The Personalized Advantage Index (PAI) shows promise as a method for identifying the most effective treatment for individual patients. Previous studies have demonstrated its utility in retrospective evaluations across various settings. In this study, we explored the effect of different methodological choices in predictive modelling underlying the PAI.

Our approach involved a two-step procedure. First, we conducted a review of prior studies utilizing the PAI, evaluating each study using the Prediction model study Risk Of Bias Assessment Tool (PROBAST). We specifically assessed whether the studies adhered to two standards of predictive modeling: refraining from using leave-one-out cross-validation (LOO CV) and preventing data leakage. Second, we examined the impact of deviating from these methodological standards in real data. We employed both a traditional approach violating these standards and an advanced approach implementing them in two large-scale datasets, PANIC-net (n = 261) and Protect-AD (n = 614).

The PROBAST-rating revealed a substantial risk of bias across studies, primarily due to inappropriate methodological choices. Most studies did not adhere to the examined prediction modeling standards, employing LOO CV and allowing data leakage. The comparison between the traditional and advanced approach revealed that ignoring these standards could systematically overestimate the utility of the PAI.

Our study cautions that violating standards in predictive modeling may strongly influence the evaluation of the PAI's utility, possibly leading to false positive results. To support an unbiased evaluation, crucial for potential clinical application, we provide a low-bias, openly accessible, and meticulously annotated script implementing the PAI.

The general understanding of the ‘vulnerability–stress model’ of mental disorders neglects the modifying impact of resilience-increasing factors such as coping ability.

Probing a conceptual framework integrating both adverse events and coping factors in an extended ‘vulnerability–stress–coping model’ of mental disorders, the effects of functional neuropeptide S receptor gene (NPSR1) variation (G), early adversity (E) and coping factors (C) on anxiety were addressed in a three-dimensional G × E × C model.

In two independent samples of healthy probands (discovery: n = 1403; replication: n = 630), the interaction of NPSR1 rs324981, childhood trauma (Childhood Trauma Questionnaire, CTQ) and general self-efficacy as a measure of coping ability (General Self-Efficacy Scale, GSE) on trait anxiety (State-Trait Anxiety Inventory) was investigated via hierarchical multiple regression analyses.

In both samples, trait anxiety differed as a function of NPSR1 genotype, CTQ and GSE score (discovery: β = 0.129, P = 3.938 × 10−8; replication: β = 0.102, P = 0.020). In A allele carriers, the relationship between childhood trauma and anxiety was moderated by general self-efficacy: higher self-efficacy and childhood trauma resulted in low anxiety scores, and lower self-efficacy and childhood trauma in higher anxiety levels. In turn, TT homozygotes displayed increased anxiety as a function of childhood adversity unaffected by general self-efficacy.

Functional NPSR1 variation and childhood trauma are suggested as prime moderators in the vulnerability–stress model of anxiety, further modified by the protective effect of self-efficacy. This G × E × C approach – introducing coping as an additional dimension further shaping a G × E risk constellation, thus suggesting a three-dimensional ‘vulnerability–stress–coping model’ of mental disorders – might inform targeted preventive or therapeutic interventions strengthening coping ability to promote resilient functioning.