RESEARCH DISCIPLINE
Statistical Analysis for Epidemiological Research
Consulting for Cohort & Case-Control Studies
Epidemiological statistical analysis for cohort, case-control and cross-sectional studies, with the effect measure chosen from the research question, confounders chosen from a causal rationale and results prepared to the reporting standard your journal expects.
No Commitment Required
Strictly Confidential
In-House PhD-Level Statisticians
Fixed Project Fee
Same-Day Response
Statistical Analysis for Epidemiological Research
Epidemiological statistical analysis is the statistical design, analysis and reporting of studies of how often disease occurs and which exposures change its risk, using population cohorts, case-control samples, cross-sectional surveys and linked records. Rates, risks, odds and hazards answer different questions, a covariate that is a consequence of the exposure can bias an adjusted estimate, and members of one household, school or clinic are correlated, so a workflow that adjusts for every available variable and reports whichever ratio the software produces gives results that are hard to defend in review. OnlineSPSS, a specialist statistical consultancy for academic, clinical and professional research, provides epidemiology consulting through in-house PhD-level statisticians, who settle the estimand, the adjustment set and the clustering with you before any model is fitted.
WHEN TO ENGAGE
Statistical Support at Different Stages of Epidemiological Research
Epidemiologists usually reach out at a recognizable point, from the first outline of a cohort or case-control study to a reviewer's challenge to an adjusted result.
Planning a Cohort or Case-Control Study You are designing a cohort or case-control study and need the case definition, the source of the controls or the follow-up, the exposure measure and the confounders written down before data collection.
Choosing the Effect Measure Your data are ready, and you need to decide whether the question calls for a rate, risk, odds or hazard ratio, and which covariates belong in the adjustment set.
Analyzing Clustered Cohort Data Participants come from households, schools or clinics, or are examined repeatedly, and the analysis has to account for the correlation among their records.
Defending an Analysis in Review A reviewer has questioned stepwise covariate selection, odds ratios reported as risks or the treatment of clustered participants, and the analysis has to be redone or defended.
RESEARCH CONTEXT
Research Designs and Data We Work With in Epidemiological Research
Epidemiology data analysis starts from datasets built to count cases and compare exposures, and each design determines which effect measure the data can support.
01
Population Cohorts
Participants followed for incident disease, with exposures measured at baseline or updated over time and some lost to follow-up along the way.
02
Case-Control Studies
Cases and sampled controls, matched or unmatched, including controls drawn from within a cohort, with exposure taken from interviews or records.
03
Cross-Sectional Prevalence Studies
Surveys that estimate how common a condition or exposure is at one time, where the prevalence ratio or odds ratio is the effect measure.
04
Registry and Linked Records
Disease registries and linked administrative files, with incidence counted over person-time and exposure assigned from a separate source.
05
Household and School Clusters
Participants recruited as families, classrooms or practices, with repeated examinations of the same people on top of the grouping.
06
Outbreak and Exposure Investigations
Time-limited investigations of a cluster of cases, in which a few suspected exposures are compared between ill and well persons.
METHODOLOGICAL CONSIDERATIONS
Statistical Issues We Consider in Epidemiological Research
These checks decide whether an association can be defended, because the effect measure and the adjustment set can change a result more than the model does.
Estimand Before Estimator Rates, risks, odds and hazards are different quantities. The one reported is fixed from the question, and an odds ratio is not presented as a risk ratio when the outcome is common.
Confounders From a Causal Rationale Adjustment variables come from a causal diagram of exposure and outcome, not from p-values in stepwise selection, and consequences of the exposure are kept out of the model.
Misclassified Exposure and Cases How exposure and case status were ascertained is examined for error, and where it may differ between groups, a bias analysis shows how far the estimate could move.
Who Is in the Comparison Controls must represent the population that produced the cases, and nonparticipation and loss to follow-up are examined for selection that could create or hide an association.
Clustered and Repeated Records People in one household, school or clinic, and repeated examinations of one participant, are correlated, so robust or multilevel methods supply the standard errors.
