This article provides a comprehensive step-by-step guide for developing and evaluating clinical prediction models, addressing common methodological limitations found in published studies. The guide covers key aspects such as defining the aim and users, selecting data sources, handling missing data, exploring modeling options, and assessing model performance. It emphasizes the importance of interdisciplinary collaboration and provides practical recommendations for each step, including the use of R code for illustration. The guide is illustrated with an example from relapsing-remitting multiple sclerosis and includes detailed discussions on various topics, such as outcome measurement, candidate predictor identification, sample size considerations, and model validation. The article also highlights the importance of addressing common pitfalls, such as inappropriate categorization of continuous outcomes, data-driven cut-off points, and overfitting. The final steps involve deciding on the final model, performing a decision curve analysis to assess the model's clinical usefulness, and optionally evaluating the predictive ability of individual predictors.This article provides a comprehensive step-by-step guide for developing and evaluating clinical prediction models, addressing common methodological limitations found in published studies. The guide covers key aspects such as defining the aim and users, selecting data sources, handling missing data, exploring modeling options, and assessing model performance. It emphasizes the importance of interdisciplinary collaboration and provides practical recommendations for each step, including the use of R code for illustration. The guide is illustrated with an example from relapsing-remitting multiple sclerosis and includes detailed discussions on various topics, such as outcome measurement, candidate predictor identification, sample size considerations, and model validation. The article also highlights the importance of addressing common pitfalls, such as inappropriate categorization of continuous outcomes, data-driven cut-off points, and overfitting. The final steps involve deciding on the final model, performing a decision curve analysis to assess the model's clinical usefulness, and optionally evaluating the predictive ability of individual predictors.