Precision Medicine in Depression and Anxiety: Measuring Clinically Meaningful Change, Assessing Symptom-Specific Response in Psychotherapy, and Exploring Stratified Treatment Selection
: (Alternative Format Thesis)

  • Clarissa Bauer-Staeb

Student thesis: Doctoral ThesisPhD


Depression and anxiety are amongst the most common mental health problems worldwide. While multiple psychotherapies exist, treatment response is varied and leaves room for improvement. There has been a growing interest in personalised psychotherapy, which aims to better match psychotherapies to patients in order to maximise treatment outcomes. The aim of my PhD was to explore different approaches that have the potential to contribute to personalised psychotherapy for depression and anxiety.

A critical component of evaluating if a treatment is effective, including any efforts made in personalised psychotherapy, is an outcome metric against which success can be measured. Many outcome metrics rely solely on statistical change on outcome questionnaires and ignore the patient perspective. In study 1, I developed a method to approximate the minimal clinically important difference (MCID), which is the smallest difference in outcome questionnaires that are tangible to patients. The patient's subjective experiences of improvement were mapped onto the changes in depression and anxiety questionnaire scores. The MCID was defined as the point at which there was a 50% probability of feeling better. The results were in line with previous literature showing that there was strong baseline dependency – how much change is associated with patients feeling better depends on their baseline severity, with patients experiencing more severe symptomatology requiring larger changes. As such, the MCID was personalised to each level of baseline severity. Metrics like the MCID can be used in clinical practice and research to support the evaluation of treatments.

In study 2, I conducted an analysis that used a prescriptive algorithm to assess whether assigning patients to two commonly available psychotherapies for depression, based on baseline characteristics, could improve clinical outcomes. The analysis was performed using a retrospective, observational cohort of patients receiving cognitive behavioural therapy or counselling for depression, which was derived from electronic healthcare records. The results showed that no individual characteristic was important in determining a differential response to the two treatments. However, when considering all characteristics collectively, small improvements of 4-10% were seen across the sample if patients were assigned to the treatment that was indicated as optimal according to the algorithm.

In study 3, I explored the impact of cognitive behavioural therapy on the individual symptoms of depression and anxiety. Electronic healthcare records were used to generate a retrospective, observational cohort of patients that received cognitive behavioural therapy and the individual symptom trajectories were modelled. Each symptom trajectory was compared to the average trajectory of all other symptoms. While all symptoms improved across cognitive behavioural therapy, the results suggested that low mood/hopelessness and guilt/worthlessness improved at a faster pace relative to other depressive symptoms, with sleeping problems, appetite changes, and psychomotor retardation/agitation improving relatively slower. The anxiety symptoms that improved fastest were uncontrollable worry and too much worry, with irritability and restlessness improving at a slower pace. Understanding variability in the response of individual symptoms to treatment has the potential to inform personalised psychotherapy. It potentially allows patients to be matched to psychotherapies in the future based on symptom profiles and/or informing where an augmentation of treatment may be necessary to enhance the response of symptoms that improve slower.

The results of my PhD, and the wider literature, suggest that small improvements may be possible through personalised psychotherapy. However, the clinical magnitude of these remains debatable, with no “magic bullet” found to date. While the current approaches in personalised psychotherapy are unlikely to lead to a profound impact at the individual level, small improvements across the population may nonetheless be relevant from a public health perspective if such approaches were adopted in national healthcare systems in the future. When interpreting the results of my PhD, the observational nature of the data must be considered, which makes inferring causality difficult. Further research with more robust research designs is required to validate the findings and develop the ideas presented further. Ground-breaking advances in the field of personalised psychotherapy are likely dependent on establishing a robust understanding of the mechanisms underlying depression and anxiety as well as the psychotherapies themselves. This may allow for more robust matching of psychotherapies to clinical presentations based on empirical evidence of the mechanisms.
Date of Award22 Feb 2023
Original languageEnglish
Awarding Institution
  • University of Bath
SupervisorKatherine Button (Supervisor), Julian Faraway (Supervisor) & Emma Griffith (Supervisor)

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