Back to Basics: The Importance of Measurement Properties in Biological Psychiatry

Abstract

Biological psychiatry is a major funding priority for organizations that fund mental health research (e.g., National Institutes of Health). Despite this, some have argued that the field has fallen short of its considerable promise to meaningfully impact the classification, diagnosis, and treatment of psychopathology. This may be attributable in part to a paucity of research about key measurement properties ("physiometrics") of biological variables as they are commonly used in biological psychiatry research. Specifically, study designs informed by physiometrics are more likely to be replicable, avoid poor measurement that results in misestimation, and maximize efficiency in terms of time, money, and the number of analyses conducted. This review describes five key physiometric principles (internal consistency, dimensionality, method-specific variance, temporal stability, and temporal specificity), illustrates how lack of understanding about these characteristics imposes meaningful limitations on research, and reviews examples of physiometric studies featuring a variety of popular biological variables to illustrate how this research can be done and substantive conclusions drawn about the variables of interest.

Building a replicable and clinically-impactful immunopsychiatry: Methods, phenotyping, and theory integration

“…However, both the immune system and many psychiatric disorders (e.g., depression) are extremely multi-faceted, complex constructs. To maximize the replicability and clinical impact of this field, careful attention needs to be paid to methodology/measurement properties ( Moriarity and Alloy, 2021 ), characterizing inflammatory phenotypes of psychopathology ( Moriarity and Alloy, 2020 ), and integration of immunology into robust, extant psychosocial frameworks (e.g., response styles theory; Moriarity et al., 2018 ; Nolen-Hoeksema and Morrow, 1991 ). Parallel growth in all three areas (summarized in Table 1 ) will ensure immunopsychiatry research is replicable, contributes to understanding how (and for whom) the immune system is associated with psychiatric symptoms, and increases the flexibility and power of personalized treatment planning.…”

“…However, there is a dearth of understanding about several key measurement properties of inflammation that are germane to standard study designs in immunopsychiatry. As we review in Moriarity and Alloy (2021) , this could impose meaningful limitations on study design, analysis planning, and result interpretation.…”

Robust group- but limited individual-level (longitudinal) reliability and insights into cross-phases response prediction of conditioned fear

“…However, a remarkable amount of predictions were non-significant – which was particularly true for CS discrimination in SCRs and BOLD fMRI. This may be explained by difference scores (i.e., CS+ minus CS−) being generally less reliable ( Infantolino et al, 2018 ; Lynam et al, 2006 ) due to a subtraction of meaningful variance ( Moriarity and Alloy, 2021 ) particularly in highly correlated predictors ( Thomas and Zumbo, 2012 ). Especially at the end of the extinction, CS discrimination is low and hence, variance limited.…”

A Physiometric Investigation of Inflammatory Composites: Comparison of “A Priori” Aggregates, Empirically-identified Factors, and Individual Proteins

“…If a choice between modeling individual proteins and empirically-supported composites is made, this decision should be made prior to data collection to facilitate complementary study design (e.g., sample size, time between observations in longitudinal studies) with the physiometrics of the variables to be used in mind. Armed with this information, researchers can design studies that require fewer resources (money, participants, repeated measures), with greater odds of finding replicable effects, and the research to intervention pipeline can be optimized (Moriarity & Alloy, 2021).…”