RESEARCH DISCIPLINE
Statistical Analysis for Medical Research
Consulting for Clinical & Academic Studies
Medical research statistical analysis for chart reviews, hospital cohorts, diagnostic studies and investigator-initiated trials, with the analysis fitted to how your records were collected and the 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 Medical Research
Medical research statistical analysis is the statistical design, analysis and reporting of clinician-led studies, most of which use data collected during patient care rather than for research, such as chart reviews, electronic health record extracts, hospital cohorts and investigator-initiated trials. Care records hold several encounters per patient, follow-up that ends at discharge or death, and values that are missing for clinical reasons, so a workflow that treats every row as an independent patient gives estimates that are too precise and often biased. OnlineSPSS, a specialist statistical consultancy for academic, clinical and professional research, provides medical statistics consulting through in-house PhD-level statisticians, who settle the unit of analysis, time zero and model size with you before anything is fitted.
WHEN TO ENGAGE
Statistical Support at Different Stages of Medical Research
Researchers usually come to us at a recognizable point in a study, from the first design questions to a reviewer's comments on a finished manuscript.
Planning a Record-Based Study You are designing a chart review, registry study or hospital cohort and need the eligibility criteria, index date, unit of analysis and primary outcome defined before extraction starts.
Analyzing Care-Record Data Your EHR or registry extract is ready, and the outcome model and the handling of repeated admissions and of patients lost to follow-up need to be chosen, run and checked.
Preparing a Clinical Manuscript Results are ready to be written up and need adjusted estimates with confidence intervals, flow-diagram counts and a methods paragraph prepared to the reporting guideline your target journal expects.
Reviewing a Completed Analysis A model has been run, often from software defaults, and a coauthor, advisor or reviewer has questioned the unit of analysis, the start of follow-up or the handling of missing data and competing deaths.
RESEARCH CONTEXT
Research Designs and Data We Work With in Medical Research
The datasets behind most medical data analysis come from the clinic or the ward, and each carries its own structure that the analysis has to respect.
01
Retrospective Chart Reviews
Records abstracted by the clinical team, where eligibility, index date and outcome were not recorded for research and must be reconstructed.
Abstracted charts
Single-site series
02
EHR and Registry Extracts
Extracts from electronic health records or disease registries, with several encounters per patient and missing values that follow clinical practice.
EHR extracts
Disease registries
03
Hospital Cohorts
Single-center or multicenter cohorts followed from admission, diagnosis or a procedure, including nested case-control samples drawn from them.
Admission cohorts
Nested case-control
04
Investigator-Initiated Studies
Randomized or pragmatic studies run within a department, often with modest samples, missed visits and outcomes measured at several follow-up appointments.
Pilot studies
Pragmatic designs
05
Diagnostic and Prognostic Studies
Index tests compared with a reference standard, and models that predict outcomes, with accuracy, calibration and validation to report.
Index test data
Prediction cohorts
06
Paired and Patient-Reported Data
Measurements from both eyes or limbs, repeated scores and questionnaires collected alongside clinical endpoints, all correlated within the patient.
Bilateral measures
Patient-reported scores
METHODOLOGICAL CONSIDERATIONS
Statistical Issues We Consider in Medical Research
These are the checks made in observational clinical data analysis before any result is trusted, because each can change an estimate more than the choice of model does.
Time Zero and Immortal Time Follow-up should start when a patient becomes eligible, not at a later recorded treatment date, or immortal person-time that cannot contain an event will favor the treated group.
Unit of Analysis A row may be a patient, an admission or an eye. Repeated admissions or both eyes counted as separate patients make intervals too narrow, so the structure is settled before any model is fitted.
Events per Model Parameter Model size follows the number of events or the information in the outcome, not the number of records. A large file with few events supports only a few predictors, so the covariate set is limited and justified.
Censoring and Competing Deaths Where death or another event prevents the outcome, treating it as censoring in a Kaplan-Meier analysis overstates cumulative incidence. The estimand decides between cause-specific and subdistribution analyses.
Why Records Are Missing Routine records are missing because of clinical decisions, such as a test ordered only for sicker patients. The reasons are examined before complete-case analysis or imputation is accepted.
Smallest Difference That Matters Before results are read, the smallest difference that would change practice for your patients is agreed with you, and each estimate and its interval are judged against it, not against the p-value.
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 medical data.
Clinical, Survival & Epidemiological
Follow-up in care records is censored and often cut short by death, so time-to-event and competing-risk models carry the main outcome analysis in cohort work.
Regression & Predictive Modeling
Binary outcomes such as complications or readmission are modeled with covariate sets limited by the events, and sparse events call for penalized estimation.
Longitudinal & Multilevel Analysis
Serial laboratory values, visit-level scores and measurements from both eyes are correlated within a patient, which mixed models and GEE represent directly.
Sensitivity & Specificity (Diagnostic Accuracy)
A new test is compared with a reference standard, predictive values reflect the prevalence in your patients, and agreement is measured when two readers score the same cases.
Comparative & Hypothesis Testing
Unadjusted comparisons in small clinical series use exact or rank-based tests when counts are sparse or the outcome is ordinal, with the difference and its interval.
Bayesian & Computational Methods
Laboratory values go unrecorded when tests are not ordered, so imputation models include the clinical variables that predicted ordering, with complete-case results as a check.
