Chen Haiying, Ambrosius Walter T, Murphy Terrence E, Fielding Roger, Pahor Marco, Santanasto Adam, Tudor-Locke Catrine, Jack Rejeski W, Miller Michael E
Department of Biostatistical Sciences, School of Medicine, Wake Forest University, Winston-Salem, North Carolina.
Department of Internal Medicine, School of Medicine, Yale University, Hartford, Connecticut.
J Am Geriatr Soc. 2017 Dec;65(12):2566-2571. doi: 10.1111/jgs.15078. Epub 2017 Sep 8.
When a 400-m walk test with time constraint (in 15 minutes) is administered, analysis of the associated 400-m gait speed can be challenging because some older adults are unable to complete the distance in time (noncompleters). A simplistic imputation method is to calculate the observed speeds of the noncompleters as the partially completed distance divided by the corresponding amount of elapsed time as an estimate of gait speed over the full 400-m distance. This common practice has not been validated to the best of our knowledge. We propose a Bayesian multiple imputation (MI) method to impute the unobserved 400-m gait speed for noncompleters. Briefly, MI is performed under the assumption that the unobserved 400-m gait speed of noncompleters is left-censored from a normal distribution. We illustrate the application of the Bayesian MI method using longitudinal data collected from the Lifestyle Interventions for Elders (LIFE) study. A simulation study was performed to assess the bias in estimation of the mean 400-m gait speed using both methods. The results indicate that the simplistic imputation method tends to overestimate the population mean, whereas the Bayesian MI method yields minimal bias as the sample size increases.
当进行有时间限制(15分钟内)的400米步行测试时,对相关的400米步态速度进行分析可能具有挑战性,因为一些老年人无法按时完成该距离(未完成者)。一种简单的插补方法是将未完成者的观察速度计算为部分完成的距离除以相应的经过时间,以此作为整个400米距离上步态速度的估计值。据我们所知,这种常见做法尚未得到验证。我们提出一种贝叶斯多重插补(MI)方法来插补未完成者未观察到的400米步态速度。简而言之,MI是在未完成者未观察到的400米步态速度从正态分布中左删失的假设下进行的。我们使用从老年人生活方式干预(LIFE)研究中收集的纵向数据说明了贝叶斯MI方法的应用。进行了一项模拟研究,以评估使用这两种方法估计平均400米步态速度时的偏差。结果表明,简单的插补方法往往会高估总体均值,而随着样本量的增加,贝叶斯MI方法产生的偏差最小。