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Choose the method only after identifying how the observations were sampled and what you want to estimate. Case and control locations treated as point patterns call for a different approach from binary outcomes sampled within neighborhoods or other clusters. Clustering detection, estimation of a spatial relative-risk surface, and estimation of an exposure association are also different goals. Spatial modeling cannot compensate for controls who do not represent the population that produced the cases.
First identify the data structure and the question
“Spatial dependence” is not one feature of every case–control dataset. It may describe the locations of cases and controls as spatial point patterns, or dependence among binary observations sampled within spatial groups. Start by describing the sampling unit, the study region, and how controls were selected. Then state whether the analysis is intended to detect clustering, estimate how relative risk varies across space, or estimate an association between an exposure and case status.
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| Data and goal | Analytic direction | Interpretation to specify |
|---|---|---|
| Cases and controls represented as point patterns; estimate spatial variation in relative risk | Compare case and control spatial intensity patterns or use a point-process model | Relative risk across the defined study region |
| Binary outcomes sampled within spatial clusters; estimate an exposure association | Consider marginal GEE or a spatial random-effects model | Population-average effects for GEE; subject-specific effects for random-effects models |
| Cases and controls; test whether cases are spatially clustered | Use a clustering statistic suited to the chosen scale or focus | Evidence of clustering, not an adjusted exposure effect |
These are broad distinctions, not interchangeable recipes. The model must match the sampling scheme and the intended estimand.
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When individual case and control locations are represented as point patterns, a risk surface can be expressed through the ratio of the case and control intensity functions over the study region. This compares where cases occur with where controls occur, rather than treating the map as a collection of independent locations. The interpretation depends on the study domain and on how the control locations were obtained.
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Point-process models and spatial variation
A modern Bayesian option described in a 2025 paper uses multivariate log-Gaussian Cox process (LGCP) models. The approach represents covariate effects with fixed effects and residual spatial variation with spatial random effects. The paper demonstrates an implementation using INLA through the R package inlabru, with the Chorley–Ribble dataset in Lancashire, England.
That example documents one practical route, not evidence that an LGCP is best for every case–control design. Check that the model’s assumptions, spatial domain, and treatment of sampling correspond to the actual study. Software implementation details should also be checked against the version in use.
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For clustered binary observations, match the model to the estimand
If binary outcomes are observed among individuals grouped in villages, neighborhoods, or other spatial clusters, methods for point-pattern data may not fit the sampling structure. Two distinct approaches are marginal generalized estimating equations (GEE) and spatial random-effects models.
Marginal GEE
GEE can target population-average effects while representing dependence among observations. A 2018 paper on spatially clustered binary prevalence data models distance-related dependence using pairwise odds ratios and a hybrid pairwise likelihood. This is a method for a particular clustered-binary setting, not a universal prescription for matched case–control point patterns.
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Spatial random effects
Spatial random-effects models can support subject-specific inference by representing shared or spatially structured variation. Their interpretation differs from the population-average target of a marginal GEE. Choose between them based on the question the study is meant to answer and the actual sampling design; do not describe their estimates as though they had the same target.
Separate cluster detection from exposure-effect estimation
A test for spatial clustering answers whether cases appear unusually concentrated under the test’s comparison or reference scheme. It does not, by itself, estimate an adjusted association between a particular exposure and case status. Conversely, an exposure-effect model is not automatically a clustering test.
Rogerson’s 2006 methods for case–control data include global and local clustering tests. Examples include counting cases closer to a given control than to other controls, counting cases within a specified distance, and calculating a local statistic around a prespecified focus. The appropriate statistic depends on whether the question concerns clustering across the study region, within a distance threshold, or around a location chosen in advance. Report that choice; it defines what the test can establish.
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Spatial adjustment does not repair biased control selection. Controls should reflect the source population that produced the cases and should be selected independently of the exposure being evaluated, as CDC field epidemiology guidance advises. Neighborhood matching may be appropriate in some designs, but excessive matching can reduce the contrasts needed to estimate an exposure association.
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If cases and controls were matched, the analysis must account for that design. CDC guidance states that case–control exposure comparisons must account for matching when matching was used. Conditional logistic regression is particularly appropriate for pair-matched data. A spatial random effect alone is not a substitute for an analysis that respects matching, nor does adding one remove selection bias or confounding.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A practical decision sequence
- Describe what was sampled. State whether observations are individuals with geocoded residences, case and control point patterns, binary outcomes grouped into spatial clusters, or area-level outcomes. Define the geographic study region and explain how control locations were obtained.
- Name the target. Specify whether the aim is a clustering test, a relative-risk surface, or an adjusted exposure association. For clustered binary data, say whether the target is population-average or subject-specific.
- Choose a method that fits both. For point patterns, consider case/control intensity comparisons or a point-process model. For spatially clustered binary data, assess marginal GEE against spatial random effects in light of the estimand. Use a clustering statistic when clustering itself is the question.
- Account for the design. Explain how controls represent the source population, whether matching was used, and how matching enters the analysis. Do not treat spatial adjustment as a cure for design problems.
- Check the assumptions and report uncertainty. Describe the dependence representation, covariates, estimation method, and uncertainty summaries. The method’s interpretation depends on these choices and on the spatial domain.
What to report so readers can assess the analysis
A reproducible account should give enough detail to understand both the design and the model. At minimum, report:
- Case and control definitions and the study region.
- Coordinate or geographic scale, plus the process used to sample or obtain control locations.
- Matching variables and how the analysis accounts for matching.
- The model’s spatial dependence structure and included covariates.
- The inferential target, including whether an effect is population-average or subject-specific where relevant.
- The estimation method, software and version where applicable, and uncertainty summaries.
This reporting checklist follows from the information needed to interpret the cited designs and models; it is not presented as a formal standard issued by those sources.
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