Frailty is the loss of ability to withstand a physiological stressor and is associated with multiple adverse outcomes in older people. Trials to prevent or ameliorate frailty are in their infancy. A range of different outcome measures have been proposed, but current measures require either large sample sizes, long follow-up, or do not directly measure the construct of frailty. We propose a composite outcome for frailty prevention trials, comprising progression to the frail state, death, or being too unwell to continue in a trial. To determine likely event rates, we used data from the English Longitudinal Study for Ageing, collected 4 years apart. We calculated transition rates between non-frail, prefrail, frail or loss to follow up due to death or illness. We used Markov state transition models to interpolate one- and two-year transition rates and performed sample size calculations for a range of differences in transition rates using simple and composite outcomes. The frailty category was calculable for 4650 individuals at baseline (2226 non-frail, 1907 prefrail, 517 frail); at follow up, 1282 were non-frail, 1108 were prefrail, 318 were frail and 1936 had dropped out or were unable to complete all tests for frailty. Transition probabilities for those prefrail at baseline, measured at wave 4 were respectively 0.176, 0.286, 0.096 and 0.442 to non-frail, prefrail, frail and dead/dropped out. Interpolated transition probabilities were 0.159, 0.494, 0.113 and 0.234 at two years, and 0.108, 0.688, 0.087 and 0.117 at one year. Required sample sizes for a two-year outcome in a two-arm trial were between 1040 and 7242 for transition from prefrailty to frailty alone, 246 to 1630 for transition to the composite measure, and 76 to 354 using the composite measure with an ordinal logistic regression approach. Use of a composite outcome for frailty trials offers reduced sample sizes and could ameliorate the effect of high loss to follow up inherent in such trials due to death and illness.

中文翻译：

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制定衰弱预防试验的综合结果衡量标准——基本原理、推导以及与其他候选衡量标准的样本量比较


虚弱是指丧失承受生理压力的能力，并与老年人的多种不良后果相关。预防或改善虚弱的试验尚处于起步阶段。已经提出了一系列不同的结果衡量标准，但目前的衡量标准要么需要大样本量，要么需要长期随访，要么不直接衡量虚弱的结构。我们提出了虚弱预防试验的综合结果，包括进展为虚弱状态、死亡或因身体不适而无法继续试验。为了确定可能的事件发生率，我们使用了英国老龄化纵向研究的数据，该研究间隔 4 年收集。我们计算了非衰弱、预衰弱、衰弱或因死亡或疾病而失访之间的转换率。我们使用马尔可夫状态转换模型来插值一年和两年的转换率，并使用简单和复合结果对转换率的一系列差异进行样本量计算。基线时可计算 4650 名个体的虚弱类别（2226 名非虚弱者、1907 名虚弱者、517 名虚弱者）；随访时，1282 人未出现衰弱，1108 人处于衰弱前状态，318 人处于衰弱状态，1936 人退出或无法完成所有衰弱测试。在第 4 波测量时，基线前衰弱者向非衰弱、前衰弱、衰弱和死亡/退出的过渡概率分别为 0.176、0.286、0.096 和 0.442。两年内插的转移概率分别为 0.159、0.494、0.113 和 0.234，一年内插的转移概率分别为 0.108、0.688、0.087 和 0.117。 在双臂试验中，从衰弱前期过渡到单独衰弱所需的两年结果所需样本量为 1040 至 7242，过渡到综合测量所需样本量为 246 至 1630，使用序数 Logistic 综合测量时所需样本量为 76 至 354。回归方法。在衰弱试验中使用复合结果可以减少样本量，并可以改善此类试验中因死亡和疾病而导致的高随访损失的影响。