Adrien Nedghie, MacLehose Richard F, Werler Martha M, Yazdy Mahsa M, Fox Matthew P, Parker Samantha E
Department of Epidemiology, Boston University School of Public Health, Boston, Massachusetts, USA.
Division for Surveillance, Research, and Promotion of Perinatal Health, Massachusetts Department of Public Health, Boston, Massachusetts, USA.
Paediatr Perinat Epidemiol. 2024 Dec 16. doi: 10.1111/ppe.13161.
Empirically evaluating the potential impact of recall bias on observed associations of prenatal medication exposure is crucial.
We sought to assess the effects of exposure misclassification on previous studies of the use of nonsteroidal anti-inflammatory drugs (NSAIDs) in early pregnancy and increased risk of amniotic band syndrome (ABS).
Using data from the National Birth Defects Prevention Study (NBDPS) on births from 1997 to 2011, we included 189 mothers of infants with ABS and 11,829 mothers of infants without congenital anomalies. We identified external studies of medication use during pregnancy to obtain validity parameters for a probabilistic bias analysis to adjust for exposure misclassification. Due to uncertainty about the transportability of these parameters, we conducted multidimensional bias analyses to explore combinations of values on the results.
When we assumed higher specificity in cases or higher sensitivity in controls, misclassification-adjusted estimates suggested confounding-adjusted estimates were attenuated. However, in a few instances, when we assumed greater sensitivity in the cases than the controls (and Sp ≥ 0.9), the misclassification-adjusted estimates suggested upward bias in the confounding-adjusted estimates.
Results from our bias analysis highlighted that the magnitude of bias depended on the mechanism and the extent of misclassification. However, the parameters available from the validation studies were not directly applicable to our study. In the absence of reliable validation studies, considering mechanisms of bias and simulation studies to outline combinations of plausible scenarios to better inform conclusions on the effects of these medications on pregnancy outcomes remains important.
实证评估回忆偏倚对观察到的产前药物暴露关联的潜在影响至关重要。
我们试图评估暴露错误分类对先前关于孕早期使用非甾体抗炎药(NSAIDs)与羊膜带综合征(ABS)风险增加研究的影响。
利用1997年至2011年全国出生缺陷预防研究(NBDPS)中的出生数据,我们纳入了189名患有ABS婴儿的母亲和11829名无先天性异常婴儿的母亲。我们确定了孕期药物使用的外部研究,以获得概率偏倚分析的有效性参数,以调整暴露错误分类。由于这些参数的可转移性存在不确定性,我们进行了多维度偏倚分析,以探索结果中值的组合。
当我们假设病例组具有更高的特异性或对照组具有更高的敏感性时,经错误分类调整的估计值表明经混杂因素调整的估计值被削弱。然而,在少数情况下,当我们假设病例组比对照组具有更高的敏感性(且Sp≥0.9)时,经错误分类调整的估计值表明经混杂因素调整的估计值存在向上偏倚。
我们的偏倚分析结果强调,偏倚的程度取决于机制和错误分类的程度。然而,验证研究中可用的参数并不直接适用于我们的研究。在缺乏可靠的验证研究的情况下,考虑偏倚机制和模拟研究以勾勒合理情景的组合,从而更好地为这些药物对妊娠结局影响的结论提供信息仍然很重要。