Background: Psychotherapy research has shifted from mainly focusing on the average effects of different treatments to concentrating more on questions related to the individual patient. This aligns with the goals of precision medicine and patient-focused research and might include studying predictors to forecast important patient behaviors, such as premature termination or expected change rates.When research attention shifts, it often gives rise to the implementation of new statistical methods that, in turn, can illuminate new challenges that must be addressed. For example, the fields of classical statistics and data science have merged in interesting ways in recent times, which has led to an expansion of the methodological toolbox available for psychotherapy research. However, some strengths and limitations of these new approaches should be recognized as they come from slightly different research traditions. For instance, the broader goals of scientific exploration, such as description, prediction, and explanation, might be addressed and given different importance under these approaches.Studying these matters in the context of routine care leads to specific considerations that must be reflected upon, such as how to define relevant outcomes and draw sound conclusions from observational data collected in a naturalistic setting.Aim: The aim of the thesis was to concretize these matters by contrasting how traditional methods for predicting certain psychotherapy outcomes have been studied in the past, and how more advanced statistical methods might be used to enhance knowledge of how to predict these outcomes today.Studies/Results: Three studies were performed: Paper I focused on how Multi Level Modeling (MLM) can be used to study aspects of the relationship between working alliance and treatment outcome that has been overlooked by most earlier studies. The classical way of studying this question has been by using simple correlation analysis to explore the association between patient-rated alliance and treatment outcome. However, with the help of MLM and ratings from both parties of the therapeutic dyad, it is possible to analyze how these ratings relate to each other at different levels in the data (i.e., from patient to patient within the same therapist, denoted within therapist herein, and from therapist to therapist, denoted between therapists herein). MLM also makes it possible to explore the degree of correlation between working alliance and treatment outcome when studied between compared to within therapists, which might have importance for how to direct further studies of this association.