Effect Modification and Subgroups Whether an effect differs between groups is tested by a prespecified interaction on a stated scale, and subgroup findings reached through many looks are reported as exploratory.
STATISTICAL METHODS
Methods Selected for the Research Question and Study Design
Each method family is chosen for the outcome and the data structure of your study, and the line under each title explains why it is used with epidemiological data.
Clinical, Survival & Epidemiological
The ratio reported follows the design and the outcome, incidence is counted over person-time, and propensity scores can summarize measured confounding when covariates are many.
Regression & Predictive Modeling
Case-control analysis uses logistic models, conditional where cases were matched, while Poisson regression with robust variance gives risk ratios for common outcomes.
Longitudinal & Multilevel Analysis
Households, schools and repeated examinations make records correlated, and marginal or mixed models give valid standard errors without treating persons as independent.
Comparative & Hypothesis Testing
Crude two-by-two comparisons come first, with exact tests for sparse cells and McNemar's test for matched case-control pairs, before any adjustment is made.
Bayesian & Computational Methods
Missing covariates are imputed under stated assumptions, and Monte Carlo simulation drives a quantitative bias analysis for misclassification and unmeasured confounding.
Evidence Synthesis & Meta-Research
Cohorts that measured the same exposure can be pooled, and individual participant data allow one adjustment set and one case definition across all the studies.
DELIVERABLES
Statistical Outputs Prepared for Your Research
The outputs follow the logic of an epidemiological study, from the review of case and exposure definitions to the scripts that regenerate every reported ratio.
Data & Study Review Case and exposure definitions, the source population, person-time, covariates and the pattern of missing values are reviewed against the causal question before any model is fitted.
Statistical Analysis Crude and adjusted analyses with the agreed effect measure, robust or multilevel variance for clustered records, and the prespecified sensitivity and bias analyses.
Effect Estimates & Uncertainty Rate, risk, odds or hazard ratios as the design defines them, with risk differences and confidence intervals, and interaction contrasts on the scale chosen.
Tables & Figures Participant characteristics by exposure, crude and adjusted effect tables, dose-response plots, a causal diagram and the participant flow that reporting guidelines ask for.
Statistical Interpretation Associations are interpreted against the design and the causal diagram, the plausible size of bias is discussed, and causal wording is used only where the design supports it.
Analysis Files Scripts or syntax with the case definitions, exposure coding and adjustment sets documented, so that each estimate can be regenerated from the study file.
REPORTING
Reporting Epidemiological Research
The checklist follows the design of the study, and its extensions are used only where they fit.
Cohort, case-control and cross-sectional studies report to STROBE, and RECORD extends it to registries, linked files and other routinely collected data. An observational study that estimates the effect of an intervention by emulating a target trial reports to the TARGET guideline of 2025. Case definitions, how exposure was measured, the handling of missing data and the participant flow are written up from the analysis files, so every count can be traced. The effect measure is named as the rate, risk, odds or hazard ratio actually estimated, a rate or risk difference is reported with it, and the instructions of the target journal are checked on top.
ILLUSTRATIVE EXAMPLE
How the Analysis May Look in Practice
An illustration of how an exposure-outcome question in a cohort moves from a causal diagram to a result that can be defended in review.
ILLUSTRATIVE EXAMPLE · CHILDHOOD ASTHMA COHORT RESEARCH · UNITED STATES
A research group followed school-enrolled children to ask whether early-life exposure to traffic-related air pollution was associated with new asthma diagnoses by age ten.
Children were recruited through schools and examined repeatedly, and asthma was common enough that odds ratios would have overstated the risk ratio. An earlier plan to choose covariates by stepwise selection was replaced, before outcomes were examined, by a set drawn from a causal diagram, which kept out a variable measured after exposure that lay on the causal path. The effect measure was fixed as a risk ratio from Poisson regression with robust variance, with standard errors that allowed for correlation within schools. Children lost to follow-up were compared with those retained, multiple imputation was run with the complete-case estimate beside it as a check, and the findings were worded as an association.
Outcome: The group received the adjusted risk ratio and risk difference with their 95% confidence intervals, the causal diagram, STROBE flow counts, a methods paragraph and the scripts.
