Harvard School of Public Health, Department of Epidemiology, Boston, MA 02115, USA.
Int J Eat Disord. 2012 Jul;45(5):677-84. doi: 10.1002/eat.20958. Epub 2011 Aug 31.
Latent class analysis (LCA) has frequently been used to identify qualitatively distinct phenotypes of disordered eating. However, little consideration has been given to methodological factors that may influence the accuracy of these results.
Monte Carlo simulations were used to evaluate methodological factors that may influence the accuracy of LCA under scenarios similar to those seen in previous eating disorder research.
Under these scenarios, the aBIC provided the best overall performance as an information criterion, requiring sample sizes of 300 in both balanced and unbalanced structures to achieve accuracy proportions of at least 80%. The BIC and cAIC required larger samples to achieve comparable performance, while the AIC performed poorly universally in comparison. Accuracy generally was lower with unbalanced classes, fewer indicators, greater or nonrandom missing data, conditional independence assumption violations, and lower base rates of indicator endorsement.
These results provide critical information for interpreting previous LCA research and designing future classification studies.
潜类分析（LCA）经常被用于识别饮食失调的定性不同表型。然而，很少有考虑到可能影响这些结果准确性的方法学因素。
使用蒙特卡罗模拟来评估可能影响 LCA 准确性的方法学因素，这些因素类似于先前饮食失调研究中所见的情况。
在这些情况下，aBIC 作为信息标准提供了最佳的整体性能，需要在平衡和不平衡结构中各有 300 个样本，以达到至少 80%的准确性比例。BIC 和 cAIC 需要更大的样本量才能达到可比的性能，而 AIC 则普遍表现不佳。准确性通常在不平衡类、较少的指标、较大或非随机缺失数据、条件独立性假设违反以及指标认可的基本率较低的情况下较低。
这些结果为解释先前的 LCA 研究和设计未来的分类研究提供了重要信息。
