigator] |
| Approvers | [@study-statistician], [@co-investigator] |
| Last updated | YYYY-MM-DD |
| Classification | [Confidential until publication] |

### Approvals

| Role | Name | Decision | Date |
|---|---|---|---|
| Principal investigator | [@name] | [Approved] | YYYY-MM-DD |
| Study statistician | [@name] | [Approved] | YYYY-MM-DD |
| Data custodian | [@name] | [Approved for release of aggregate results] | YYYY-MM-DD |

## Revision history

<!-- Guidance: Record each draft circulated to co-authors and what changed in the analysis. -->

| Version | Date | Author | Change summary |
|---|---|---|---|
| 0.1 | YYYY-MM-DD | [@name] | First draft after analysis |
| 0.2 | YYYY-MM-DD | [@name] | *Example:* Added E-value sensitivity analysis for unmeasured confounding |
| 1.0 | YYYY-MM-DD | [@name] | Approved for submission |

## Contents

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

1. [Title and abstract](#1-title-and-abstract)
2. [Introduction](#2-introduction)
3. [Methods](#3-methods)
4. [Results](#4-results)
5. [Discussion](#5-discussion)
6. [Other information](#6-other-information)
7. [Appendices](#7-appendices)

## 1. Title and abstract

<!-- Guidance: Name the design with a commonly used term in the title or abstract. The abstract summarises what was done and found in a balanced way. -->

The title states the design; the abstract gives an informative, balanced
summary.

**Title:** [Exposure] and [outcome] in [population]: a [prospective
cohort / population-based case-control / cross-sectional] study

| Heading | Content |
|---|---|
| Objective | [Research question in one sentence] |
| Design and setting | [Design, data source, country, years] |
| Participants | [Who and how many] |
| Exposure and outcome | [Definitions in brief] |
| Results | *Example:* Among 18,240 adults followed for a median 6.1 years, 1,032 developed [outcome]. The adjusted hazard ratio for [exposure] was 1.34 (95% CI 1.15 to 1.56). |
| Conclusions | [Interpretation proportionate to the design] |

## 2. Introduction

<!-- Guidance: Explain the scientific background and why the question matters, then state specific objectives and any pre-specified hypotheses. -->

The introduction explains the background and states what the study set
out to find.

### 2.1 Background and rationale

[Summarise what is known about the exposure-outcome relationship, the
gaps or inconsistencies in the evidence and why an observational design
is appropriate.]

### 2.2 Objectives

| ID | Objective | Pre-specified hypothesis |
|---|---|---|
| OBJ-01 | *Example:* Estimate the association between [exposure] and incident [outcome] | Higher exposure is associated with higher risk |
| OBJ-02 | [Secondary objective] | [Hypothesis or "exploratory"] |

## 3. Methods

<!-- Guidance: Describe the study in enough detail for others to judge validity and reproduce it. Present key design elements early. -->

This section explains how the study was designed and analysed.

### 3.1 Study design

[State the design and its key elements, for example: prospective cohort
nested within [cohort name], with exposure measured at baseline and
outcome ascertained through linkage to [registry].]

The design determines which measures and checklist items apply:

| Design | Participants sampled by | Typical measure | Key validity question |
|---|---|---|---|
| Cohort | Exposure status, then followed for outcome | Risk ratio, rate ratio, hazard ratio | Was follow-up complete and similar across exposure groups? |
| Case-control | Outcome status, then exposure measured | Odds ratio | Do controls represent the population that produced the cases? |
| Cross-sectional | Neither; one point in time | Prevalence, prevalence ratio, odds ratio | Can the order of exposure and outcome be established? |

### 3.2 Setting

<!-- Guidance: Give locations, relevant dates and periods of recruitment, exposure, follow-up and data collection. -->

| Item | Detail |
|---|---|
| Location | [Country, region, facilities] |
| Recruitment period | YYYY-MM-DD to YYYY-MM-DD |
| Exposure assessment period | [Dates] |
| Follow-up period | *Example:* From baseline until outcome, death, emigration or 2024-12-31, whichever came first |
| Data collection | [Dates] |

