ViewpointA primer on common analytic concerns in psychoneuroimmunology: Alternatives and paths forward
Section snippets
Linear regressions assume normality of residuals *not* values
Many psychoneuroimmunology studies investigate hypothesized linear relationships between immunological and psychosocial/behavioral variables. For example, whether higher levels of CRP are associated with higher levels of depression or whether individuals who perseverate more during a stressor have greater inflammatory reactions. Consequently, linear regressions (and other types of linear models) are commonly used in psychoneuroimmunology. A common misconception about linear regression is that
Sometimes it is best to change models, not data
It is common for data to be manipulated via transformation and winsorization/removal of outliers, given the oft skewed nature of immunological data (and many psychological variables of interest) and frequency of extreme values. Although these decisions might help standard models run smoothly, certain types of data manipulation result in data that no longer fully represent the original sample. For example, consider a sample of adolescents without autoimmune conditions recruited to investigate
Biological composites need to be physiometrically evaluated before use
Biological aggregates are becoming increasingly popular in psychoneuroimmunology to counteract issues with multiple comparison and selective reporting associated with analyzing multiple analytes. Additionally, if hypotheses are not specific to the effects of specific proteins (e.g., inflammation generally), a latent or composite variable might better match the level at which the underlying theory is described. When composites are used it is common to include all proteins in a dataset; however,
We got the power (analyses)
Psychoneuroimmunology is far from the only field suffering from a dearth of power analyses reported in manuscripts, but it is an important area with room for improvement. Monte Carlo simulations provide a relatively straightforward way to determine power to detect an effect of interest with the sample size available. This technique is also incredibly flexible, able to evaluate the power for any type of analysis. Briefly, this procedure (code provided) involves simulating a dataset with the
Conclusion
This primer seeks to highlight potential areas of improvement in the analytic execution of psychoneuroimmunology research. By improving the standard statistical rigor of the field it is possible to conduct more replicable, informative, and clinically-relevant studies to advance the field at a more efficient, consistent, and meaningful pace.
Funding
Daniel P. Moriarity was supported by National Research Service Award F31MH122116 and an APF Visionary Grant.
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