RESEARCH DISCIPLINE
Statistical Analysis for Biomedical Research
Consulting for Laboratory & Preclinical Studies
Biomedical research statistical analysis for cell culture experiments, animal models and quantitative assays, with the experimental unit defined, exclusions and outliers handled by rules set in advance, 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 Biomedical Research
Biomedical research statistical analysis is the statistical design, analysis and reporting of laboratory studies of disease mechanisms and treatments, most of them experiments in cell cultures and rodent models measured by assays, imaging or physiological readouts. Groups are small, each animal or culture yields several readings, and image fields, tissue sections or repeated readings from one animal are often counted as separate samples, so a workflow that applies a t-test to every readout inflates the evidence and loses the biological replicate. OnlineSPSS, a specialist statistical consultancy for academic, clinical and professional research, provides biomedical data analysis through in-house PhD-level statisticians, who settle the experimental unit, the exclusion rules and the primary outcome with you before any test is run.
WHEN TO ENGAGE
Statistical Support at Different Stages of Biomedical Research
Laboratory groups tend to contact us when a decision about animals, cages or exclusions cannot wait, whether before allocation or after a reviewer's report.
Planning an Animal Experiment You are designing a study in mice or rats and need the experimental unit, group sizes, randomization, blinding and primary outcome fixed before the first animal is enrolled.
Analyzing Laboratory Readouts Your plates, blots or images are measured, and the handling of replicate readings, batches and outlying values needs to be decided, and the model run and checked.
Writing Up a Preclinical Study Your figures are drafted and need effect sizes with intervals, the number of animals behind each group and a statistics section that satisfies the journal's checklist.
Defending an Analysis to Reviewers A reviewer has questioned the sample size, a series of repeated t-tests or the exclusion of animals, and the reply has to rest on the allocation and housing records of the experiment.
RESEARCH CONTEXT
Research Designs and Data We Work With in Biomedical Sciences
Experimental study analysis in biomedical work starts from cultures and animals, and each design carries a structure of cages, days or specimens that the analysis has to respect.
01
Primary Cells and Organoids
Primary cells, organoids or tissue explants derived from several patients or animals, with each source tested under every condition so that it carries the replication.
02
Animal Model Studies
Mice or rats allocated to treatment groups and housed several to a cage, with weight, behavior or physiology recorded from each animal.
03
Repeated Measurements in Animals
Tumor volume, body weight or blood glucose measured on several days in the same animals, with humane endpoints that end some series early.
04
Imaging and Histology Data
Several fields or sections scored for each animal, where images from one animal are subsamples and scoring is blinded and checked between observers.
05
Assay Readouts From Tissue and Serum
ELISA, qPCR and multiplex panels from samples taken per animal, normalized to reference controls, with values that fall near the detection limit.
06
Preclinical Efficacy Comparisons
Randomized comparisons of a treatment with its vehicle in a disease model, where the primary outcome and the sample size are named before the study starts.
METHODOLOGICAL CONSIDERATIONS
Statistical Issues We Consider in Biomedical Research
These are the checks made in laboratory research statistics before an effect is reported, since a mistaken unit or an undeclared exclusion changes the evidence more than the choice of test does.
What Each n Counts Repeated sections, image fields and duplicate serum readings from one animal are subsamples. They are averaged within the animal or modeled with a random term, so n counts animals or independent experiments.
The Cage as Shared Environment Co-housed animals share diet, microbiota and social effects. If treatment goes to whole cages, the cage is the unit. If animals in one cage differ in treatment, cage enters as a block.
Power in Very Small Groups With three animals per group, a normality check has almost no power, and an exact Mann-Whitney test cannot give a two-sided p below 0.10, so a planned rank test of that size can never reach 0.05.
Humane Endpoints and Exclusions Animals that reach a humane endpoint are early endpoints, not outliers, so their data stay in the analysis. Any other exclusion follows a criterion written into the protocol and is reported with its reason.
Randomization and Blinding in the Model How animals were allocated, who scored the outcome and in what order are recorded and used, for instance as blocks, so the analysis matches the design that was actually run.
Many Readouts and Time Points A study of ten markers at four time points offers forty comparisons. One primary outcome is named in advance, and secondary and exploratory results are labeled and adjusted or interpreted as such.
STATISTICAL METHODS
Methods Selected for the Research Question and Study Design
Each method family is chosen for the unit and the outcome of your experiment, and the line under each title explains why it suits biomedical data.
Comparative & Hypothesis Testing
Small animal groups are compared by the difference and its interval, with Welch t-tests or exact rank tests, and one-way ANOVA with Dunnett contrasts when treatments share a vehicle.
Longitudinal & Multilevel Analysis
Growth and weight curves repeat within animals and animals share cages, so mixed models carry a random term for each, with generalized mixed models for repeated counts or scores.
Regression & Predictive Modeling
Baseline value, sex and body weight enter as covariates of the outcome, and cell or lesion counts are modeled as counts, not as normally distributed values.
Clinical, Survival & Epidemiological
Time to tumor onset or to a humane endpoint is censored for animals alive at the end, so survival curves replace a comparison of mean survival times.
Multivariate, Scale & Reliability
Marker panels are summarized by principal components with the animal as the sample, and agreement between two scorers of histology is quantified.
