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The PROBIT approach in estimating the prevalence of wasting: revisiting bias and precision

机译：估算浪费发生率的PROBIT方法：重新审视偏差和准确性

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Background The PROBIT methodology was presented in the 1995 World Health Organization Technical Report on Anthropometry as an alternative to the standard prevalence based method of measuring malnutrition in children. Theoretically the PROBIT method will always give a smaller standard error than the standard prevalence method in measuring malnutrition. A recent article by Dale et al. assessed the PROBIT method for measuring global acute malnutrition measure and found that the method was biased and the precision was superior only for sample sizes less than 150 when compared to the standard method. In a manner similar to Dale, our study further investigated the bias and precision of the PROBIT method for different sample sizes using simulated populations. Results The PROBIT method showed bias for each of the ten simulated populations, but the direction and magnitude of the average bias was changed depending on the simulated population. For a given simulated population, the average bias was relatively constant for all sample sizes drawn. The 95% half-width confidence interval was lower for the PROBIT method than the standard prevalence method regardless of the sample size or simulated population. The absolute difference in the confidence limits showed the most gains for the PROBIT method for the smaller samples sizes, but the ratio of confidence intervals was relatively constant across all sample sizes. Conclusions The PROBIT method will provide gains in precision regardless of the sample size, but the method may be biased. The direction and magnitude of the bias depends on the population it is drawn from.
机译：背景技术1995年世界卫生组织关于人体测量学的技术报告中介绍了PROBIT方法，以替代基于儿童患病率的基于患病率的标准方法。从理论上讲，在营养不良测量中，PROBIT方法将始终提供比标准流行率方法小的标准误差。 Dale等人的最新文章。评估了用于测量全球急性营养不良状况的PROBIT方法，发现该方法存在偏差，并且与标准方法相比，仅当样本量小于150时，精度才更高。以类似于Dale的方式，我们的研究进一步使用模拟总体研究了PROBIT方法对不同样本量的偏倚和精度。结果PROBIT方法对10个模拟种群均显示出偏差，但平均偏差的方向和大小随模拟种群而变化。对于给定的模拟总体，所有抽取的样本量的平均偏差相对恒定。无论样本量或模拟种群如何，PROBIT方法的95％半角置信区间均低于标准流行度方法。置信极限的绝对差异表明，对于较小的样本量，PROBIT方法获得的收益最大，但在所有样本量下，置信区间的比率相对恒定。结论无论样本大小如何，PROBIT方法都将提供更高的精度，但该方法可能会有偏差。偏差的方向和大小取决于其来源。

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