The School of Medicine, University of Split is one of the most important medical faculties in Croatia, especially recognized for its strong international orientation, English-language medical program and many international students. Because of this international structure, statistical data analysis must be methodologically precise, transparent and aligned with standards expected in clinical, biomedical and public health research.
Students, doctoral candidates and researchers often work with clinical data, laboratory measurements, questionnaires, scales, retrospective patient databases, prospective studies, epidemiological data, diagnostic tests, treatment outcomes and biomedical parameters. This article belongs to the F2 hub: statistical help for clinical and scientific research at medical faculties in Croatia.
Statistical data analysis for the School of Medicine, University of Split
The School of Medicine, University of Split is one of the most important medical faculties in Croatia and the region, especially recognized for its strong international orientation, English-language medical program and large number of international students. Because of this international research environment, academic and scientific papers prepared in Split often need to meet high methodological standards, clear statistical reasoning and precise interpretation.
Research at this faculty may include basic medical sciences, clinical medicine, public health, epidemiology, laboratory medicine, biomedicine, surgery, internal medicine, pediatrics, gynecology, neurology, psychiatry, oncology, radiology, dental medicine, rehabilitation and interdisciplinary projects. Statistical data analysis is therefore a central component of research methodology.
Students, doctoral candidates and researchers often work with clinical data, laboratory values, diagnostic tests, questionnaires, rating scales, retrospective hospital databases, prospective clinical studies, epidemiological data, treatment outcomes, public health indicators and biomedical parameters. Such studies require clear definition of primary and secondary outcomes, independent variables, covariates, possible confounders and the statistical analysis plan.
At StatistischeBeratung.de, professional support is provided throughout the research process — from study design and sample size planning to dataset structuring, method selection, SPSS analysis, interpretation and table preparation. The author remains responsible for the final text and interpretation. This article belongs to the F2 hub: statistical help for clinical and scientific research at medical faculties in Croatia.
Specific characteristics of the School of Medicine in Split
A key strength of the School of Medicine in Split is its international program. This means that students and researchers from different healthcare systems, academic traditions and methodological backgrounds often work in the same academic environment. This increases the need for standardized, clearly explained and internationally understandable statistical analysis.
Papers written in English or intended for international publication often require more precise reporting of methodology, clear definition of statistical tests, confidence intervals, effect sizes and clinical relevance. Reporting only p-values is not sufficient. Results must be explained in relation to clinical practice, diagnostics, therapy, public health or future research.
Data types in medical papers
Projects related to the School of Medicine, University of Split may include demographic data, clinical outcomes, laboratory parameters, diagnostic tests, treatment outcomes, symptom scales, quality-of-life questionnaires, comorbidities, complications, hospitalization, mortality, survival, epidemiological indicators and public health data.
Clinical databases often include age, sex, diagnosis, disease stage, disease duration, treatment, laboratory values, imaging findings, surgical data, complications and treatment outcomes. Questionnaire studies may include instrument validation, scale reliability, factor structure, correlations between psychological and clinical variables, and differences between patient groups. For psychological and questionnaire topics, the F3 hub may be relevant: statistical help for humanities and social sciences, and for validation of instruments and rehabilitation assessments the F6 hub: questionnaires, assessments and instrument validation.
Medical research design
Retrospective clinical studies
Retrospective studies often use existing hospital databases, medical records, laboratory registries or archived diagnostic findings. The advantage is data availability; challenges include missing values, heterogeneous documentation, inconsistent measurement quality and limited control over original data collection.
Prospective studies
Prospective studies allow better control over measurements, follow-up and outcome definition. Primary outcome, secondary outcomes, sample size, inclusion and exclusion criteria and the statistical analysis plan should be defined in advance.
Cross-sectional studies
Cross-sectional studies assess prevalence, risk factors, knowledge, attitudes, quality of life or relationships between clinical and sociodemographic indicators at a single time point.
Longitudinal studies and repeated measurements
Longitudinal studies follow patients over time, for example before and after therapy, during rehabilitation or across several follow-up visits. Such data require methods that account for dependence between repeated measurements. For rehabilitation and functional outcomes, the F4 hub may be useful: sports and kinesiology research.
