I often wonder why the whole world is so prone to generalise. Generalisations are seldom, if ever, true and are usually utterly inaccurate.
– Agatha Christie
It is much more important to know what sort of a patient has a disease than what sort of a disease a patient has.
– Sir William Osler
▢ Define and understand the role of moderators and mediators in clinical psychopharmacology trials
▢ Discuss the moderating effects on drug treatment outcome with key factors such as baseline clinical severity, demographic factors, age at onset, chronicity, episode number, duration of untreated illness, and history of trauma
▢ Recognize the mediating effects on treatment outcome of medication nonadherence, pharmacokinetic interactions, interpersonal factors, and comorbid disorders
One Size Fits One
Previous chapters have described ways in which “real-world” patients usually present with a diversity of psychiatric, medical, psychosocial, and other features that make a “one-size-fits-all” approach to treatment problematic. Large-scale randomized trials typically favor diagnostic uniformity so that all enrolled subjects more or less display the same kinds of symptoms under study. Consequently, the controlled trials literature that informs evidence-based practice largely comes from rarified, homogeneous study groups with rigidly defined diagnostic criteria. As a result, such studies trade off optimal outcomes (“efficacy”) for generalizability (“effectiveness”) under more ordinary conditions. This is why so-called “effectiveness” studies such as the Clinical Antipsychotics Treatment Intervention Effectiveness trial (CATIE; see Chapter 15) strive to enroll representative patients with comorbidities, imperfect treatment adherence, and issues with drug tolerability, adopting “bottom line” primary outcome measures such as “all-cause dropout.” No matter how well a treatment can work, the pragmatic concern remains how well it actually does work in real-life settings.
Treatment efficacy refers to how well a treatment can work under optimized conditions; effectiveness refers to how well an efficacious treatment actually performs under ordinary clinical conditions.
In routine treatment settings, outcomes can vary greatly when clinicians extrapolate from idealized patient types to more heterogeneous groups, whose actual problems may only faintly resemble those seen in study patients. Obviously, not everyone with the same overarching diagnosis responds to the same treatment, for many reasons. Therefore, we will now consider how to dissect the varied clinical elements that define and make every case unique, and use those characteristics to forecast likely outcomes and inform best pharmacotherapy decisions.
Like a made-to-measure suit individually tailored to the dimensions of a single wearer, “bespoke” psychopharmacology takes into account each patient’s defining characteristics in order to craft the best-fitting regimen for them.
Like fingerprints, most patients have unique identifiable features that distinguish them from other people with the same overall condition. Those unique attributes create a biosignature that discriminates the individual from the group, and thus may influence (and sometimes even govern) the usefulness of a particular treatment. Knowledge of someone’s distinctive clinical features is central to the concept of personalized medicine. It encompasses not only potential biomarkers (e.g., pharmacogenetics, as discussed more fully in Chapter 8) but also a wide range of clinical and demographic characteristics. By creating a case-by-case profile of those patient-specific elements that affect treatment outcome– called moderators and mediators – one can better refine the goodness-of-fit between a specific patient (rather than a general diagnosis) and a candidate therapy.