Causal inference cohort re-analysis of the 'CARES' trial to better understand the roles of colchicine, allopurinol, and febuxostat on cardiovascular events among patients with gout

Gout is one of the most common inflammatory arthritis, which reportedly affects about 4% of adults in the US. Gout is caused by the deposition of urate crystals. Thus, urate-lowering medications such as febuxostat and allopurinol are used to prevent future gout attacks. Concerns about the cardiovascular safety of urate-lowering medications resulted in two major clinical trials among gout patients: Cardiovascular Safety of Febuxostat and Allopurinol in Patients with Gout and Cardiovascular Morbidities (CARES) and Febuxostat versus Allopurinol Streamlined Trial (FAST). However, these studies gave somewhat contradictory results.

CARES showed an increase in deaths among febuxostat users in one of the analyses. However, many patients did not complete all planned study visits in this trial, making the interpretation difficult. On the other hand, some analyses in FAST favored febuxostat over allopurinol. However, there were substantial differences in the proportion of patients stopping urate-lowering therapy and the use of colchicine in FAST. These idiosyncrasies have led to lingering questions which call for more advanced analytical approaches.

1. Did the use of colchicine (cardio-protective in other trials) affect the CARES trial results?
2. Did the higher loss to follow-up in the allopurinol group in CARES bias the results?

In this proposed study on CARES, we will use advanced causal inference methods to address these questions. Causal inference is an epidemiological approach to measure the accurate effects of treatment. Although clinical trials are the gold standard of medical research, more advanced analytical methods are needed to overcome imperfections of clinical trials and accurately estimate the impact of medications.

In Aim 1, we will first compare the effect of colchicine prophylaxis on heart attacks compared to no prophylaxis or non-steroidal anti-inflammatory drug prophylaxis using the data from CARES. We will then conduct causal mediation analysis, an approach quantifying the extent to which an intermediate factor (colchicine use during the study in this case) impacted the trial results. In Aim 2, We will estimate the "per-protocol effect" of febuxostat compared to allopurinol on heart attacks. This "per-protocol effect" approach estimates the effect of these medications statistically correcting for the influence from patients who did not complete all planned study visits. Our methodologically innovative project is expected to help reconcile the discrepant results from CARES and FAST, providing reassurance to practitioners. We will also demonstrate the usefulness of causal inference methods in clinical trial analysis for the rheumatology research community at large. We will further disseminate these advanced methods via online and offline methodology tutorials.

痛风（Gout）是最常见的炎症性关节炎之一，据报道在美国影响约4%的成年人群。痛风由尿酸盐结晶沉积引发，因此临床常使用非布司他（febuxostat）、别嘌醇（allopurinol）等降尿酸药物（urate-lowering medications）预防后续痛风发作。由于对降尿酸药物心血管安全性的担忧，针对痛风患者开展了两项大型临床试验：《痛风合并心血管疾病患者中非布司他与别嘌醇的心血管安全性研究（Cardiovascular Safety of Febuxostat and Allopurinol in Patients with Gout and Cardiovascular Morbidities, CARES）》与《非布司他对比别嘌醇简化试验（Febuxostat versus Allopurinol Streamlined Trial, FAST）》。但两项研究的结果存在一定矛盾。
在一项分析中，CARES研究显示非布司他使用者的死亡风险有所升高，但该试验中大量患者未完成全部计划内的研究随访，导致结果解读困难。另一方面，FAST研究的部分分析结果支持非布司他优于别嘌醇，但该研究中停止降尿酸治疗的患者比例以及秋水仙碱（colchicine）的使用情况均存在显著差异。这些特异性特征引发了诸多悬而未决的问题，亟需更先进的分析方法。
1. 秋水仙碱（在其他试验中被证实具有心血管保护作用）的使用是否影响了CARES研究的结果？
2. CARES研究中别嘌醇组更高的失访率是否对研究结果产生了偏倚？
在这项针对CARES研究的拟开展研究中，我们将采用先进的因果推断（causal inference）方法来解决上述问题。因果推断是一种用于准确衡量治疗效果的流行病学研究方法。尽管临床试验是医学研究的金标准，但仍需更先进的分析方法来弥补临床试验的固有缺陷，精准评估药物的临床影响。
在目标1中，我们将首先利用CARES研究的数据，对比秋水仙碱预防与未预防、或非甾体类抗炎药（non-steroidal anti-inflammatory drug, NSAIDs）预防对心肌梗死发生的影响。随后我们将开展因果中介分析，该方法可量化中间变量（本研究中为研究期间的秋水仙碱使用情况）对试验结果的影响程度。
在目标2中，我们将估算非布司他对比别嘌醇对心肌梗死的“符合方案效应（per-protocol effect）”。该“符合方案效应”分析方法可通过统计学手段校正未完成全部计划内研究随访的患者所带来的影响，从而评估药物的真实效应。
本研究在方法学上具有创新性，有望解决CARES与FAST两项研究结果不一致的问题，为临床医师提供更可靠的用药依据。同时，本研究也将向广大风湿病学研究群体展示因果推断方法在临床试验分析中的应用价值。我们还将通过线上线下的方法学教程，进一步推广这些先进的分析方法。