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Statistical and computational methods are widely used in today's scientificstudies. Using a female fertility potential in childhood cancer survivors as anexample, we illustrate how these methods can be used to extract insightregarding biological processes from noisy observational data in order to informdecision making. We start by contextualizing the computational methods with theworking example: the modelling of acute ovarian failure risk in femalechildhood cancer survivors to quantify the risk of permanent ovarian failuredue to exposure to lifesaving but nonetheless toxic cancer treatments. This isfollowed by a description of the general framework of classification problems.We provide an overview of the modelling algorithms employed in our example,including one classic model (logistic regression) and two popular modernlearning methods (random forest and support vector machines). Using the workingexample, we show the general steps of data preparation for modelling, variableselection steps for the classic model, and how model performance might beimproved utilizing visualization tools. We end with a note on the importance ofmodel evaluation.

Modelling fertility potential in survivors of childhood cancer: An introduction to modern statistical and computational methods