Lead Investigator: Sara Tedeschi, Brigham and Women’s Hospital
Title of Proposal Research: 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
Vivli Data Request: 7142
Funding Source: None
Potential Conflicts of Interest: Dr. Hyon Choi reports research support from Ironwood and Horizon and consulting fees from Ironwood, Selecta, Horizon, Takeda, Kowa, and Vaxart for unrelated projects. This proposed research project is not funded by any of these commercial entities.
Summary of the Proposed Research:
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.
Requested Studies:
A Multicenter, Randomized, Active-Control, Phase 3B Study to Evaluate the Cardiovascular Safety of Febuxostat and Allopurinol in Subjects With Gout and Cardiovascular Comorbidities
Data Contributor: Takeda
Study ID: NCT01101035
Sponsor ID: TMX-67_301