Objectives Researchers are concerned whether multiple imputation (MI) or complete case analysis should be used when a large proportion of data are missing. We aimed to provide guidance for drawing conclusions from data with a large proportion of missingness. Study Design and Setting Via simulations, we investigated how the proportion of missing data, the fraction of missing information (FMI), and availability of auxiliary variables affected MI performance. Outcome data were missing completely at random or missing at random (MAR). Results Provided sufficient auxiliary information was available; MI was beneficial in terms of bias and never detrimental in terms of efficiency. Models with similar FMI values, but differing proportions of missing data, also had similar precision for effect estimates. In the absence of bias, the FMI was a better guide to the efficiency gains using MI than the proportion of missing data. Conclusion We provide evidence that for MAR data, valid MI reduces bias even when the proportion of missingness is large. We advise researchers to use FMI to guide choice of auxiliary variables for efficiency gain in imputation analyses, and that sensitivity analyses including different imputation models may be needed if the number of complete cases is small.
Objective: The negative control design can be used to provide evidence for whether a prenatal exposureeoutcome association occurs by in utero mechanisms. Assortative mating has been suggested to influence results from negative control designs, although how and why has not yet been adequately explained. We aimed to explain why mutual adjustment of maternal and paternal exposure in regression models can account for assortative mating. Study Design and Setting: We used directed acyclic graphs to show how bias can occur when modeling maternal and paternal effects separately. We empirically tested our claims using a simulation study. We investigated how increasing assortative mating influences the bias of effect estimates obtained from models that do and do not use a mutual adjustment strategy. Results: In models without mutual adjustment, increasing assortative mating led to increased bias in effect estimates. The maternal and paternal effect estimates were biased by each other, making the difference between them smaller than the true difference. Mutually adjusted models did not suffer from such bias. Conclusions: Mutual adjustment for maternal and paternal exposure prevents bias from assortative mating influencing the conclusions of a negative control design. We further discuss issues that mutual adjustment may not be able to resolve.
Evidence supports no link between maternal smoking in pregnancy and autism spectrum disorder (autism) overall. To address remaining questions about the unexplained heterogeneity between study results and the possibility of risk for specific autism sub-phenotypes, we conducted a wholepopulation cohort study in Denmark. We followed births 1991 -2011 (1,294,906 persons, including 993,301 siblings in 728,271 families), from 1 year of age until an autism diagnosis (13,547), death, emigration, or December 31, 2012. Autism, with and without attention deficit hyperactivity disorder (ADHD) and with and without intellectual disability (ID) were based on ICD-8 and ICD-10 codes from Danish national health registers, including 3,319 autism+ADHD, 10,228 autism-no ADHD, 2,205 autism+ID, and 11,342 autism-no ID. We estimated hazard ratios (HR) and 95% confidence intervals (95% CI) between any maternal smoking (from birth records) and autism (or sub-phenotypes) using survival models with robust standard errors, stratifying by birth year and adjusting for child sex, parity, and parental age, education, income, and psychiatric history. To additionally address confounding using family designs, we constructed a maternal
Background The association between maternal smoking in pregnancy and offspring intellectual disability (ID) is less well understood than that of smoking and fetal growth restriction. As fetal growth and cognitive development may share similar confounding structures, comparison of the two associations may improve understanding of the causal nature of the association with ID. Furthermore, comparisons of smoking with smokeless tobacco use may aid identification of mechanisms of action. Methods This was a cohort study of all Swedish births between 1999 and 2012 (n = 1 070 013), with prospectively recorded data. We assessed the association between maternal smoking during pregnancy and offspring outcomes ID and born small for gestational age (SGA). Analyses were repeated for snus use in pregnancy. Using a sibling design, we estimated within-family effects that control for shared sibling characteristics. Results Those exposed to maternal smoking in pregnancy had increased odds of ID [odds ratio (OR) = 1.24, 95% confidence interval (CI): 1.16-1.33] and SGA (OR = 2.19, 95% CI: 2.11-2.27) after confounder adjustment. Within-family effects were found for SGA (OR = 1.44, 95% CI: 1.27-1.63) but not ID (OR = 0.92, 95% CI: 0.74-1.14). For snus use, the results for ID were similar to smoking. We found increased odds of offspring SGA among mothers who used snus in pregnancy in sensitivity analyses but not in primary analyses. Conclusions Our findings are consistent with a causal effect of maternal smoking in pregnancy on risk of offspring born SGA but not on risk of ID. We found no evidence for a causal effect of snus use in pregnancy on ID and inconclusive evidence for SGA.
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