Clinical trials and intervention studies
Intervention studies require careful planning, clear inclusion and exclusion criteria, defined outcomes, control of possible confounders and transparent interpretation of clinical relevance.
Important statistical methods
Descriptive statistics
Descriptive statistics describe sample structure: age, sex, diagnoses, clinical characteristics, laboratory values and outcomes. Depending on distribution, mean and standard deviation or median and interquartile range are reported.
Group comparison tests
Group comparisons use t-test, Mann-Whitney U test, ANOVA, Kruskal-Wallis test, chi-square test and Fisher’s exact test. Method selection depends on variable type, distribution, group size and research question.
Correlation analysis
Correlation analysis examines relationships between laboratory values, clinical scales, disease duration, symptom intensity, functional status, quality of life and other continuous or ordinal indicators.
Regression analysis
Linear, logistic and ordinal regression are used to evaluate predictors of outcomes. Logistic regression is especially important when the outcome is binary, such as complication, positive finding, treatment response, hospitalization or mortality.
ROC analysis
ROC analysis evaluates diagnostic accuracy of biomarkers, laboratory parameters and clinical tests. It presents sensitivity, specificity, AUC and possible cut-off values.
Survival analysis
Kaplan-Meier curves and Cox regression are used when time-to-event outcomes are important, such as time to relapse, death, complication, hospitalization or treatment failure.
Reliability analysis and instrument validation
For questionnaires and scales, Cronbach’s alpha, item analysis, factor analysis, correlations and construct validity are used. These methods are important in clinical, psychological, rehabilitation and public health research.
Multivariate methods
Multivariate methods are used when many related variables are present. In medical papers they can help identify patient profiles, risk classes, biomarker patterns or dimensions of complex data.
SPSS analysis and interpretation
SPSS is frequently used in medical papers because it supports group comparisons, correlations, regression, ROC analysis, reliability analysis and graphical presentation. Technical execution alone is not enough.
In medicine, results must be interpreted statistically and clinically. A p-value shows statistical significance, but not automatically effect size, clinical importance or applicability. Confidence intervals, effect sizes, odds ratios, relative risks, AUC values and other indicators are therefore important.
Sample size and statistical power
Medical studies often require sample size planning. Too small a sample may fail to confirm a real effect, while very large samples may show statistically significant but clinically small differences. Sample size should be planned according to the primary outcome, expected effect size, significance level and desired power.
Typical topics for Split
Typical topics include clinical studies, retrospective patient analyses, prospective studies, international medical projects, laboratory biomarkers, diagnostic accuracy, treatment outcomes, epidemiology, public health, quality of life, questionnaire validation, medical rehabilitation, neurological and psychiatric scales, oncology outcomes, cardiovascular risk factors, pediatric data and survival analysis.
Workflow
The workflow includes reviewing the topic and hypotheses, checking dataset structure, defining primary and secondary outcomes, selecting statistical methods, SPSS analysis, table preparation and interpretation. I prepare statistical results and suggest how to present them in the academic paper. The author remains responsible for the final text and interpretation.
FAQ
Can support be provided before a medical study starts?
Yes. This is the best time to plan study design, sample size, outcomes and database structure.
Can a retrospective hospital database be analyzed?
Yes. Data structure, missing values, variable types and possible confounders must be checked.
Is SPSS sufficient for a medical paper?
For many student papers, master theses and doctoral dissertations, SPSS is sufficient. More complex analyses may require additional tools.
Can ROC analysis and logistic regression be performed?
Yes. ROC analysis and logistic regression are often very useful in diagnostic and clinical research.
Keywords
statistics School of Medicine Split, Medicinski fakultet Split statistics, MEF Split SPSS, clinical research Split statistics, medical statistics Split, international medical program statistics, logistic regression medicine, ROC analysis medicine, survival analysis medicine, biomedicine statistics
Metatext
Statistical data analysis for the School of Medicine, University of Split: SPSS, clinical research, international program, ROC analysis, logistic regression, survival analysis and biomedicine.