This example does not describe a real client or project. We never share details of actual client work, in line with the confidentiality we promise every researcher. This illustrative example is included to show the type, scope, and standard of work involved in this kind of analysis.
RESEARCHER VOICE
What Researchers Say
Feedback from an epidemiologist on advice that reflected the realities of the field, on clustered data and on a result that could be defended in review.
“I had worked with analysts before, but this was the first time the statistical advice really reflected the realities of Epidemiology. They picked up the clustered structure of the data rather than treating every row as independent quickly and helped us avoid a result that would have been difficult to defend in review.”
HOW WE ASSESS YOUR PROJECT
We Review Your Actual Research
Your assessment is based on a review of your actual research materials and analytical requirements, not a generic project category.
You Submit Your Project Details
Share whatever is available at the current stage, such as the research aim, protocol or proposal, dataset, variable list, manuscript or statistical requirements.
We Review Your Research and Define the Scope
Your statistically trained Research Coordinator reviews the materials to understand the study design, research questions, data structure, analytical requirements and expected deliverables. If clarification is needed, additional documents or information can be requested before the assessment is finalized.
Your Analysis Is Completed and Delivered
Once the scope and fixed project fee are agreed, the in-house statistician completes the work and the Research Coordinator remains the communication point through delivery, with the analysis, deliverables and timeline as agreed.
SOFTWARE
Statistical Software for Epidemiological Research
Epidemiological analyses are run in the package your group already uses, with syntax or scripts provided where applicable.
Statistical Support Within Your Research OnlineSPSS provides the agreed methodological, analytical, and statistical reporting support. The researcher or research team retains the scientific question, substantive interpretation, conclusions, and overall responsibility for the research.
FREQUENTLY ASKED QUESTIONS
Questions About Statistical Analysis for Epidemiological Research
Short answers to what epidemiologists ask before sharing a study, from confounder selection and effect measures to clustered participants, confidentiality and the assessment.
Which statistical methods are used in epidemiology?
The core methods are regression for ratio measures, time-to-event models when follow-up differs in length, and cluster-aware or mixed models when participants are grouped. Which applies depends on the design, whether cohort, case-control or cross-sectional, and on whether the question concerns rates, risks or odds. Each family has its own page under Statistical Methods.
Should I choose confounders by stepwise selection?
No: confounders should be chosen from a causal rationale, not from p-values. Stepwise selection can drop true confounders, keep variables that bias the estimate and make intervals too narrow. The adjustment set is written down before results are seen, and a sensitivity analysis shows how far the estimate moves under a reasonable alternative set.
Can I report odds ratios instead of risk ratios?
Odds ratios are the natural measure in a case-control study, and when the outcome is rare they approximate the risk ratio, but in a cohort with a common outcome they overstate it. Where risk is the question, Poisson regression with robust variance gives risk ratios, and a risk difference gives the absolute scale. The report names the measure that was actually estimated.
How are participants from the same household or school analyzed?
Participants from one household or school are correlated, so their standard errors come from generalized estimating equations with robust variance or from a mixed model. With few clusters, robust standard errors can be too small, so the number of clusters is checked and a small-sample correction or a different model is used where needed.
How is confidential epidemiological data handled?
Research data and project details are handled confidentially, and project information is not published. Researcher names and institutions are not used publicly without permission. An NDA is available where required and can be arranged before sensitive materials are shared. Read more about confidentiality and data security.
What happens when I request an assessment, and how long does it take?
You send your research question, study design and any data or protocol you have. A statistically trained Research Coordinator reviews them and responds the same day, and the analysis, deliverables, timeline and one fixed project fee are confirmed before work begins, so the timeline follows from the agreed scope.
Still have a question? Find more answers in our full FAQ.
NEXT STEP
Discuss Your
Epidemiological Research With Us
Share your research question, design and data, even if only a case definition and a dictionary exist. We review the effect measure and adjustment set and confirm the approach before work begins.
No Commitment Required
Strictly Confidential
Fixed Project Fee
In-House PhD-Level Statisticians
Same-Day Response
Serving Researchers Since 2014