Neural Networks vs. Random Forests: A Practical Guide for Machine Learning Practitioners
I. Introduction Machine learning has evolved significantly, with Neural Networks (NNs) and Random Forests (RFs) being two widely used algorithms. While deep learning dominates in many areas, Random Forests often excel in structured data applications. Selecting a machine learning model depends on the analysis goal and application. A common approach is to compare models—logistic regression, […]
Fusing Machine Learning in R: Late Integration Predictive Modeling with fuseMLR
Introduction Recent technological advances have enabled the simultaneous collection of multi-omics data, i.e., different types or modalities of molecular data across various organ tissues of patients. For integrative predictive modeling, analyzing such data presents several challenges: Data Availability: Ideally, different modalities are measured in the same individuals, enabling early or intermediate integration techniques. However, real-world […]
Mastering Clinical Trial Design: Key Concepts for Statisticians
Summary Clinical trials are research studies conducted to assess the safety, efficacy, and sometimes the cost-effectiveness of new medical treatments, interventions, or diagnostic tests in humans. Statisticians play a crucial role in designing these studies, ensuring methodological rigor, and analyzing the resulting data to generate reliable conclusions. A thorough understanding of various study designs, along […]
Statistical Test Insights: Key and Common Tips for Accurate Analysis and Inference
Summary Statistical tests are essential tools in research and data analysis, helping us draw meaningful conclusions from data. We explore some key and common knowledge and tips related to statistical tests. First, we point out the importance of developing a research question, stating hypotheses, and collecting relevant data. Understanding the p-value distinction between one-tail and […]