Biostatistical analysis for clinical research and publication

Yes, because the apparent simplicity of comparing groups hides decisions that determine whether the conclusion holds: which test suits each type of variable, whether its assumptions are met and whether adjustment for confounders is needed. This applies to most clinical, epidemiological and health studies that reach publication with an analysis built on group comparisons and regression. The risk is not complexity but applying the habitual test without checking its conditions: a t-test on data that don't meet its assumptions, or a comparison without adjustment for confounders, produces a result that looks valid until a reviewer examines it. What the service contributes is choosing the right method for the data and the question, and leaving the analysis ready to be defended. A well-designed standard analysis helps make the conclusion publishable and defensible, instead of leaving it exposed to a basic methodological objection.

The service covers the biostatistical analysis most studies need: sample size calculation, group comparisons, regression, adjustment for confounders, and publication-ready tables and figures. It applies from study design, whether observational or experimental, cross-sectional or longitudinal, through to writing the methods and results sections. Group comparisons include tests such as the t-test, ANOVA, Mann-Whitney, Kruskal-Wallis, chi-squared and Fisher's exact test, with the non-parametric alternative when the assumptions are not met; regression covers linear, logistic, Poisson or negative binomial models, depending on the type of outcome. Specialised methods are not covered here as general analysis: survival, advanced multivariate analysis, time series, causal inference or omics analysis. Each one is handled in its own service. The criterion is clear: the standard analysis that answers many clinical or epidemiological questions is done here; specialised work is referred on, not forced.

We choose the test from the research question, the type of variable and the structure of the data, not by habit. Before applying anything, we check the relevant assumptions: independence, the distribution of residuals, homogeneity of variance, cell sizes or the presence of repeated measures. If they do not hold, we select an alternative that fits, such as a non-parametric test or a robust model. A continuous outcome may call for linear regression, a binary outcome for logistic regression with adjusted odds ratios, and a count for a Poisson or negative binomial model. These are starting points, not recipes: the final choice also depends on the study design and the estimate you need to interpret. We document the rationale so that a reviewer can follow why that method was used and not another.

The database does not need to arrive clean, but its condition determines the starting point. The client collects, records and provides the data; the service reviews the quality and structure of each variable, defines how missing data are handled, whether by exclusion, imputation or sensitivity analysis where appropriate, and checks the assumptions before applying any test. A database with poorly coded variables, inconsistent categories or out-of-range values is dealt with before the analysis, because a flawless analysis of an unreviewed database can produce a result that is convincing and wrong. What does help is having the research question and the variables that answer it defined in advance; if the database needs extensive preparation, that phase is scoped separately. Arriving with the data in order speeds up the analysis, but it is not an absolute condition for assessing the project.

Your study needs a specialised method when the question or the structure of the data cannot be handled by a standard test or regression. For example, time to an event with censoring calls for survival analysis; many candidate variables for building a panel, multivariate and biomarker analysis; omics data, applied bioinformatics; repeated measures or a complex longitudinal structure, longitudinal data analysis with mixed models; the behaviour of a metric over time, time series analysis; and causal effect estimation, causal inference. You do not need to decide this before contacting us: once we review the question, design and data, we can tell whether a general biostatistical analysis is sufficient or whether a dedicated method is needed. The distinction is methodological, because each method answers a different question and relies on different assumptions, and forcing a general tool onto the wrong problem leads to weaker conclusions. Standard analyses are handled here and specialised ones in their own service, with the same team.

Yes. We deliver the baseline characteristics and results tables, the scientific figures and the statistical methods section in the format your target journal asks for, following the reporting guidelines that journal applies. This applies if you are submitting to a journal with specific requirements for presentation and methodological reporting, such as CONSORT for trials or STROBE for observational studies. The scope of those guidelines is worth stating precisely: CONSORT and STROBE structure the reporting according to the study design; on their own they do not secure acceptance, which depends on many other factors. What you receive is material ready to submit: tables, figures, methods and interpretation for the discussion, not uninterpreted software output that has to be reformatted later. The aim is for the analysis to reach the journal documented and defensible, reducing the risk of having to redo it because of a basic methodological objection.

You receive the interpreted analysis, not software output with no reading attached. This applies across the service: the result includes what each analysis answers, what it doesn't and how it translates into a defensible clinical conclusion. A p-value on its own is not a conclusion. A non-significant result may reflect insufficient evidence, a lack of power or an effect compatible with a range of plausible sizes, not necessarily the absence of an effect; and an odds ratio, a difference in means or a rate ratio needs its confidence interval, its reference scale and its clinical interpretation to be useful. The work includes writing the statistical methods section and providing the interpretation for the discussion, scientist to scientist. The aim is not for you to have a number, but for you to understand and be able to defend what the analysis says and what it doesn't. That interpretation is what turns a table of results into an argument that holds up under review.

Yes. The service provides biostatistical support for clinical, epidemiological and health research, including PhD theses, clinical research projects and academic work with real methodological demands, especially when it is going to be submitted for publication or defended. It applies to researchers, PhD students and clinical teams who need to resolve the analysis of their study with a correct, defensible method. The approach is the same whatever the context: choose the test or model that matches the data and the question, check the assumptions and prepare the results so that they hold up before examiners or reviewers. What the service is not: a shortcut that replaces the methodological judgement of the researcher, who signs and directs their own work. The research team owns the data, the question and the authorship; the service contributes the method, the analysis and its justification. The goal is work that stands up to examination, not work that merely passes it.