recurrent neural networks, event detection, data preprocessing, outlier removal, type 1 diabetes, automated detection, craniovertebral angle, neuronal network, Openpose, RGB, C7, tragus, laboratory diagnostics, requirements engineering, genomics, gene expression, metagenomics, medical diagnostics, standardized regression coefficient, statistics, meta-analysis, research synthesis, data presentation, carotid intima-media thickness, overweight, childhood, colon adenocarcinoma, bioinformatic analysis, MMP3 and TESC, data science, machine-learning, digital medicine, artificial intelligence, SARS-CoV-2 vaccines, deep learning, spike protein, ACE2, CpG DNA, machine learning, clinical trials, RCT, automated meta-analysis, automation, big data, nephrology, chronic kidney disease, prediction models, outcomes, personalized medicine, primary prevention, treatment, structural models, protein structures, membrane bilayer, DOPE, Modeller, AlphaFold2, weighted trajectory analysis, Kaplan–Meier estimator, clinical outcome assessment, logrank test, ordinal variables, Parkinson’s disease, hyperbaric oxygen therapy, genomic factors, single-cell RNA-seq, WSI, patches, tumour, cancer, Camelyon17, algorithmic fairness, electronic health records, data missingness, data imputation, diabetes, model explainability, mental workload, statistical feature selection, Shapley-based feature selection, alpha and theta EEG band ratios, Public health and preventive medicine

Recent years have witnessed an increase in the use of advanced data analysis methods in biomedical, clinical, and epidemiological research. The aim of this reprint is to highlight the practical aspects of new data analysis methods and to provide insights into the challenges in biostatistics and data science. By considering recent developments in medical technology, the reprint includes novel approaches in the field of medical statistics and data science, covering meta-analysis, the assessment of clinical outcomes, machine learning, medical diagnostics, and genomic factors.