DELIVERABLES
Statistical Outputs Prepared for Your Research
The outputs follow the structure of a medical study, from the review of the data dictionary to the scripts that regenerate every reported value.
Data & Study Review The data dictionary, unit of analysis, index date, outcome and covariate definitions, and the pattern of missing data are reviewed before any model is fitted.
Statistical Analysis Kaplan-Meier estimates and the agreed Cox, logistic or mixed models, with proportional hazards, calibration and influence diagnostics and the planned sensitivity analyses.
Effect Estimates & Uncertainty Adjusted hazard, risk or odds ratios and absolute differences with confidence intervals, and cumulative incidence at clinically chosen time points.
Tables & Figures Baseline tables by exposure or group, adjusted effect tables, survival and cumulative incidence plots, forest plots and the participant counts that reporting guidelines ask for.
Statistical Interpretation Each result is explained in clinical terms within what the design supports, observational findings are worded as associations, and limitations are stated for the discussion.
Analysis Files SPSS syntax, R scripts, Stata do-files or SAS programs, with the analysis dataset structure documented so that every table can be regenerated.
REPORTING
Reporting Medical Research
The guideline depends on the type of study you are reporting, and each is applied only where it fits.
Investigator-initiated randomized studies report to CONSORT 2025. Chart reviews, cohorts and case-control studies report to STROBE, with RECORD added where the data come from health records, registries or claims. A new test evaluated against a reference standard in your patients follows STARD 2015, and a model that predicts outcomes from your records follows TRIPOD+AI. Flow-diagram counts, the handling of missing data and the prespecified analyses are tabulated from the analysis itself, so the manuscript and the output agree. Journal instructions and the ICMJE recommendations are then checked for your target journal, and each adjusted ratio is placed beside the absolute risk or cumulative incidence it implies for your patients.
ILLUSTRATIVE EXAMPLE
How the Analysis May Look in Practice
An illustration of how a retrospective record-based question moves from an extract to a defensible, reportable result.
ILLUSTRATIVE EXAMPLE · HOSPITAL MEDICINE RESEARCH · UNITED STATES
A hospital medicine group wanted to know whether scheduling a follow-up visit before discharge was associated with readmission within 90 days, using electronic health record data.
Many patients had several discharges, and some died at home before any readmission. Before outcomes were examined, we set the unit of analysis to the first eligible discharge per patient and time zero at discharge. Exposure was defined as a visit scheduled at discharge, not attended, because attending requires surviving and staying out of hospital, which would add immortal time. Death was treated as a competing event, with cumulative incidence by group and cause-specific hazards for readmission. The number of covariates was limited by the number of readmission events, and complete-case results were compared with multiple imputation for laboratory values missing through clinical practice.
Outcome: The group received the cause-specific hazard ratio with its 95% confidence interval, cumulative incidence curves with death as a competing event, STROBE and RECORD 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 a clinical research fellow on study design, the usual data problems and reporting expectations, and on paired measurements handled from the start.
“What worked for me was that I did not have to translate the whole Medicine context before discussing the statistics. They understood the study design, the usual data problems, and the reporting expectations. We were particularly concerned about the repeated or paired structure of the measurements, and that was dealt with properly from the start.”
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 Medical Research
Medical analyses are run in the software your team 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 Medical Research
Short answers to what clinician-researchers ask before sharing a study, from method choice and sample size to repeated admissions, review and confidentiality.
What statistical methods are used in medical research?
Medical research most often uses survival and competing-risks models, logistic regression, mixed models or generalized estimating equations, and diagnostic accuracy analysis, chosen by the outcome and by how the records were collected. In a hospital cohort, for example, time to readmission with death as a competing event calls for competing-risks methods, and serial laboratory values for mixed models. Our Statistical Methods pages show how each family is applied.
How many patients or charts does my retrospective study need?
A retrospective study has a fixed number of records, so the useful question is what that number can support. We can estimate the precision of the primary estimate, or the number of predictors the events allow, before extraction is complete. For a protocol, sample size or power is calculated for the primary outcome and written into the analysis plan.
How do you analyze patients with several admissions?
Several admissions by one patient are one person’s correlated records, not independent patients. Depending on the question, the analysis keeps the first eligible admission, summarizes at patient level, or models the admissions with a mixed model or generalized estimating equations. The right choice depends on whether the question concerns patients or admissions, and it is documented before any model is fitted.
Can you review an analysis after reviewer comments?
Yes. We can review a completed analysis before submission or in response to reviewer comments. The review covers the unit of analysis, the start of follow-up, the handling of death and missing data, the model size against the events and the reporting against the relevant guideline, and states what stands and what should change. An analysis produced from software defaults or an AI tool is reviewed in the same way.
How is confidential clinical research data handled?
Study datasets, protocols and analysis files are handled confidentially, and project information is not published. 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 share your research question, design and relevant materials, and a statistically trained Research Coordinator reviews the request and responds the same day. The analysis, deliverables, timeline and one fixed project fee are then confirmed before any 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
Medical Research With Us
Share your research question, study design and available data, even if only an extract or a protocol exists. We review the structure of your records and confirm the approach before any work begins.
No Commitment Required
Strictly Confidential
Fixed Project Fee
In-House PhD-Level Statisticians
Same-Day Response
Serving Researchers Since 2014