University Hospital of Psychiatry, University of Zurich, Zurich, Switzerland.
BMC Psychiatry. 2021 Mar 5;21(1):129. doi: 10.1186/s12888-021-03094-5.
Conditional power of network meta-analysis (NMA) can support the planning of randomized controlled trials (RCTs) assessing medical interventions. Conditional power is the probability that updating existing inconclusive evidence in NMA with additional trial(s) will result in conclusive evidence, given assumptions regarding trial design, anticipated effect sizes, or event probabilities.
The present work aimed to estimate conditional power for potential future trials on antidepressant treatments. Existing evidence was based on a published network of 502 RCTs conducted between 1979-2018 assessing acute antidepressant treatment in major depressive disorder (MDD). Primary outcomes were efficacy in terms of the symptom change on the Hamilton Depression Scale (HAMD) and tolerability in terms of the dropout rate due to adverse events. The network compares 21 antidepressants consisting of 231 relative treatment comparisons, 164 (efficacy) and 127 (tolerability) of which are currently assumed to have inconclusive evidence.
Required sample sizes to achieve new conclusive evidence with at least 80% conditional power were estimated to range between N = 894 - 4190 (efficacy) and N = 521 - 1246 (tolerability). Otherwise, sample sizes ranging between N = 49 - 485 (efficacy) and N = 40 - 320 (tolerability) may require stopping for futility based on a boundary at 20% conditional power. Optimizing trial designs by considering multiple trials that contribute both direct and indirect evidence, anticipating alternative effect sizes or alternative event probabilities, may increase conditional power but required sample sizes remain high. Antidepressants having the greatest conditional power associated with smallest required sample sizes were identified as those on which current evidence is low, i.e., clomipramine, levomilnacipran, milnacipran, nefazodone, and vilazodone, with respect to both outcomes.
The present results suggest that conditional power to achieve new conclusive evidence in ongoing or future trials on antidepressant treatments is low. Limiting the use of the presented conditional power analysis are primarily due to the estimated large sample sizes which would be required in future trials as well as due to the well-known small effect sizes in antidepressant treatments. These findings may inform researchers and decision-makers regarding the clinical relevance and justification of research in ongoing or future antidepressant RCTs in MDD.
网络荟萃分析（NMA）的条件效力可以支持评估医学干预措施的随机对照试验（RCT）的规划。条件效力是指在给定试验设计、预期效应大小或事件概率假设的情况下，用额外的试验更新 NMA 中现有不确定证据将得出确定性结论的概率。
本研究旨在估计未来抗抑郁治疗试验的条件效力。现有证据基于一项已发表的网络，其中包括 1979 年至 2018 年间进行的 502 项 RCT，评估了重度抑郁症（MDD）中急性抗抑郁治疗。主要结局是汉密尔顿抑郁量表（HAMD）上症状变化的疗效和因不良事件而导致的停药率的耐受性。该网络比较了 21 种抗抑郁药，包括 231 种相对治疗比较，其中 164 种（疗效）和 127 种（耐受性）目前被认为证据不确定。
为了达到至少 80%的条件效力的新的确定性证据，需要估计的新样本量范围为 N = 894-4190（疗效）和 N = 521-1246（耐受性）。否则，根据 20%条件效力的界限，可能需要停止无效性试验，所需样本量范围为 N = 49-485（疗效）和 N = 40-320（耐受性）。通过考虑多个既能提供直接证据又能提供间接证据的试验、预测替代效应大小或替代事件概率，优化试验设计，可以提高条件效力，但所需样本量仍然很高。与当前证据较低的抗抑郁药相比，与两种结局相关的具有最大条件效力和最小所需样本量的抗抑郁药被确定为氯米帕明、左米那普仑、米那普仑、奈法唑酮和维拉佐酮。
本研究结果表明，正在进行或未来的抗抑郁治疗试验中获得新的确定性证据的条件效力较低。本条件效力分析的主要局限性是未来试验所需的估计大样本量，以及抗抑郁治疗中众所周知的小效应量。这些发现可能为正在进行或未来的 MDD 抗抑郁 RCT 中的研究的临床相关性和合理性提供信息。
