On the pursuit of reproducibility: the importance of large sample sizes in psychoimmunology.

Abstract

Peripheral inflammatory markers (PIMs), such as C-reactive protein (CRP) or white blood cell count (WBC), have been associated with depression severity in meta-analyses and large cohort studies. However, in typically-sized psychoimmunology studies (N < 200) that explore associations between PIMs and neurobiological/psychosocial constructs related to depression and studies that examine less-studied PIMs (e.g., interferon gamma), significant concerns about reproducibility of results exist. For the well-characterized association between PIMs (CRP/WBC) and depression severity, we examined statistical errors as a function of sample size in a large community cohort (n = 24,550). We further assessed how statistical errors varied as related to analytic decisions (e.g., number of covariates) and characteristics related to study design (e.g., relationships within subgroups of patients). Only large samples (e.g., n = 1000 to n = 10,000) were sufficiently powered to detect PIM-depression associations and minimized overestimation of effect sizes (e.g., effect size inflation), and greater sample sizes were required as more covariates were included in analytic models. Moderately sized samples (n > 500) generally ensured the correct directionality of effect sizes (e.g., low rates of sign reversal). Sample sizes required for 80% power also varied widely depending on study design characteristics (e.g., N = 350 to N = 10,000+). Typically-sized psychoimmunology studies examining PIM-depression associations (N < 200) are likely underpowered and at high risk of overestimation of effect sizes. Study design characteristics also notably influence power and statistical error rates. Use of large sample sizes (e.g., N > 7000) and consideration of analytic decisions (e.g., number/choice of covariates) will maximize reproducibility of psychoimmunology studies related to depression to enhance development of treatments for depression or to help understand pathophysiological mechanisms of depression.