Bayesian PTSD-Trajectory Analysis with Informed Priors Based on a Systematic Literature Search and Expert Elicitation

There is a recent increase in interest of Bayesian analysis. However, little effort has been made thus far to directly incorporate background knowledge via the prior distribution into the analyses. This process might be especially useful in the context of latent growth mixture modeling when one or more of the latent groups are expected to be relatively small due to what we refer to as limited data. We argue that the use of Bayesian statistics has great advantages in limited data situations, but only if background knowledge can be incorporated into the analysis via prior distributions. We highlight these advantages through a data set including patients with burn injuries and analyze trajectories of posttraumatic stress symptoms using the Bayesian framework following the steps of the WAMBS-checklist. In the included example, we illustrate how to obtain background information using previous literature based on a systematic literature search and by using expert knowledge. Finally, we show how to translate this knowledge into prior distributions and we illustrate the importance of conducting a prior sensitivity analysis. Although our example is from the trauma field, the techniques we illustrate can be applied to any field.近年来，贝叶斯分析（Bayesian analysis）的研究热度持续攀升。然而，目前鲜有研究尝试通过先验分布（prior distribution）直接将背景知识融入分析流程。当因有限数据（limited data）导致一个或多个潜在群体规模较小时，该思路在潜在增长混合模型（latent growth mixture modeling）场景中尤为实用。我们认为，贝叶斯统计（Bayesian statistics）在有限数据场景中具备显著优势，但前提是能够通过先验分布将背景知识融入分析过程。我们通过一份包含烧伤患者的数据集（data set），遵循WAMBS检查表（WAMBS-checklist）的步骤，基于贝叶斯框架分析了创伤后应激症状（posttraumatic stress symptoms）的发展轨迹，以此阐明上述优势。在本次示例中，我们展示了如何通过系统文献检索获取既往研究文献，结合专家知识来获取背景信息。最后，我们演示了如何将此类背景知识转化为先验分布，并阐明了开展先验敏感性分析（prior sensitivity analysis）的重要性。尽管本次示例取自创伤医学领域，但我们所展示的分析技术可推广至任意研究领域。