Using the PreventS trial data, our objective is to estimate average effects of a Health Wellness Coaching (HWC) intervention on improvement of cardiovascular health at 9 months post randomization and in three consecutive 3-month periods over 9 months post randomization. Conventional approaches, including instrumental variable models, are not applicable in the presence of multiple correlated multivalued exposures and unmeasured confounding. We propose a causal framework and its Bayesian modelling procedures to identify and estimate average effects of one or multiple multivalued exposures on one outcome in the presence of unmeasured confounding, noncompliance and missing data, in a two-arm randomized trial. We also propose estimation methods of unmeasured confounders, where the exposure and outcome distributions are conditional on unmeasured confounders and then unmeasured confounders are imputed as completely missing variables. Several types of model non-identifiability and possible solutions are described. There is a risk that estimation methods of unmeasured confounders can fail when multiple contradictory posterior solutions are produced. The random intercept outcome models that only adjust for unmeasured confounding in the outcome distribution are proposed as a good surrogate causal model in this case, and they need further development. There is evidence that the HWC intervention is beneficial to cardiovascular health at 9 months post randomization. On average, completing one HWC session improves the Life's Simple Seven total score by 0.16 (0.09, 0.22) and reduces systolic blood pressure by 0.54 (0.19, 0.90) mm Hg. There is also evidence that the HWC intervention has a larger beneficial effect on cardiovascular health during 3 months post randomization. There is no clear evidence that the HWC intervention benefits or harms mental health. The complete abstract is in the article.

翻译：基于PreventS试验数据，本研究旨在评估健康保健辅导干预对随机分组后9个月及此后每3个月周期（共3个周期）心血管健康改善的平均效应。当存在多重相关多值暴露变量及未测量混杂因素时，传统方法（包括工具变量模型）不再适用。我们提出了一套因果框架及其贝叶斯建模程序，用于在双组随机试验中识别和估计存在未测量混杂、不依从性和缺失数据时，一个或多个多值暴露变量对单一结局的平均效应。我们还提出了未测量混杂因素的估计方法，其中暴露和结局分布以未测量混杂因素为条件，随后将未测量混杂因素作为完全缺失变量进行插补。描述了多种模型不可识别性类型及其可能解决方案。当产生多个矛盾的后验解时，未测量混杂因素的估计方法存在失效风险。在此情况下，仅调整结局分布中未测量混杂因素的随机截距结局模型被提出作为良好的替代因果模型，该模型仍需进一步开发。现有证据表明，HWC干预对随机分组后9个月的心血管健康有益。平均而言，每完成一次HWC辅导可使"简单生活七要素"总分提高0.16（0.09，0.22）分，并使收缩压降低0.54（0.19,0.90）mmHg。另有证据表明，HWC干预在随机分组后3个月内对心血管健康具有更大的有益效应。目前尚无明确证据表明HWC干预对心理健康产生有益或有害影响。完整摘要详见正文。