This article provides a step-by-step guide for developing and evaluating clinical prediction models. It highlights the importance of defining the model's purpose, target population, outcome, healthcare setting, users, and clinical decisions. The guide emphasizes the need for a collaborative, interdisciplinary team including clinicians, methodologists, users, and people with lived experiences. Common pitfalls include inappropriate categorization of continuous outcomes or predictors, data-driven cut-off points, univariable selection methods, overfitting, and lack of attention to missing data and model performance. The guide outlines 13 steps, including defining aims, creating a team, reviewing literature, and developing a protocol. It discusses the choice between developing a new model or updating an existing one, defining the outcome measure, identifying candidate predictors, collecting and examining data, considering sample size, dealing with missing data, fitting prediction models, assessing model performance, internal and external validation, deciding on the final model, performing decision curve analysis, and assessing the predictive ability of individual predictors. The guide also addresses issues related to model inception, predictor selection, sample size considerations, handling missing data, assessing model performance, and evaluating the model's clinical usefulness. It provides an example of a prediction model for relapse in relapsing-remitting multiple sclerosis and includes R code for implementation. The article emphasizes the importance of rigorous methodology, transparency, and validation to ensure the model's reliability and applicability in clinical practice.This article provides a step-by-step guide for developing and evaluating clinical prediction models. It highlights the importance of defining the model's purpose, target population, outcome, healthcare setting, users, and clinical decisions. The guide emphasizes the need for a collaborative, interdisciplinary team including clinicians, methodologists, users, and people with lived experiences. Common pitfalls include inappropriate categorization of continuous outcomes or predictors, data-driven cut-off points, univariable selection methods, overfitting, and lack of attention to missing data and model performance. The guide outlines 13 steps, including defining aims, creating a team, reviewing literature, and developing a protocol. It discusses the choice between developing a new model or updating an existing one, defining the outcome measure, identifying candidate predictors, collecting and examining data, considering sample size, dealing with missing data, fitting prediction models, assessing model performance, internal and external validation, deciding on the final model, performing decision curve analysis, and assessing the predictive ability of individual predictors. The guide also addresses issues related to model inception, predictor selection, sample size considerations, handling missing data, assessing model performance, and evaluating the model's clinical usefulness. It provides an example of a prediction model for relapse in relapsing-remitting multiple sclerosis and includes R code for implementation. The article emphasizes the importance of rigorous methodology, transparency, and validation to ensure the model's reliability and applicability in clinical practice.