Bayesian & Computational Methods
Animals lost before the last measurement are handled under stated missing-data assumptions, values below detection as censored, and earlier results can enter as a prior in small groups.
DELIVERABLES
Statistical Outputs Prepared for Your Research
The outputs follow the structure of a laboratory study, from the review of the protocol to the files that regenerate every figure.
Data & Study Review The protocol, the unit behind every n, cage allocation, exclusions and assay details are reviewed before any test is run, so each value traces to an animal or an independent experiment.
Statistical Analysis The agreed comparisons and mixed or survival models, with residual and influence checks and the planned analyses with and without any excluded animal.
Effect Estimates & Uncertainty Mean differences, ratios or hazard ratios with 95% confidence intervals and standardized effects such as Hedges' g, with the primary outcome reported first.
Tables & Figures Group summary tables, plots that show every animal or experiment as a point, growth and survival curves, and a flow table of animals enrolled, excluded and analyzed.
Statistical Interpretation Each result is explained in biological terms within what the design supports, exploratory findings are labeled as such, and the limits of small groups are stated for the discussion.
Analysis Files R scripts, SPSS syntax or GraphPad Prism files, with the dataset structure and the exclusion log documented so that every table and figure can be regenerated.
REPORTING
Reporting Biomedical Research
Animal work, molecular assays and the target journal each bring their own checklist, and a laboratory paper often answers to all three.
Your animal experiments report to ARRIVE 2.0, so the methods state whether the cage or the animal was the experimental unit, how group sizes were justified and how many animals entered and left each analysis, with the humane endpoints that applied. Quantitative PCR reports to MIQE 2.0, with the efficiency and detection limits of each target. Journals then apply their own statistics checklists, such as the Nature Portfolio Reporting Summary, and the Transparency and Openness Promotion Guidelines (TOP 2025). Figures show each animal or independent experiment as a point, group sizes count animals rather than wells or sections, and every effect is reported with its confidence interval beside the p-value.
ILLUSTRATIVE EXAMPLE
How the Analysis May Look in Practice
An illustration of how a mouse-model question with co-housed animals and repeated photographs becomes one prespecified comparison.
ILLUSTRATIVE EXAMPLE · WOUND HEALING MOUSE MODEL RESEARCH · UNITED STATES
A research institute group wanted to know whether a topical compound, with wounds covered to prevent transfer between cage mates, accelerated wound closure in mice compared with its vehicle.
Wound area was photographed on several days in each animal, and mice lived four to a cage. The plan was agreed before the first wound measurement was analyzed: the animal as the unit, treatments allocated within cages so that each cage held both groups, wound area on the log scale in a mixed model with random terms for animal and cage, and a prespecified comparison at day 10. Some mice reached humane endpoints before the last photograph. Their earlier measurements stayed in the model, and results with and without those animals were set side by side. Images were scored by someone blinded to group.
Outcome: The group received the ratio of geometric mean wound area with its 95% confidence interval, plots of every animal, the flow table of animals for ARRIVE 2.0, 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 biomedical research scientist on whether the distribution and outliers made a planned analysis defensible, and on tables written for the research context.
“Our project had a very field-specific problem around whether the distribution and outliers made the planned analysis defensible. The team understood why it mattered in Biomedical Sciences and chose the analysis accordingly. The final tables and interpretation felt written for our research context rather than copied from a generic statistics template.”
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 Biomedical Research
Biomedical analyses are run in the tools your laboratory already uses, from group comparisons in Prism to mixed models in R, with files 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 Biomedical Research
Short answers to what laboratory researchers ask before sharing a study, from method choice and outliers to group size, confidentiality and the assessment process.
What statistical methods are used in biomedical research?
Biomedical studies most often use group comparisons with effect sizes, mixed-effects models for repeated measures and co-housed animals, survival analysis for time to an endpoint, and count or nonlinear models for assays, chosen by the unit and the outcome. Biomedical biostatistics for small experiments puts the design first, so the model follows how animals were allocated. Our Statistical Methods pages show how each family is applied.
Why not run a t-test on each marker and time point?
Separate t-tests treat each time point as a new experiment and each marker as the main result, so false positives accumulate and the link between time points from the same animal is lost. Time points are modeled together in one mixed model, with contrasts at the times named in the protocol, and markers outside the primary outcome are reported as secondary or exploratory with the adjustment stated.
How many animals or independent runs does my study need?
The number follows from the effect you would want to detect, the variability seen in earlier work or a pilot, and the planned analysis, and it counts independent animals or experiments, not wells or readings. We calculate it for the primary outcome, state the assumptions and show how the detectable effect changes if fewer animals are available. Animal ethics review usually asks for this justification.
Can I exclude an outlier from my analysis?
An unusual value alone is not a reason to exclude an animal. Exclusion needs a criterion written before the data were seen, such as a failed injection, a documented procedure error or an assay that did not run. Where values are extreme but valid, we consider a model that fits the distribution of the outcome and show how far the result depends on those animals.
How is confidential laboratory data handled?
Raw data files, experimental protocols, assay records 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
Biomedical Research With Us
Share your research question, experimental design and available data, even if only a protocol or a pilot dataset exists. We review how each n was formed and confirm the approach first.
No Commitment Required
Strictly Confidential
Fixed Project Fee
In-House PhD-Level Statisticians
Same-Day Response
Serving Researchers Since 2014