### 3.3 Participants

<!-- Guidance: The required detail differs by design. Fill the row for your design and delete the others. -->

| Design | Item | Detail |
|---|---|---|
| Cohort | Eligibility, sources and selection | [Criteria and methods] |
| Cohort | Follow-up methods | [Active follow-up, linkage] |
| Cohort (matched) | Matching criteria, number exposed and unexposed | [Criteria] |
| Case-control | Case ascertainment and diagnosis | [Definition and source] |
| Case-control | Control selection and rationale | *Example:* Four population controls per case, matched on age (+/- 2 years), sex and region, sampled by incidence density |
| Cross-sectional | Eligibility, sources and selection | [Criteria and sampling frame] |

### 3.4 Variables

<!-- Guidance: Define every outcome, exposure, predictor, potential confounder and effect modifier. Give diagnostic criteria where relevant. -->

| ID | Variable | Role | Definition | Type |
|---|---|---|---|---|
| VAR-01 | *Example:* Incident type 2 diabetes | Outcome | First diagnosis code [codes] or HbA1c of at least 48 mmol/mol | Time to event |
| VAR-02 | [Exposure] | Exposure | [Definition, categories] | [Categorical] |
| VAR-03 | Age | Confounder | Years at baseline | Continuous |
| VAR-04 | [Smoking status] | Confounder | [Never / former / current] | Categorical |
| VAR-05 | [Sex] | Effect modifier | [Recorded sex] | Binary |

A directed acyclic graph helps justify the confounder set:

```mermaid
flowchart LR
    C["Confounders (age, smoking, SES)"] --> E[Exposure]
    C --> O[Outcome]
    E --> O
    E --> M["Mediator (not adjusted)"]
    M --> O
```

### 3.5 Data sources and measurement

<!-- Guidance: For each variable of interest, give the source and method of measurement. Describe comparability of assessment across groups. -->

| Variable | Source | Measurement method | Comparability across groups |
|---|---|---|---|
| [Exposure] | [Questionnaire / records / biomarker] | [Instrument, validation reference] | [Same method for all] |
| [Outcome] | [Registry / adjudication] | [Coding system, adjudication process] | *Example:* Adjudicators blind to exposure status |
| [Confounder] | [Source] | [Method] | [Comment] |

### 3.6 Bias

<!-- Guidance: Describe efforts to address each potential source of bias. Name the bias, its likely direction and the mitigation. -->

| Bias | How it could arise | Mitigation | Likely direction if residual |
|---|---|---|---|
| Selection bias | [Healthy volunteer effect] | [Population-based sampling] | [Towards null] |
| Information bias | *Example:* Recall of exposure differs between cases and controls | Exposure taken from records made before diagnosis | Not applicable |
| Confounding | [Unmeasured lifestyle factors] | [Adjustment; E-value] | [Away from null] |
| Loss to follow-up | [Differential attrition] | [Linkage-based follow-up] | [Unclear] |

### 3.7 Study size

<!-- Guidance: Explain how the study size was arrived at. For fixed data sources, report the precision achievable rather than a post-hoc power calculation. -->

[Explain how the size was determined. For example: all eligible members
of the cohort were included; with an expected 1,000 events, the study
could detect a hazard ratio of 1.20 with 80% power at a two-sided alpha
of 0.05.]

### 3.8 Quantitative variables

<!-- Guidance: Explain how continuous variables were handled. Avoid arbitrary cut-points; if you categorise, justify the boundaries. -->

| Variable | Handling | Rationale |
|---|---|---|
| [Age] | *Example:* Restricted cubic spline with 4 knots | Non-linear relationship expected |
| [Exposure] | [Quartiles of the distribution in controls] | [Rationale] |
| [Body mass index] | [WHO categories] | [Clinical convention] |

### 3.9 Statistical methods

<!-- Guidance: Describe all methods including those for confounding, subgroups and interactions, missing data, loss to follow-up or matching, sampling strategy and sensitivity analyses. -->

