当前位置: X-MOL 学术Stat › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Alternative parameterization approaches for modelling risk assessment in the presence of imbalance: An example using parotid malignancy
Stat ( IF 1.7 ) Pub Date : 2022-07-27 , DOI: 10.1002/sta4.489
Kaming Lo 1 , Shari Messinger 2 , Christopher Fundakowski 3 , Zoukaa Sargi 4
Affiliation  

In collaborative statistics, logistic regression models are commonly used with binary outcomes and reference cell coding for categorical predictors. However, despite the usefulness of reference cell coding schemes under many investigative objectives, it is not always appropriate to address research questions of interest. Investigators often consider modifying research questions to align with inference possible using reference cell coding. Alternative coding schemes can offer a more appropriate approach for the investigation. We explored application of deviation from means coding in determining how results from a diagnostic tool provide additional information on a patient's risk of disease with respect to the overall (naïve) risk at clinical presentation. We compared model parameterizations between using reference cell coding and deviation from means coding, by both unweighted and weighted approaches, for assessing risk of parotid malignancy, comparing patients with indeterminate FNAB results with the general (naïve) risk among presenting patients. Unweighted deviation from means coding estimates a 1.2-fold increase in the odds of malignancy with an indeterminate FNAB result compared to the naïve odds of malignancy at clinical presentation (OR: 1.21 [95% CI: 1.63–2.32], p = 0.5699). The weighted approach takes into account the imbalance in the presenting population and estimates an increased risk (OR: 2.54 [95% CI: 1.52–4.26], p = 0.0004), which is more accurately representing the naïve risk at presentation and to answer the research question of interest. Using standard reference cell coding, an indeterminate result is associated with significantly higher odds than that of a negative result (OR: 5.20 [95% CI: 2.22–12.20], p = 0.0001), but this does not inform us as to the risk with respect to that inherent at clinical presentation and thus may not be useful for clinical decision making.

中文翻译:

在存在不平衡的情况下建模风险评估的替代参数化方法:使用腮腺恶性肿瘤的示例

在协作统计中,逻辑回归模型通常与二元结果和分类预测变量的参考单元格编码一起使用。然而,尽管参考细胞编码方案在许多研究目标下很有用,但并不总是适合解决感兴趣的研究问题。调查人员经常考虑修改研究问题,以与使用参考细胞编码的可能推理保持一致。替代编码方案可以为调查提供更合适的方法。我们探索了偏离均值编码的应用,以确定诊断工具的结果如何提供有关患者疾病风险相对于临床表现的总体(初始)风险的额外信息。我们通过未加权和加权方法比较了使用参考细胞编码和均值编码偏差之间的模型参数化,以评估腮腺恶性肿瘤的风险,将 FNAB 结果不确定的患者与就诊患者的一般(幼稚)风险进行比较。未加权均值编码偏差估计,与临床表现时恶性肿瘤的天真几率相比,FNAB 结果不确定的恶性肿瘤几率增加 1.2 倍(OR:1.21 [95% CI:1.63–2.32],p = 0.5699)。加权方法考虑了就诊人群的不平衡并估计风险增加(OR:2.54 [95% CI:1.52–4.26],p = 0.0004),这更准确地代表了就诊时的天真风险并回答了感兴趣的研究问题。使用标准参考细胞编码,不确定结果的相关几率明显高于阴性结果(OR:5.20 [95% CI:2.22–12.20],p = 0.0001),但这并未告知我们风险关于临床表现所固有的,因此可能对临床决策没有用处。
更新日期:2022-07-27
down
wechat
bug