Variable Selection for Prediction in Clinical Research

ABSTRACT Selecting the appropriate set of variables is a crucial challenge in predicting outcomes in clinical research. Effective variable selection improves model prediction accuracy and helps clarify the underlying prediction process. This paper categorizes four different types of variable selection methods based on their underlying models: p value‐based methods, penalty methods, tree‐based methods, and Bayesian methods. We introduce commonly used models for each category, summarize the existing software and packages available, and provide an overview of how these models function within a statistical framework. We also demonstrate the application of each method in clinical research, discussing their advantages and disadvantages in terms of model complexity, robustness, and accessibility. Additionally, we explore how these methods relate to others and how researchers can interpret them. In the final section, we discuss how appropriate variable selection can improve model prediction accuracy in different aspects of clinical research. We also summarize the data sizes most suitable for each variable selection method, propose general guidelines for using various methods with other data types, and comment on recent developments in variable selection methodologies. Lastly, we address considerations for new challenges, such as high‐dimensional data.