The measures of association used depend on design. From a 2 x 2 table
with exposed cases $a$, exposed non-cases $b$, unexposed cases $c$ and
unexposed non-cases $d$:

$$
\text{RR} = \frac{a/(a+b)}{c/(c+d)}, \qquad \text{OR} = \frac{a\,d}{b\,c}
$$

The 95% confidence interval for the odds ratio (Woolf method) is:

$$
\exp\!\left(\ln \text{OR} \pm 1.96\sqrt{\tfrac{1}{a} + \tfrac{1}{b} + \tfrac{1}{c} + \tfrac{1}{d}}\right)
$$

| Item | Method |
|---|---|
| Main model | *Example:* Cox proportional hazards with age as the time scale; proportional hazards checked with Schoenfeld residuals |
| Confounder adjustment | [Covariates chosen from the DAG in 3.4] |
| Subgroups and interactions | [Pre-specified; likelihood ratio test] |
| Missing data | [Multiple imputation by chained equations, 20 data sets] |
| Loss to follow-up / matching | [Censoring rules; conditional logistic regression for matched sets] |
| Survey sampling | [Weights, strata, clusters for cross-sectional surveys] |
| Sensitivity analyses | [Complete case; excluding first 2 years of follow-up; E-value] |
| Software | [R / Stata / SAS version] |

## 4. Results

<!-- Guidance: Report numbers at each stage, then descriptive data, outcome data, main results and other analyses. Give absolute numbers alongside relative measures. -->

This section reports who was studied and what was found.

### 4.1 Participants

<!-- Guidance: Report numbers at each stage of the study and reasons for non-participation. A flow diagram is recommended. -->

The flow diagram shows numbers at each stage of the study.

```mermaid
flowchart TD
    A["Potentially eligible (n = [24,105])"] --> B["Excluded (n = [5,865])"]
    A --> C["Included at baseline (n = [18,240])"]
    C --> D["Lost to follow-up (n = [412])"]
    C --> E["Included in main analysis (n = [17,828])"]
```

| Stage | n | Reasons for exclusion or non-participation |
|---|---|---|
| Potentially eligible | [24,105] | |
| Excluded | [5,865] | *Example:* 3,210 prevalent outcome at baseline; 1,904 missing exposure; 751 declined |
| Included | [18,240] | |
| Analysed | [17,828] | [Reasons] |

### 4.2 Descriptive data

<!-- Guidance: Describe participant characteristics by exposure group (cohort) or case status (case-control). Show missing data counts for each variable and summarise follow-up time. -->

| Characteristic | Exposed (n = [x]) | Unexposed (n = [y]) | Missing, n |
|---|---|---|---|
| Age, years, mean (SD) | *Example:* 57.3 (8.9) | 55.1 (9.4) | 0 |
| Female, n (%) | [Value] | [Value] | [0] |
| Current smoker, n (%) | [Value] | [Value] | [214] |
| [Confounder] | [Value] | [Value] | [n] |

Follow-up: [median (IQR) years and total person-years].

### 4.3 Outcome data

<!-- Guidance: Cohort: numbers of outcome events or summary measures over time. Case-control: numbers in each exposure category. Cross-sectional: numbers of outcome events or summary measures. -->

| Group | Events / participants | Person-years | Rate per 1,000 person-years |
|---|---|---|---|
| Exposed | *Example:* 612 / 8,904 | 53,420 | 11.5 |
| Unexposed | 420 / 8,924 | 55,310 | 7.6 |

### 4.4 Main results

<!-- Guidance: Give unadjusted and confounder-adjusted estimates with precision. State which confounders were adjusted for. Translate relative risk into absolute risk for a meaningful time period where relevant. -->

| Model | Estimate (95% CI) | Adjusted for |
|---|---|---|
| Unadjusted | *Example:* HR 1.52 (1.34 to 1.73) | None |
| Model 1 | HR 1.41 (1.24 to 1.61) | Age, sex |
| Model 2 (main) | HR 1.34 (1.15 to 1.56) | Model 1 plus smoking, socioeconomic status, [others] |

Category boundaries for categorised continuous variables: [list].
Absolute risk difference over [10 years]: [value (95% CI)].

