Two important considerations in clinical research studies are proper evaluations of internal and external validity. While randomized clinical trials can overcome several threats to internal validity, they may be prone to poor external validity. Conversely, large prospective observational studies sampled from a broadly generalizable population may be externally valid, yet susceptible to threats to internal validity, particularly confounding. Thus, methods that address confounding and enhance transportability of study results across populations are essential for internally and externally valid causal inference, respectively. These issues persist for another problem closely related to transportability known as data-fusion. We develop a calibration method to generate balancing weights that address confounding and sampling bias, thereby enabling valid estimation of the target population average treatment effect. We compare the calibration approach to two additional doubly robust methods that estimate the effect of an intervention on an outcome within a second, possibly unrelated target population. The proposed methodologies can be extended to resolve data-fusion problems that seek to evaluate the effects of an intervention using data from two related studies sampled from different populations. A simulation study is conducted to demonstrate the advantages and similarities of the different techniques. We also test the performance of the calibration approach in a motivating real data example comparing whether the effect of biguanides vs sulfonylureas—the two most common oral diabetes medication classes for initial treatment—on all-cause mortality described in a historical cohort applies to a contemporary cohort of US Veterans with diabetes.

中文翻译：

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可运输性和与观测数据的数据融合的校准方法

临床研究中的两个重要考虑因素是内部和外部有效性的正确评估。虽然随机临床试验可以克服内部有效性的多种威胁，但它们可能容易出现外部有效性较差的问题。相反，从广泛普遍人群中抽样的大型前瞻性观察研究可能具有外部有效性，但容易受到内部有效性的威胁，尤其是混杂性。因此，解决混杂问题并增强研究结果在人群中的可移植性的方法对于内部和外部有效的因果推断分别至关重要。这些问题仍然存在于另一个与可传输性密切相关的问题（称为数据融合）中。我们开发了一种校准方法来生成平衡权重，以解决混杂和抽样偏差，从而能够有效估计目标人群平均治疗效果。我们将校准方法与另外两种双稳健方法进行比较，后者估计干预措施对第二个可能不相关的目标人群内结果的影响。所提出的方法可以扩展到解决数据融合问题，这些问题试图使用从不同人群中采样的两项相关研究的数据来评估干预措施的效果。进行模拟研究以证明不同技术的优点和相似之处。我们还在一个激励性的真实数据示例中测试了校准方法的性能，比较双胍类药物与磺酰脲类药物（用于初始治疗的两种最常见的口服糖尿病药物类别）对历史队列中描述的全因死亡率的影响是否适用于当代患有糖尿病的美国退伍军人队列。