The Faculty of Medicine, University of Rijeka is one of Croatia’s key medical faculties, especially relevant because of its focus on biomedicine, scientific research, laboratory projects, clinical medicine and public health topics. Due to this research profile, statistical data analysis plays a particularly important role in academic papers, doctoral dissertations, specialist work and scientific publications.
Students, doctoral candidates and researchers often work with clinical data, laboratory measurements, biomedical parameters, questionnaires, scales, retrospective patient databases, prospective studies, epidemiological data, diagnostic tests and treatment outcomes. This article belongs to the F2 hub: statistical help for clinical and scientific research at medical faculties in Croatia.
Statistical data analysis for the Faculty of Medicine, University of Rijeka
The Faculty of Medicine, University of Rijeka is one of Croatia’s important medical faculties and a significant institution for education, clinical medicine, biomedicine, public health and scientific research. In SEO context, it is often searched as Medicine Rijeka, MEF Rijeka, Medicinski fakultet u Rijeci or University of Rijeka Faculty of Medicine. It is especially relevant because of its strong focus on biomedical research, laboratory analyses, translational medicine, clinical studies and postgraduate research programs.
Medical and biomedical papers in Rijeka often include complex databases, clinical outcomes, laboratory parameters, immunological and molecular indicators, diagnostic tests, treatment outcomes, epidemiological data, retrospective hospital databases, prospective studies and instrument validation. Statistical analysis must therefore be precise, transparent and connected with the biomedical logic of the research.
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 Faculty of Medicine in Rijeka
The Faculty of Medicine in Rijeka is especially interesting for SEO and research because clinical medicine, biomedicine, laboratory research, public health and interdisciplinary projects strongly overlap. For such work, basic statistics is often not enough; it may be necessary to combine several methodological levels: descriptive statistics, group comparisons, regression models, ROC analysis, reliability analysis, instrument validation and sometimes survival analysis.
The biomedical focus of Rijeka is important because laboratory and translational studies often include smaller but methodologically demanding samples, many variables, several measurement points, experimental groups, biomarkers and outcomes that must be interpreted statistically and biologically. Results should therefore not be interpreted only through p-values, but also through effect size, confidence intervals, direction of association and potential clinical or biological relevance.
Data types in medical and biomedical papers
Projects related to the Faculty of Medicine, University of Rijeka may include demographic data, clinical outcomes, laboratory parameters, biochemical and immunological indicators, diagnostic tests, treatment outcomes, quality-of-life questionnaires, symptom scales, comorbidities, complications, hospitalization, mortality, survival and public health indicators.
Clinical databases often include age, sex, diagnosis, disease stage, disease duration, treatment, laboratory values, imaging findings, surgical data, complications and treatment outcomes. Biomedical databases may include biomarkers, molecular indicators, protein or genetic variables, cellular parameters, experimental conditions and laboratory groups. 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 use existing hospital databases, medical records, laboratory registries or archived diagnostic findings. The advantage is data availability; challenges include missing values, heterogeneous documentation, different measurement methods and limited control over original data collection.
Prospective studies
Prospective studies allow better control over measurements, follow-up and outcome definition. Primary and secondary outcomes, inclusion and exclusion criteria, sample size and statistical analysis plan should be defined in advance.
Biomedical and laboratory-based research
Biomedical studies often include laboratory parameters, biomarkers, experimental groups, multiple measurement points and complex biological relationships. It is important to check distribution, identify outliers, choose suitable tests and distinguish statistical from biological relevance.
Cross-sectional and epidemiological studies
Cross-sectional studies assess prevalence, risk factors, attitudes, quality of life or relationships between clinical and sociodemographic indicators. Epidemiological studies may include incidence, prevalence, odds ratios, relative risks and risk factor analysis.
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.
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, biomarkers, 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 and biomedical papers they can help identify patient profiles, risk classes, biomarker patterns, groups of laboratory parameters 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 and biomedicine, results must be interpreted statistically, clinically and biologically. A p-value shows statistical significance, but not automatically effect size, biological 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 and biomedical 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 connected with the primary outcome, expected effect size, significance level and desired power.
Typical topics for Rijeka
Typical topics include biomedical research, laboratory biomarkers, clinical studies, retrospective patient analyses, prospective studies, diagnostic accuracy, treatment outcomes, epidemiology, public health, quality of life, questionnaire validation, medical rehabilitation, neurological and psychiatric scales, oncology outcomes, immunological indicators, 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 biomedical laboratory data be analyzed?
Yes. Biomarkers, laboratory parameters, experimental groups, repeated measurements and biologically relevant outcomes can be analyzed.
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, logistic regression and survival analysis be performed?
Yes. These methods are often very useful in diagnostic, clinical and biomedical research.
Keywords
statistics Faculty of Medicine Rijeka, Medicinski fakultet Rijeka statistics, MEF Rijeka SPSS, biomedicine Rijeka statistics, clinical research Rijeka statistics, medical statistics Rijeka, logistic regression medicine, ROC analysis medicine, survival analysis medicine, biomedical research statistics
Metatext
Statistical data analysis for the Faculty of Medicine, University of Rijeka: SPSS, biomedicine, clinical research, ROC analysis, logistic regression, survival analysis and laboratory research.
