Case-control

Nested case-control - sample controls for your cases

Published

September 9, 2026

This page continues from Build your study population. In a nested case-control study, your group from the landing page is instead your cases (those who developed the outcome), and their index date is the outcome date. For each case you sample a number of controls: people without the outcome who were at risk exactly when the case had the outcome.

WarningStructural guidance, not verified code

The examples show the shape of each step. They have not yet been run end to end on real register data, so treat them as a starting point: check the column names against your own delivery, and check the logic against your analysis plan. Confirm too that heaven::incidenceMatch() and Epi::ccwc() are available on your project.

Cohort or case-control? Both designs can answer the same question; case-control is especially efficient when the outcome is rare, or when a covariate is expensive to extract for the whole cohort. See Phase 1 - what type of study for the design choice.

Case-control vs. nested case-control - what is the difference?
  • Classic case-control: you find cases (with the outcome) and select controls (without the outcome) from the population, often without a defined cohort with follow-up behind it. Exposure is typically ascertained retrospectively (e.g. interview) - vulnerable to recall and selection bias, and choosing controls is hard.
  • Nested case-control: the case-control study sits inside a defined cohort (e.g. a registry cohort followed over time). Cases are those who develop the outcome during follow-up; controls are sampled from those who were at risk (outcome-free) exactly when the case had the outcome (risk-set / incidence-density sampling). Exposure comes from registry data collected before the outcome → less recall bias.

In register research at DST you almost always do the nested variant: the cohort already exists in the registers. Advantages: exposure is measured beforehand, and the odds ratio from incidence-density sampling estimates the rate/hazard ratio without a rare-outcome assumption. That is what this page shows.

The principle: incidence-density sampling

Controls are selected by risk-set / incidence-density sampling: for each case you draw controls who, on the case’s index date (= the outcome date), (1) have not yet developed the outcome and (2) meet the inclusion criteria. A person can be a control for one case and later become a case themselves - that is correct in this design (and is exactly what the risk set captures).

It is the same mechanic as the matching in Comparison cohort - only the “event” has changed: here it is the outcome (case), not an exposure. So this page also reuses the person table from there.

Step 1 - Build the dataset for matching

(Steps 1 and 2 - identify cases and exclude prevalent cases - are on the landing page.)

You need one row per person with matching variables and the dates eligibility depends on. Build it exactly like the pool in Comparison cohort, Steps 3-4 (demographics + earliest_bef + death date + emigration date + lookback). For case-control, though:

  • Exposure is not an eligibility criterion here (unlike the comparison cohort, where a control must be exposure-free at index). In case-control, exposure is exactly what you study: you measure it for both cases and controls afterwards and do not use it to decide who can be a control. A control just needs to be outcome-free (and meet your other criteria) at the case’s index.
  • You need to know who is a case (developed the outcome) and when (outcome date = index).
Show the code: build the dataset for matching 
# Step 1: build the dataset for matching ---------------------------------------
# 'population' = one row per person from Comparison cohort (pnr, sex, birth_year,
#                death_date, emigration_date, earliest_bef_year). See Comparison cohort, Steps 3-4.
STUDY_END <- as.Date("2022-12-31")                # we choose: last day of the study
outcome <- readRDS("path/to/outcome_dates.rds")   # pnr, first_outcome_date (FIRST outcome; NA = never)

all_persons <- population %>%
  left_join(outcome, by = "pnr") %>%              # add outcome date (NA = never outcome)
  transmute(                                       # keep ONLY the columns we name
    pnr,
    case       = if_else(!is.na(first_outcome_date), 1L, 0L),  # 1 = case (had outcome), 0 = potential control
    sex        = as.factor(sex),                   # matching variable MUST be factor/character
    birth_year = as.factor(birth_year),            # matching variable
    case_index = first_outcome_date,               # the case index = outcome date (NA if never)
    # end_fu = when the person leaves the risk set: the EARLIEST of death, emigration,
    #          own outcome (they become a case then) or study end. pmin = earliest.
    end_fu     = pmin(death_date, emigration_date, first_outcome_date, STUDY_END, na.rm = TRUE)
  )

Step 2 - Sample controls

Two routes. Both do incidence-density sampling; pick one.

Route A - heaven::incidenceMatch() (sibling of exposureMatch() from Comparison cohort, but the “event” is the outcome).

Show the code (Route A): sample with incidenceMatch() 
# Step 2 (Route A): sample with incidenceMatch() -------------------------------
library(heaven)   # incidenceMatch

# Argument (left of =) <- value we set (usually a column name in quotes)
matched <- incidenceMatch(
  ptid         = "pnr",                  # the person-id column
  event        = "case",                 # the column that is 1 for case (outcome), 0 for potential control
  terms        = c("sex", "birth_year"), # matching variables (exact match; factor/character)
  data         = all_persons,            # the dataset from Step 1
  n.controls   = 5,                      # controls per case
  case.index   = "case_index",           # the case's index date (the outcome date)
  end.followup = "end_fu",               # when a person stops being eligible as a control
  seed         = 20260620                # reproducibility
)
# Output: data.table with 'case.id' identifying each matched set (1 case + its controls)

Route B - Epi::ccwc() (counter-matched/risk-set sampling from entry and exit times). Confirm arguments with ?Epi::ccwc.

Show the code (Route B): sample with Epi::ccwc() 
# Step 2 (Route B): sample with Epi::ccwc() ------------------------------------
library(Epi)   # ccwc - case-control within cohort

cohort <- all_persons %>%
  mutate(
    entry = as.Date("2010-01-01"),                # study start (adapt; e.g. 18th birthday or first eligible date)
    exit  = end_fu,                               # exit time = end of risk time (from Step 1)
    fail  = case                                  # 1 if the person is a case at exit
  )

ncc <- ccwc(
  entry    = entry, exit = exit, fail = fail,     # entry time, exit time, failure at exit
  controls = 5,                                   # controls per case
  match    = list(sex, birth_year),               # matching variables
  include  = list(pnr),                           # keep pnr in the output
  data     = cohort
)
# Output: the matched-set id is called 'Set' here (not 'case.id')

Same rules as in Comparison cohort: apply inclusion/exclusion criteria BEFORE sampling and to everyone (cases + pool); set a seed; and decide on with/without replacement (controls can typically be used for several cases) - which must be handled in the analysis. incidenceMatch() itself handles the risk-set time dimension via case.index + end.followup.

Analysis (brief)

The analysis itself belongs to Regression (Phase 13) - here only briefly, so you know where this leads. Matched case-control data can be analyzed with conditional logistic regression (survival::clogit() with a strata() term per matched set), so the matching is respected; the odds ratio from an incidence-density design estimates the rate/hazard ratio. This is the standard approach for matched case-control studies. Before the analysis you attach exposure and covariates to the matched persons (from Phase 11/Phase 12). The matched-set id is called case.id (Route A) or Set (Route B).

What now?

You have a matched dataset (cases + controls) with a matched-set id (case.id or Set). Use pnr to extract exposure and covariates for everyone in the dataset:

cc_pnrs <- unique(matched$pnr) # all pnr's (cases + controls) for the other extractions
Extraction Phase
Exposure + covariates Phase 11
Comorbidity (NMI) NMI
Assemble into one analysis dataset Phase 12
Conditional logistic regression Phase 13

See also

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