Hinkle Stefanie N, Mitchell Emily M, Grantz Katherine L, Ye Aijun, Schisterman Enrique F
Epidemiology Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD.
Glotech, Inc, Rockville, MD.
Paediatr Perinat Epidemiol. 2016 May;30(3):294-304. doi: 10.1111/ppe.12284. Epub 2016 Feb 24.
Studies examining total gestational weight gain (GWG) and outcomes associated with gestational age (GA) are potentially biased. The z-score has been proposed to mitigate this bias. We evaluated a regression-based adjustment for GA to remove the correlation between GWG and GA, and compared it to published weight-gain-for-gestational-age z-scores when applied to a study sample with different underlying population characteristics.
Using 65 643 singleton deliveries to normal weight women at 12 US clinical sites, we simulated a null association between GWG and neonatal mortality. Logistic regression was used to estimate approximate relative risks (RR) of neonatal mortality associated with GWG, unadjusted and adjusted for GA, and the z-score, overall and within study sites. Average RRs across 5000 replicates were calculated with 95% coverage probability to indicate model bias and precision, where 95% is nominal.
Under a simulated null association, total GWG resulted in a biased mortality estimate (RR = 0.87; coverage = 0%); estimates adjusted for GA were unbiased (RR = 1.00; coverage = 94%). Quintile-specific RRs ranged from 0.97-1.03. Similar results were observed for site-specific analyses. The overall z-score RR was 0.97 (84% coverage) with quintile-specific RRs ranging from 0.64-0.90. Estimates were close to 1.0 at most sites, with coverage from 70-94%. Sites 1 and 6 were biased with RRs of 0.66 and 1.43, respectively, and coverage of 70% and 80%.
Adjusting for GA achieves unbiased estimates of the association between total GWG and neonatal mortality, providing an accessible alternative to the weight-gain-for-gestational-age z-scores without requiring assumptions concerning underlying population characteristics.
研究总孕期体重增加(GWG)及与孕周(GA)相关的结局可能存在偏差。有人提出使用z分数来减轻这种偏差。我们评估了一种基于回归的孕周调整方法,以消除GWG与GA之间的相关性,并将其应用于具有不同潜在人群特征的研究样本时,与已发表的孕周体重增加z分数进行比较。
利用美国12个临床地点65643例体重正常的单胎分娩数据,我们模拟了GWG与新生儿死亡率之间的零关联。采用逻辑回归估计未调整和调整孕周以及z分数时,GWG与新生儿死亡率相关的近似相对风险(RR),包括总体及各研究地点的情况。计算5000次重复抽样的平均RR,并给出95%的覆盖概率,以表明模型偏差和精度,其中95%为名义值。
在模拟的零关联情况下,总GWG导致死亡率估计有偏差(RR = 0.87;覆盖概率 = 0%);调整孕周后的估计无偏差(RR = 1.00;覆盖概率 = 94%)。五分位数特异性RR范围为0.97 - 1.03。各地点特异性分析也观察到类似结果。总体z分数RR为0.97(覆盖概率84%),五分位数特异性RR范围为0.64 - 0.90。大多数地点的估计值接近1.0,覆盖概率为70% - 94%。地点1和地点6存在偏差,RR分别为0.66和1.43,覆盖概率分别为70%和80%。
调整孕周可实现总GWG与新生儿死亡率之间关联的无偏估计,为孕周体重增加z分数提供了一种无需对潜在人群特征做假设的可行替代方法。