### 4.5 Other analyses

<!-- Guidance: Report subgroup, interaction and sensitivity analyses, and say which were pre-specified. -->

| Analysis | Pre-specified? | Result | Comment |
|---|---|---|---|
| By sex | Yes | [HR women 1.29; men 1.40; interaction P = 0.52] | No effect modification |
| Excluding first 2 years | Yes | [HR 1.31 (1.11 to 1.54)] | Reverse causation unlikely |
| E-value for main estimate | No | [2.02] | [Interpretation] |

## 5. Discussion

<!-- Guidance: Summarise key results with reference to objectives, discuss limitations including direction and size of bias, and interpret cautiously, especially regarding causation. -->

The discussion interprets the findings with appropriate caution.

### 5.1 Key results

[Summarise the main findings with reference to the study objectives.]

*Example:* In this population-based cohort, adults with [exposure] had a
34% higher adjusted rate of [outcome] than those without, corresponding
to about 3.9 additional cases per 1,000 person-years. The association was
consistent across sex and age groups and persisted after excluding the
first two years of follow-up.

### 5.2 Limitations

- [Source of potential bias or imprecision, its likely direction and magnitude]
- [Residual and unmeasured confounding]
- [Measurement error in exposure, for example a single baseline measurement that may not reflect long-term exposure, which usually biases estimates towards the null]
- [Missing data and the plausibility of the missing-at-random assumption]
- [Multiple comparisons across secondary outcomes and subgroups]

For each limitation, state whether it is likely to have inflated or
attenuated the main estimate and, where possible, by how much. A
quantitative bias analysis or an E-value is more informative than a
general statement that confounding cannot be excluded.

### 5.3 Interpretation

[Give a cautious overall interpretation considering objectives,
limitations, multiplicity of analyses, results from similar studies and
other relevant evidence. Avoid causal language unless justified.]

### 5.4 Generalisability

[Discuss the external validity of the results: to which populations and
settings they apply.]

Consider how the study population differs from the target population in
age, ethnicity, healthcare access and baseline risk; whether the exposure
definition matches how the exposure occurs in practice; and whether the
calendar period of the study still reflects current care. Distinguish
between transporting a relative effect, which may hold across settings,
and an absolute effect, which depends on local baseline risk.

## 6. Other information

<!-- Guidance: Funding and its role are required. Add data availability and conflicts of interest as your journal requires. -->

Transparency information for readers:

| Item | Detail |
|---|---|
| Funding | [Funder, grant number] |
| Role of funder | [Role in design, analysis, interpretation, publication, or none] |
| Original study funding | [If the present article is based on an earlier study] |
| Ethics | [Approval reference, consent or waiver] |
| Data availability | [Access route, conditions, governance] |
| Code availability | [Repository link] |
| Conflicts of interest | [Statement] |

## 7. Appendices

<!-- Guidance: Supplementary material reviewers need to check your work. -->

Supporting material for reviewers:

| Appendix | Content |
|---|---|
| A | Completed STROBE checklist for the design, with page numbers |
| B | Code lists for exposure, outcome and covariates |
| C | Full model output and sensitivity analyses |
| D | Glossary: [CI confidence interval, DAG directed acyclic graph, HR hazard ratio, OR odds ratio, RR relative risk] |

## Standard and licence note

Based on the STROBE Statement: von Elm E, Altman DG, Egger M, Pocock SJ,
Gøtzsche PC, Vandenbroucke JP, for the STROBE Initiative, *The
Strengthening the Reporting of Observational Studies in Epidemiology
(STROBE) statement: guidelines for reporting observational studies*
(PLoS Med 2007;4(10):e296 and parallel publications),
https://www.strobe-statement.org. The checklists are published open
access; this template uses their item topics only, paraphrased in
original text, and reproduces no checklist wording. Use the official
checklist for your design when submitting. This is a reporting format,
not clinical, epidemiological or statistical advice. Template licence:
MIT.