College of Veterinary Medicine, Nanjing Agricultural University, Nanjing, China.
Department of Mathematics and Statistics, University of Otago, Dunedin, New Zealand.
Prev Vet Med. 2024 Apr;225:106144. doi: 10.1016/j.prevetmed.2024.106144. Epub 2024 Feb 10.
In diagnostic accuracy studies, a commonly employed approach involves dichotomizing continuous data and subsequently analyzing them using a Bayesian latent class model (BLCM), often relying on binomial or multinomial distributions, rather than preserving their continuous nature. However, this procedure can inadvertently lead to less reliable outcomes due to the inherent loss of information when converting the original continuous measurements into binary values. Through comprehensive simulations, we demonstrated the limitations and disadvantages of dichotomizing continuous biomarkers from two correlated tests. Our findings highlighted notable disparities between the true values and the model estimates as a result of dichotomization. We discovered the crucial significance of selecting a reference test with high diagnostic accuracy in test evaluation in order to obtain reliable estimates of test accuracy and prevalences. Our study served as a call to action for veterinary researchers to exercise caution when utilizing dichotomization.
在诊断准确性研究中，一种常用的方法是将连续数据二值化，然后使用贝叶斯潜在类别模型（BLCM）进行分析，通常依赖于二项式或多项式分布，而不是保留其连续性质。然而，这种方法可能会由于将原始连续测量值转换为二进制值时会丢失信息，从而导致结果不可靠。通过全面的模拟，我们从两个相关测试中演示了将连续生物标志物二值化的局限性和缺点。我们的发现强调了由于二值化而导致真实值和模型估计值之间存在显著差异。我们发现，在测试评估中选择具有高诊断准确性的参考测试对于获得测试准确性和患病率的可靠估计非常重要。我们的研究呼吁兽医研究人员在使用二值化时要谨慎。
