Plan your study

Before opening R - define your question, cohort, and data model

Published

September 9, 2026

Register-based research does not start in R. It starts with pen and paper. This page guides you through the things you should have in place before writing a single line of code.

In short: Settle four things on paper before you code - a precise research question, your data model (which registers cover exposure, outcome and covariates), your covariates chosen with a DAG, and your comparison cohort.

What type of study are you doing?

Almost all register-based research is observational and analytical: you observe what has already happened, without intervening. Registers record the past, so you cannot randomise anyone. The two classic analytical designs are case-control and cohort; Phase 10 shows how to build a matched cohort study.

Case-control or cohort - what is the difference?

The two analytical designs differ in which end you start from:

Cohort Case-control
Starting point Exposure Outcome
Direction Follows forward: exposed → outcome Looks back: case → prior exposure
Best when Exposure is rare; multiple outcomes Outcome is rare; single outcome
Effect measure Incidence, relative risk (RR), hazard ratio Odds ratio (OR)
In registers Define exposed group + comparator cohort, follow forward Find all cases, select controls, look back at exposure

Cohort follows persons forward in time from the index date and measures how many develop the outcome - which is why you can compute incidence and risk. Well suited when you have multiple outcomes (cf. the all_dx approach in Extract from LPR).

Case-control starts from those who already have the outcome and matches them with controls without it - efficient for rare outcomes, but cannot compute absolute risk.

With register data you can do both, because the entire population’s history is available. Phase 10 shows a matched cohort study step by step.

Key concepts

Before planning a study it is worth knowing these terms - they are used throughout the guide.

Cohort A group of people followed over time because they share a particular characteristic at a particular point in time. Example: all patients who underwent bariatric surgery in the period 2010–2020.

Index date The start date of follow-up - the point from which you begin counting. For operated patients this is typically the date of surgery. For matched comparators the same date as the matched operated patient is assigned.

Exposure The factor whose effect you are investigating - e.g. a surgery, a medication, or a diagnosis.

Outcome What you are measuring - e.g. onset of a disease, a hospitalisation, or death.

Covariates Variables you include to account for confounding - factors that affect both exposure and outcome. Examples: age, sex, comorbidity, socioeconomic status.

1. What do I want to investigate?

Formulate your research question precisely before looking at any data. A vague question produces a messy dataset. A precise question produces a clear plan.

Ask yourself:

Question Example
Who is my population? All adults with T2D in Denmark, 2010–2020
What is my exposure? Bariatric surgery
What is my outcome? Dementia
When does follow-up start? Date of surgery (index date)
When does it end? Diagnosis, death, emigration, or end of study period
Which confounders should be adjusted for? Age, sex, comorbidity, SES

Make sure follow-up starts at the same moment eligibility and exposure are decided. When those drift apart you get immortal time bias.

2. Which registers cover what?

Before mapping your data model it is useful to know which registers exist.

What do you need to find? Register
Demographics (age, sex, residence) BEF - Population Register
Hospital diagnoses and contacts LPR - National Patient Register (LPR2 + LPR3)
Dispensed prescriptions LMDB - Prescription Register
Date of death (for censoring) DOD - Death Register
Emigration (for censoring) VNDS - Migration Register
Education UDDA - Education Register
Income FAIK - Family Income Register
Employment AKM - Labour Classification Module

A complete description of all registers with column names and join keys is in Overview of registers →

3. Choose your covariates using a DAG

Which variables should you adjust for? The answer is not “as many as possible”. Adjusting for the wrong variables can introduce bias rather than remove it.

A DAG (directed acyclic graph - a causal diagram) is a drawing of your assumptions about how exposure, outcome and other variables relate to each other. It makes your assumptions explicit and helps you choose the right set of covariates.

Rules of thumb:

  • Adjust for confounders: variables that affect both exposure and outcome (e.g. age, comorbidity).
  • Do NOT adjust for mediators: variables that lie on the causal pathway between exposure and outcome (this removes part of the effect you want to measure).
  • Do NOT adjust for colliders: common effects of two variables (this opens a spurious association).

