In this module, we discuss the role of predictive analytics for prevention, diagnosis, and effectiveness. We begin with a brief introduction to predictive analytics, which we follow by differentiating between population-based and targeted interventions. We then explain why and when it may be beneficial to test for a diagnosis, and how analytic tools can help inform these decisions. Finally, we focus on the balance between benefits and harms of a certain treatment, and how we can predict the benefit for an individual.

In this module, we will present some key concepts in prediction modeling. First, we weigh the strengths and weakness of various study designs. Second, we stress the importance of an appropriate sample size for reliable inference. Then, we discuss the issues of overfitting a prediction model, and regression-to-the-mean. Finally, we will guide you through the popular bootstrap procedure, showing how it can be used to assess parameter variability.

In this module, we focus on model development. First, we turn our attention to the missing values problem. We discuss well-known missingness mechanisms, and methods to deal with missing values appropriately. Second, we learn about methods to deal with non-linearity in a dataset. We then address the topic of model selection, focusing on the limitations of traditional stepwise selection procedures. Last, we talk about how introducing bias in exchange for lower variance can improve prediction quality. This can be done by using advanced methods, such as LASSO and Ridge regression.

In this final module, we learn about assessing the quality of a prediction model. First, we extensively discuss standard performance measures for both binary and continuous outcomes. Second, we explore different ways of validating a prediction model. We look at how to assess both the internal, and the more relevant external validity of a model. Next, we will look at how to update a model and make it applicable to a specific medical setting. We conclude with an interview, where we more broadly discuss the potential of predictive analytics by taking the example of the island of Aruba.