Midwifery Research and Education Unit, Hannover Medical School, Hannover, Germany.
Biostatistics and Research Decision Sciences, MSD Europe Inc., Brussels, Belgium.
Res Synth Methods. 2021 Jul;12(4):475-490. doi: 10.1002/jrsm.1478. Epub 2021 Feb 21.
Conducting sensitivity analyses is an integral part of the systematic review process to explore the robustness of results derived from the primary analysis. When the primary analysis results can be sensitive to assumptions concerning a model's parameters (e.g., missingness mechanism to be missing at random), sensitivity analyses become necessary. However, what can be concluded from sensitivity analyses is not always clear. For instance, in a pairwise meta-analysis (PMA) and network meta-analysis (NMA), conducting sensitivity analyses usually boils down to examining how 'similar' the estimated treatment effects are from different re-analyses to the primary analysis or placing undue emphasis on the statistical significance. To establish objective decision rules regarding the robustness of the primary analysis results, we propose an intuitive index, which uses the whole distribution of the estimated treatment effects under the primary and alternative re-analyses. This novel index is compared to an objective threshold to infer the presence or lack of robustness. In the case of missing outcome data, we additionally propose a graph that contrasts the primary analysis results to those of alternative scenarios about the missingness mechanism in the compared arms. When robustness is questioned according to the proposed index, the suggested graph can demystify the scenarios responsible for producing inconsistent results to the primary analysis. The proposed decision framework is immediately applicable to a broad set of sensitivity analyses in PMA and NMA. We illustrate our framework in the context of missing outcome data in both PMA and NMA using published systematic reviews.
进行敏感性分析是系统评价过程的一个组成部分，旨在探索主要分析结果的稳健性。当主要分析结果可能对模型参数的假设（例如，随机缺失机制）敏感时，就需要进行敏感性分析。然而，从敏感性分析中得出的结论并不总是清晰的。例如，在成对荟萃分析（PMA）和网络荟萃分析（NMA）中，进行敏感性分析通常归结为检查不同重新分析与主要分析之间估计的治疗效果有多么相似，或者过分强调统计显著性。为了确定主要分析结果的稳健性制定客观的决策规则，我们提出了一个直观的指标，该指标使用主要和替代重新分析下估计的治疗效果的整个分布。将这个新指标与客观阈值进行比较，以推断主要分析结果的稳健性。在缺失结局数据的情况下，我们还提出了一个图表，对比主要分析结果和比较组中缺失机制的替代情景的结果。根据提出的指标质疑稳健性时，建议的图表可以揭示导致与主要分析结果不一致的情景。所提出的决策框架可以立即应用于 PMA 和 NMA 中的广泛敏感性分析。我们使用已发表的系统评价，在 PMA 和 NMA 中缺失结局数据的情况下说明了我们的框架。
