Mayne Tracy J, Whalen Edward, Vu An
Outcomes Research, Pfizer Pharmaceuticals, 235 East 42nd Street, 205/9/10, New York, NY 10017, USA.
J Clin Epidemiol. 2006 Mar;59(3):217-23. doi: 10.1016/j.jclinepi.2005.07.006. Epub 2005 Oct 13.
Recent studies have calculated number needed to treat (NNT) estimates based on annualized rates; however, the ramifications of altering the NNT statistic have not yet been explored in the literature. Here we introduce the concept of annualized NNT (ANNT), and apply it to data from randomized controlled trials (RCTs).
Incidence rates from RCTs for serious adverse events for three medicines were compared to an older class of drugs. NNT and ANNT were calculated from the event rates for these events.
Based on the data, the NNT to prevent one adverse event a year vs. older medications was drug A, ANNT = 88; drug B, ANNT = 77; drug C, ANNT = 68. Equivalent calculations based on Bayesian statistics are drug C, ANNT = 54; drug B, ANNT = 49. Drug A produced a bimodal distribution, with one mode within the NNT range and the other in the number needed to harm range.
NNT can erroneously inflate differences between treatments when based on absolute and not differential safety. We propose that NNT be limited to acute conditions with short-term, well-defined treatment courses, and that ANNT be used for chronic conditions.
近期研究基于年化率计算所需治疗人数（NNT）估计值；然而，改变NNT统计量的影响在文献中尚未得到探讨。在此，我们引入年化NNT（ANNT）的概念，并将其应用于随机对照试验（RCT）的数据。
将三种药物严重不良事件的RCT发生率与一类较老的药物进行比较。根据这些事件的发生率计算NNT和ANNT。
基于数据，与较老药物相比，每年预防一例不良事件的NNT为：药物A，ANNT = 88；药物B，ANNT = 77；药物C，ANNT = 68。基于贝叶斯统计的等效计算为：药物C，ANNT = 54；药物B，ANNT = 49。药物A产生双峰分布，一个峰在NNT范围内，另一个峰在伤害所需人数范围内。
当基于绝对安全性而非差异安全性时，NNT可能会错误地夸大治疗之间的差异。我们建议NNT仅限于具有短期、明确治疗疗程的急性病症，而ANNT用于慢性病症。
