Leslie Hannah H, Karasek Deborah A, Harris Laura F, Chang Emily, Abdulrahim Naila, Maloba May, Huchko Megan J
Division of Epidemiology, University of California, Berkeley, California, United States of America.
Joint Medical Program, University of California, Berkeley, and University of California San Francisco, San Francisco, California, United States of America.
PLoS One. 2014 Jun 30;9(6):e101090. doi: 10.1371/journal.pone.0101090. eCollection 2014.
To demonstrate the application of causal inference methods to observational data in the obstetrics and gynecology field, particularly causal modeling and semi-parametric estimation.
Human immunodeficiency virus (HIV)-positive women are at increased risk for cervical cancer and its treatable precursors. Determining whether potential risk factors such as hormonal contraception are true causes is critical for informing public health strategies as longevity increases among HIV-positive women in developing countries.
We developed a causal model of the factors related to combined oral contraceptive (COC) use and cervical intraepithelial neoplasia 2 or greater (CIN2+) and modified the model to fit the observed data, drawn from women in a cervical cancer screening program at HIV clinics in Kenya. Assumptions required for substantiation of a causal relationship were assessed. We estimated the population-level association using semi-parametric methods: g-computation, inverse probability of treatment weighting, and targeted maximum likelihood estimation.
We identified 2 plausible causal paths from COC use to CIN2+: via HPV infection and via increased disease progression. Study data enabled estimation of the latter only with strong assumptions of no unmeasured confounding. Of 2,519 women under 50 screened per protocol, 219 (8.7%) were diagnosed with CIN2+. Marginal modeling suggested a 2.9% (95% confidence interval 0.1%, 6.9%) increase in prevalence of CIN2+ if all women under 50 were exposed to COC; the significance of this association was sensitive to method of estimation and exposure misclassification.
Use of causal modeling enabled clear representation of the causal relationship of interest and the assumptions required to estimate that relationship from the observed data. Semi-parametric estimation methods provided flexibility and reduced reliance on correct model form. Although selected results suggest an increased prevalence of CIN2+ associated with COC, evidence is insufficient to conclude causality. Priority areas for future studies to better satisfy causal criteria are identified.
展示因果推断方法在妇产科领域观察性数据中的应用，尤其是因果建模和半参数估计。
感染人类免疫缺陷病毒（HIV）的女性患宫颈癌及其可治疗前驱病变的风险增加。随着发展中国家HIV阳性女性寿命延长，确定诸如激素避孕等潜在风险因素是否为真正病因对于制定公共卫生策略至关重要。
我们建立了一个与复方口服避孕药（COC）使用和宫颈上皮内瘤变2级或更高级别（CIN2+）相关因素的因果模型，并对该模型进行修改以拟合从肯尼亚HIV诊所宫颈癌筛查项目中的女性获取的观察数据。评估了证实因果关系所需的假设。我们使用半参数方法估计人群水平的关联：g计算、治疗权重逆概率和靶向最大似然估计。
我们确定了从COC使用到CIN2+的2条合理因果路径：通过人乳头瘤病毒（HPV）感染和通过疾病进展增加。研究数据仅在无未测量混杂因素的强假设下才能估计后者。按照方案筛查的2519名50岁以下女性中，219名（8.7%）被诊断为CIN2+。边际模型表明，如果所有50岁以下女性都接触COC，CIN2+患病率将增加2.9%（95%置信区间0.1%，6.9%）；这种关联的显著性对估计方法和暴露错误分类敏感。
因果建模的使用能够清晰呈现感兴趣的因果关系以及从观察数据估计该关系所需的假设。半参数估计方法提供了灵活性并减少了对正确模型形式的依赖。尽管选定结果表明与COC相关的CIN2+患病率增加，但证据不足以得出因果关系的结论。确定了未来研究为更好满足因果标准的优先领域。
