Comparison of Parametric and Nonparametric Estimators for the Association Between Incident Prepregnancy Obesity and Stillbirth in a Population-Based Cohort Study

Abstract

While prepregnancy obesity increases risk of stillbirth, few studies have evaluated the role of newly developed obesity independent of long-standing obesity. Additionally, researchers have relied almost exclusively on parametric models, which require correct specification of an unknown function for consistent estimation. We estimated the association between incident obesity and stillbirth in a cohort constructed from linked birth and death records in Pennsylvania (2003–2013). Incident obesity was defined as body mass index (weight (kg)/height (m)2) greater than or equal to 30. We used parametric G-computation, semiparametric inverse-probability weighting, and parametric/nonparametric targeted minimum loss-based estimation (TMLE) to estimate the association between incident prepregnancy obesity and stillbirth. Compared with pregnancies from women who stayed nonobese, women who became obese prior to their next pregnancy were estimated to have 2.0 (95% confidence interval (CI): 0.5, 3.5) more stillbirths per 1,000 pregnancies using parametric G-computation. However, despite well-behaved stabilized inverse probability weights, risk differences estimated from inverse-probability weighting, nonparametric TMLE, and parametric TMLE represented 6.9 (95% CI: 3.7, 10.0), 0.4 (95% CI: 0.1, 0.7), and 2.9 (95% CI: 1.5, 4.2) excess stillbirths per 1,000 pregnancies, respectively. These results, particularly those derived from nonparametric TMLE, were highly sensitive to covariates included in the propensity score models. Our results suggest that caution is warranted when using nonparametric estimators to quantify exposure effects.

Invited Commentary: The Causal Association Between Obesity and Stillbirth—Strengths and Limitations of the Consecutive-Pregnancies Approach

“…Of course, the decision as to which results are most important and broadly applicable is subjective and depends on the specific stakeholder. Nonetheless, we argue that as epidemiologists we should focus on rigorously defining our question of interest (whether causal or noncausal) as Yu et al have done (1), and also making explicit how the exposure, outcome, and target population are defined within that question definition.…”

“…This becomes especially salient when excluding large proportions of the potential study population when motivated by a causal effect definition rather than an a priori interest in the population that is retained in the study. Yu et al report that they included 363,610 pregnancies occurring during 2003-2013 in their analysis, from an available 1,551,919-approximately 23% (1). Using data from all births taking place in California during 2007-2011, we followed the authors' process of restricting the study population to examine how those exclusions changed the study population (Table 2).…”

Data Driven Approach for Eye Disease Classification with Machine Learning

“…A number of classification algorithms are in use in the medical domain with the promise of increasing reliability and confidence [50,51]. One can easily select a classifier according to the data, whether parametric or non-parametric [52,53]. Tree based methods such as Decision Tree and Random Forest are non-parametric classifiers and, therefore, can be applied to the data whose distribution in not know.…”

Pre-pregnancy body mass index and adverse maternal and perinatal outcomes in twin pregnancies: a population based retrospective cohort study

“…In such cases, triangulation by other methods (e.g., regression model, as done in our study) is recommended and results need to be interpreted cautiously. 47 Second, we were unable to completely stratify the study population by chorionicity, which could have confounded or modified the effects of BMI on adverse perinatal outcomes. Third, height and weight information was obtained routinely during prepregnancy and early pregnancy physician visits, and this information was self-reported.…”