BackgroundFor investigating the individual–environment interplay and individual differences in response to environmental exposures as captured by models of environmental sensitivity including Diathesis‐stress, Differential Susceptibility, and Vantage Sensitivity, over the last few years, a series of statistical guidelines have been proposed. However, available solutions suffer of computational problems especially relevant when sample size is not sufficiently large, a common condition in observational and clinical studies.MethodIn the current contribution, we propose a Bayesian solution for estimating interaction parameters via Monte Carlo Markov Chains (MCMC), adapting Widaman et al. (Psychological Methods, 17, 2012, 615) Nonlinear Least Squares (NLS) approach.ResultsFindings from an applied exemplification and a simulation study showed that with relatively big samples both MCMC and NLS estimates converged on the same results. Conversely, MCMC clearly outperformed NLS, resolving estimation problems and providing more accurate estimates, particularly with small samples and greater residual variance.ConclusionsAs the body of research exploring the interplay between individual and environmental variables grows, enabling predictions regarding the form of interaction and the extent of effects, the Bayesian approach could emerge as a feasible and readily applicable solution to numerous computational challenges inherent in existing frequentist methods. This approach holds promise for enhancing the trustworthiness of research outcomes, thereby impacting clinical and applied understanding.

中文翻译：

---

在环境敏感性元框架内探索人×环境相互作用的贝叶斯方法

背景为了研究个体与环境的相互作用以及个体对环境暴露反应的个体差异，通过环境敏感性模型（包括素质压力、差异易感性和优势敏感性）捕获，在过去几年中，提出了一系列统计指南。然而，可用的解决方案会遇到计算问题，尤其是当样本量不够大时，这是观察和临床研究中的常见情况。方法在当前的贡献中，我们提出了一种通过蒙特卡罗马尔可夫链（MCMC）估计交互参数的贝叶斯解决方案，改编 Widaman 等人。 （心理学方法，17 号，2012，615）非线性最小二乘法（NLS）方法。结果应用示例和模拟研究的结果表明，对于相对较大的样本，MCMC 和 NLS 估计收敛于相同的结果。相反，MCMC 明显优于 NLS，解决了估计问题并提供了更准确的估计，特别是在小样本和较大残差方差的情况下。结论随着探索个体和环境变量之间相互作用的研究主体的增长，可以预测相互作用的形式和程度从效果来看，贝叶斯方法可能成为解决现有频率论方法固有的众多计算挑战的可行且易于应用的解决方案。这种方法有望提高研究结果的可信度，从而影响临床和应用理解。