Lupo Philip J, Symanski Elaine, Waller D Kim, Chan Wenyaw, Canfield Mark A, Langlois Peter H, Mitchell Laura E
Division of Epidemiology and Disease Control, University of Texas School of Public Health, Houston, Texas, USA.
Birth Defects Res A Clin Mol Teratol. 2010 Aug;88(8):701-5. doi: 10.1002/bdra.20671.
In birth defect epidemiology, phenotypic subgroups are often combined into a composite phenotype in an effort to increase statistical power. Although the validity of using composite phenotypes has been questioned, formal evaluations of the underlying assumption of effect homogeneity across component phenotypes have not been conducted.
Polytomous logistic regression was used to assess effect heterogeneity of several generally accepted neural tube defect (NTD) risk factors across the component phenotypes of anencephaly and spina bifida. Data for these analyses were obtained from the National Birth Defects Prevention Study.
The use of a composite phenotype has the potential to mask associations specific to a component phenotype and in some cases the effect of a variable may be misattributed to the composite phenotype. For example, an association between infant sex and anencephaly (adjusted odds ratio [AOR], 1.5; 95% CI, 1.1-1.9) was masked when data from all NTDs were analyzed (AOR, 1.1; 95% CI, 0.9-1.3), whereas an association with maternal body mass index that was specific to spina bifida (AOR, 1.9; 95% CI, 1.6-2.4) was attributed to all NTDs (AOR, 1.6; 95% CI, 1.4-2.0). Furthermore, conclusions regarding effect heterogeneity based on ad hoc comparisons, rather than some formal assessment, may be vulnerable to considerable subjectivity, as was the case for the association of maternal Hispanic ethnicity with spina bifida (AOR, 1.4; 95% CI, 1.2-1.8) and anencephaly (AOR, 2.0; 95% CI, 1.5-2.8).
Polytomous logistic regression provides a useful tool for evaluating putative risk factors for which there is no a priori basis for assuming effect homogeneity across component phenotypes.
在出生缺陷流行病学中，为了提高统计效力，表型亚组常常被合并成一个复合表型。尽管使用复合表型的有效性受到质疑，但尚未对各组成表型间效应同质性这一潜在假设进行正式评估。
采用多分类逻辑回归来评估几种普遍认可的神经管缺陷（NTD）风险因素在无脑儿和脊柱裂这两种组成表型间的效应异质性。这些分析的数据来自国家出生缺陷预防研究。
使用复合表型有可能掩盖特定组成表型的关联，在某些情况下，变量的效应可能会被错误地归因于复合表型。例如，分析所有神经管缺陷数据时，婴儿性别与无脑儿之间的关联（调整优势比[AOR]，1.5；95%可信区间[CI]，1.1 - 1.9）被掩盖了（AOR，1.1；95% CI，0.9 - 1.3），而脊柱裂特有的与孕妇体重指数的关联（AOR，1.9；95% CI，1.6 - 2.4）却被归因于所有神经管缺陷（AOR，1.6；95% CI，1.4 - 2.0）。此外，基于临时比较而非某种正式评估得出的关于效应异质性的结论，可能会受到相当大的主观性影响，孕妇西班牙裔种族与脊柱裂（AOR，1.4；95% CI，1.2 - 1.8）及无脑儿（AOR，2.0；95% CI，1.5 - 2.8）的关联就是如此。
多分类逻辑回归为评估那些没有先验依据假定各组成表型间效应同质性的假定风险因素提供了一个有用的工具。
