Department of Biostatistics & Bioinformatics, Duke University, Durham, North Carolina, USA.
Center for Predictive Medicine, Duke Clinical Research Institute, Durham, North Carolina, USA.
J Am Med Inform Assoc. 2019 May 1;26(5):429-437. doi: 10.1093/jamia/ocy188.
Participants enrolled into randomized controlled trials (RCTs) often do not reflect real-world populations. Previous research in how best to transport RCT results to target populations has focused on weighting RCT data to look like the target data. Simulation work, however, has suggested that an outcome model approach may be preferable. Here, we describe such an approach using source data from the 2 × 2 factorial NAVIGATOR (Nateglinide And Valsartan in Impaired Glucose Tolerance Outcomes Research) trial, which evaluated the impact of valsartan and nateglinide on cardiovascular outcomes and new-onset diabetes in a prediabetic population.
Our target data consisted of people with prediabetes serviced at the Duke University Health System. We used random survival forests to develop separate outcome models for each of the 4 treatments, estimating the 5-year risk difference for progression to diabetes, and estimated the treatment effect in our local patient populations, as well as subpopulations, and compared the results with the traditional weighting approach.
Our models suggested that the treatment effect for valsartan in our patient population was the same as in the trial, whereas for nateglinide treatment effect was stronger than observed in the original trial. Our effect estimates were more efficient than the weighting approach and we effectively estimated subgroup differences.
The described method represents a straightforward approach to efficiently transporting an RCT result to any target population.
纳入随机对照试验（RCT）的参与者通常无法反映真实人群。此前关于如何将 RCT 结果最佳传递给目标人群的研究主要集中在对 RCT 数据进行加权以使其与目标数据相似。然而，模拟研究表明，结果模型方法可能更可取。在这里，我们使用来自 2×2 析因 NAVIGATOR（那格列奈和缬沙坦在糖耐量受损结局研究）试验的源数据描述了这种方法，该试验评估了缬沙坦和那格列奈对糖尿病前期人群心血管结局和新发糖尿病的影响。
我们的目标数据包括在杜克大学卫生系统接受治疗的糖尿病前期患者。我们使用随机生存森林为每种 4 种治疗方法分别开发了结果模型，估计进展为糖尿病的 5 年风险差异，并估计了我们当地患者人群以及亚人群中的治疗效果，并将结果与传统加权方法进行了比较。
我们的模型表明，缬沙坦在我们患者人群中的治疗效果与试验相同，而那格列奈的治疗效果强于原始试验观察到的效果。我们的效果估计比加权方法更有效，并且可以有效地估计亚组差异。
所描述的方法代表了一种将 RCT 结果高效传递给任何目标人群的简单方法。
