The Faculty of Medicine of the University of Sarajevo is the oldest and most important medical higher education institution in Bosnia and Herzegovina and represents a central academic hub for educating physicians, researchers and healthcare professionals. It covers a wide range of fields including clinical medicine, public health, biomedical research, epidemiology, pharmacology and medical statistics.
Within the F2 SEO structure, this faculty acts as the core authority for Bosnia and Herzegovina, similar to the role of the Faculty of Medicine in Zagreb for Croatia. Due to the high number of students, research projects and doctoral theses, there is a constant need for statistical data analysis, SPSS processing, interpretation of results and methodological guidance.
Statistical data analysis for the Faculty of Medicine Sarajevo
Research conducted at the Faculty of Medicine Sarajevo spans clinical studies, epidemiological research, biomedical investigations, laboratory-based studies and public health research. Statistical analysis must therefore be precise and tailored to the research design.
Clinical research includes treatment outcomes, comparison of therapies, risk assessment, diagnostic accuracy and monitoring disease progression. Such data often require group comparison tests, regression models and survival analysis.
Epidemiological research includes incidence, prevalence, risk factor analysis and disease distribution in populations. Logistic regression, cohort analyses, case-control designs and multivariate models are commonly used.
Biomedical studies include laboratory data, biomarker measurements, experimental conditions and controlled study designs, where variance analysis and biological interpretation are essential.
I prepare dataset structure, suggest appropriate statistical methods, perform SPSS analyses and prepare tables. I prepare statistical results and suggest how to present them. The author remains responsible for the final interpretation.
F2 HUB connection
This article belongs to the broader F2 structure and is connected to the central hub page: F2 hub EN - SPSS, clinical and scientific research for medical faculties.
Data types in medical research
Medical papers in Sarajevo include different data types: continuous variables such as blood pressure, laboratory findings and biochemical parameters; categorical variables such as diagnoses, therapy groups and treatment outcomes; and ordinal variables such as Likert scales for quality of life, pain or patient satisfaction.
Understanding data structure is especially important because medical data are often hierarchical: measurements within patients, repeated measurements over time, or grouping by departments and institutions.
Research design
Clinical studies
Clinical studies include comparison of therapies, treatment outcomes, medical interventions, complications and patient follow-up.
Epidemiological studies
Epidemiological studies analyze prevalence, incidence, risk factors and disease distribution. Logistic regression, odds ratios and multivariate models are frequently used.
Experimental studies
Experimental studies include controlled conditions and laboratory investigations. Group design, repetitions and control of variability are important.
Observational studies
Observational studies follow patients without direct intervention and may be retrospective, prospective or longitudinal.
Longitudinal studies
Longitudinal studies examine change over time and require methods that account for dependence between repeated measurements.
Statistical methods
Descriptive statistics
Descriptive statistics summarize sample characteristics and variables using frequencies, percentages, means, standard deviations, medians and interquartile ranges.
Hypothesis testing
t-test, ANOVA, Mann-Whitney U test, Kruskal-Wallis test, chi-square test and Fisher’s exact test are selected depending on variable type and distribution.
Correlation analysis
Pearson and Spearman coefficients are used to examine associations between variables.
Regression analysis
Linear and logistic regression are used to build predictive models and assess factors associated with medical outcomes.
Survival analysis
Kaplan-Meier analysis and Cox regression are used when the outcome is time to event.
ROC analysis
ROC analysis evaluates diagnostic accuracy of tests, biomarkers or clinical indicators.
SPSS analysis and interpretation
SPSS is the primary tool for statistical analysis in medical research. However, the main value is not only running tests, but interpreting results correctly in clinical context.
Results should be interpreted with respect to the research question, effect size, confidence intervals and clinical relevance.
Sample size and power
Sample size planning is essential for reliable results. Too small a sample may fail to detect real effects, while very large samples may produce statistically significant but clinically small differences.
Typical topics
Typical topics include clinical research, epidemiology, public health, laboratory biomarkers, diagnostic accuracy, treatment outcomes, quality of life, pain, functional status, complications, survival analysis, regression models and questionnaire validation.
FAQ
Can clinical research be analyzed?
Yes, including therapies, outcomes, diagnostics, complications and patient follow-up.
Can epidemiological analysis be performed?
Yes, including prevalence, incidence, risk factors and logistic regression.
Is SPSS sufficient?
For most papers yes, but complex models may require additional tools.
Keywords
statistics Faculty of Medicine Sarajevo, SPSS Sarajevo medicine, medical data analysis Bosnia, clinical research Sarajevo statistics, epidemiology Bosnia, Faculty of Medicine University of Sarajevo statistics, ROC analysis medicine
Metatext
Statistical data analysis for the Faculty of Medicine Sarajevo: SPSS, clinical research, epidemiology, regression, ROC analysis and medical data analysis.