You do not have to work the rules out by hand. dagitty.net takes your drawing and returns the minimal adjustment set: the smallest group of variables that removes the confounding without opening a new bias through a collider.

Example: surgery and dementia - a DAG with confounder, mediator and collider

A concrete example: does surgery affect the risk of dementia?

Causal diagram with five variables: surgery (exposure), dementia (outcome), age (confounder), delirium (mediator) and hospitalisation (collider).
Figure 1: DAG of surgery → dementia with a confounder (age), a mediator (delirium) and a collider (hospitalisation).
  • Age is a confounder - it affects both the probability of surgery and of dementia. Adjust for it.
  • Delirium (post-operative delirium) is a mediator - it lies on the path surgery → delirium → dementia. Do not adjust - that removes part of the effect you want to measure.
  • Hospitalisation is a collider - both surgery and dementia lead to hospitalisation. Do not adjust - it opens a spurious association.

You can paste the model straight into dagitty.net and have the minimal adjustment set computed:

dag {
  Age            [pos="0,-1"]
  Surgery        [exposure, pos="-1.5,0"]
  Delirium       [pos="0,0"]
  Dementia       [outcome,  pos="1.5,0"]
  Hospitalisation [pos="0,1"]
  Age      -> Surgery
  Age      -> Dementia
  Surgery  -> Delirium
  Delirium -> Dementia
  Surgery  -> Hospitalisation
  Dementia -> Hospitalisation
}

For this DAG the minimal adjustment set is {Age} - you only need to adjust for age.

Tools

4. The comparator cohort

Many studies compare an exposed group with a comparator cohort. How you build it is a design decision to be made on paper - before writing code. At this stage you only need to settle four things:

  • Who is an appropriate comparator? An active comparator (unexposed people with the same indication - reduces confounding by indication), or a matched background population (maximal contrast).
  • What decides the comparator’s index date, since the exposure does not give them one.
  • Which variables you match on, and at what ratio (e.g. age, sex and calendar year, 1:5).
  • What happens if a comparator becomes exposed later.

→ Each of these, plus eligibility at index, the shared exclusions and the immortal-time trap, is worked through in Phase 10 - Build your study population and Comparison cohort.

5. Get an overview - pen and paper

Before opening R, answer these questions in writing:

  1. Which variables do I need? (patient information - age, sex, diagnoses etc. - and for which years)
  2. Which registers contain this information? (LPR, BEF, LMDB, …)
  3. In what order should data be assembled? (define population → extract outcome → extract covariates)

A solid overview on paper saves many hours of debugging in code.

Example: overview for a dementia study 
Population:   Adults who have undergone bariatric surgery (identified via the Danish Obesity
              Treatment Database - DBSO), 2010–2024
              Matched comparators from the Population Register (BEF)

Outcome:      First dementia diagnosis (LPR - ICD-10: F00–F03, G30–G31)
              Date: first contact with a dementia code after the surgery date

Covariates:   Age and sex (BEF)
              Comorbidity (LPR - 5-year lookback, i.e. diagnoses in the 5 years before index date)
              Education (UDDA)
              Income (FAIK via BEF familie_id)
              Employment (AKM)

Censoring:    Death (DOD)
              Emigration (VNDS)
              End of study period (31 Dec 2024)

6. Write an analysis plan

An analysis plan is a document you write before looking at your data. It forces you to commit to design, statistics and variables before results can colour your decisions.

Use the STROBE checklist as a skeleton: STROBE Statement - checklists →

For register-based studies, RECORD extends STROBE with items on routinely collected data, and RECORD-PE covers pharmacoepidemiology specifically: RECORD-PE (EQUATOR Network) →

Pre-register your analysis plan on e.g. OSF - this is good scientific practice and required by many journals: Open Science Framework - registration templates

Power and sample size. Even large registers have limited power (the ability to detect an effect that is really there) for rare outcomes or small subgroups. Already in the plan, consider the smallest effect you could meaningfully detect with your expected number of events. The pwr package does simple power/sample-size calculations; for survival and rate designs it is often the number of events (not the number of people) that drives your power.

7. Next steps

Once you have your overview in place:

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