Interdisciplinary Program in Medical Informatics, Seoul National University College of Medicine, Seoul, South Korea.
Institute of Health Policy and Management, Medical Research Center, Seoul National University, Seoul, South Korea.
BMC Med Res Methodol. 2021 Jun 1;21(1):113. doi: 10.1186/s12874-021-01290-1.
In a star-shaped network, pairwise comparisons link treatments with a reference treatment (often placebo or standard care), but not with each other. Thus, comparisons between non-reference treatments rely on indirect evidence, and are based on the unidentifiable consistency assumption, limiting the reliability of the results. We suggest a method of performing a sensitivity analysis through data imputation to assess the robustness of results with an unknown degree of inconsistency.
The method involves imputation of data for randomized controlled trials comparing non-reference treatments, to produce a complete network. The imputed data simulate a situation that would allow mixed treatment comparison, with a statistically acceptable extent of inconsistency. By comparing the agreement between the results obtained from the original star-shaped network meta-analysis and the results after incorporating the imputed data, the robustness of the results of the original star-shaped network meta-analysis can be quantified and assessed. To illustrate this method, we applied it to two real datasets and some simulated datasets.
Applying the method to the star-shaped network formed by discarding all comparisons between non-reference treatments from a real complete network, 33% of the results from the analysis incorporating imputed data under acceptable inconsistency indicated that the treatment ranking would be different from the ranking obtained from the star-shaped network. Through a simulation study, we demonstrated the sensitivity of the results after data imputation for a star-shaped network with different levels of within- and between-study variability. An extended usability of the method was also demonstrated by another example where some head-to-head comparisons were incorporated.
Our method will serve as a practical technique to assess the reliability of results from a star-shaped network meta-analysis under the unverifiable consistency assumption.
在星形网络中，两两比较将治疗方法与对照治疗（通常是安慰剂或标准护理）联系起来，但不与彼此联系。因此，非对照治疗之间的比较依赖于间接证据，并基于不可识别的一致性假设，限制了结果的可靠性。我们建议通过数据插补进行敏感性分析的方法，以评估在未知程度不一致的情况下结果的稳健性。
该方法涉及对比较非对照治疗的随机对照试验进行数据插补，以生成完整的网络。插补的数据模拟了允许混合治疗比较的情况，具有可接受的统计不一致程度。通过比较原始星形网络荟萃分析获得的结果与纳入插补数据后的结果之间的一致性，可以量化和评估原始星形网络荟萃分析结果的稳健性。为了说明这种方法，我们将其应用于两个真实数据集和一些模拟数据集。
将该方法应用于从完整网络中丢弃所有非对照治疗之间比较的星形网络，分析纳入插补数据后在可接受的不一致性下，33%的结果表明治疗排名将与从星形网络获得的排名不同。通过一项模拟研究，我们展示了在具有不同内部和研究间变异性水平的星形网络中，数据插补后结果的敏感性。另一个例子也证明了该方法的扩展可用性，其中纳入了一些头对头比较。
我们的方法将成为评估在不可验证的一致性假设下星形网络荟萃分析结果可靠性的实用技术。
