Emily M. Mitchell

Is anti-müllerian hormone associated with fecundability? Findings from the EAGeR trial

OBJECTIVE The objective of the study was to evaluate whether anti-Müllerian hormone (AMH) is associated with fecundability among women with proven fecundity and a history of pregnancy loss. DESIGN This was a prospective cohort study within a multicenter, block-randomized, double-blind, placebo-controlled clinical trial ( clinicaltrials.gov , number NCT00467363). SETTING The study was conducted at four US medical centers (2006-2012). PARTICIPANTS Participating women were aged 18-40 years, with a history of one to two pregnancy losses who were actively attempting pregnancy. MAIN OUTCOME MEASURES Time to human chorionic gonadotropin detected and clinical pregnancy were assessed using Cox proportional hazard regression models to estimate fecundability odds ratios (fecundability odds ratios with 95% confidence interval [CI]) adjusted for age, race, body mass index, income, low-dose aspirin treatment, parity, number of previous losses, and time since most recent loss. Analyses examined by preconception AMH levels: low (<1.00 ng/mL, n = 124); normal (referent 1.00-3.5 ng/mL, n = 595); and high (>3.5 ng/mL, n = 483). RESULTS Of the 1202 women with baseline AMH levels, 82 women with low AMH (66.1%) achieved an human chorionic gonadotropin detected pregnancy, compared with 383 with normal AMH (65.2%) and 315 with high AMH level (65.2%). Low or high AMH levels relative to normal AMH (referent) were not associated with fecundability (low AMH: fecundability odds ratios 1.13, 95% CI 0.85-1.49; high AMH: FOR 1.04, 95% CI 0.87-1.24). CONCLUSIONS Lower and higher AMH values were not associated with fecundability in unassisted conceptions in a cohort of fecund women with a history of one or two prior losses. Our data do not support routine AMH testing for preconception counseling in young, fecund women.

Trying to Conceive After an Early Pregnancy Loss: An Assessment on How Long Couples Should Wait.

OBJECTIVE: To compare time to pregnancy and live birth among couples with varying intervals of pregnancy loss date to subsequent trying to conceive date. METHODS: In this secondary analysis of the Effects of Aspirin in Gestation and Reproduction trial, 1,083 women aged 18–40 years with one to two prior early losses and whose last pregnancy outcome was a nonectopic or nonmolar loss were included. Participants were actively followed for up to six menstrual cycles and, for women achieving pregnancy, until pregnancy outcome. We calculated intervals as start of trying to conceive date minus pregnancy loss date. Time to pregnancy was defined as start of trying to conceive until subsequent conception. Discrete Cox models, accounting for left truncation and right censoring, estimated fecundability odds ratios (ORs) adjusting for age, race, body mass index, education, and subfertility. Although intervals were assessed prior to randomization and thus reasoned to have no relation with treatment assignment, additional adjustment for treatment was evaluated given that low-dose aspirin was previously shown to be predictive of time to pregnancy. RESULTS: Couples with a 0–3-month interval (n=765 [76.7%]) compared with a greater than 3-month (n=233 [23.4%]) interval were more likely to achieve live birth (53.2% compared with 36.1%) with a significantly shorter time to pregnancy leading to live birth (median [interquartile range] five cycles [three, eight], adjusted fecundability OR 1.71 [95% confidence interval 1.30–2.25]). Additionally adjusting for low-dose aspirin treatment did not appreciably alter estimates. CONCLUSION: Our study supports the hypothesis that there is no physiologic evidence for delaying pregnancy attempt after an early loss.

It's about time: A survival approach to gestational weight gain and preterm delivery

There is substantial interest in understanding the impact of gestational weight gain on preterm delivery (delivery <37 weeks). The major difficulty in analyzing the association between gestational weight gain and preterm delivery lies in their mutual dependence on gestational age, as weight naturally increases with increasing pregnancy duration. In this study, we untangle this inherent association by reframing preterm delivery as time to delivery and assessing the relationship through a survival framework, which is particularly amenable to dealing with time-dependent covariates, such as gestational weight gain. We derive the appropriate analytical model for assessing the relationship between weight gain and time to delivery when weight measurements at multiple time points are available. Since epidemiologic data may be limited to weight gain measurements taken at only a few time points or at delivery only, we conduct simulation studies to illustrate how several strategically timed measurements can yield unbiased risk estimates. Analysis of the study of successive small-for-gestational-age births demonstrates that a naive analysis that does not account for the confounding effect of time on gestational weight gain suggests a strong association between higher weight gain and later delivery (hazard ratio: 0.89, 95% confidence interval = 0.84, 0.93). Properly accounting for the confounding effect of time using a survival model, however, mitigates this bias (hazard ratio: 0.98, 95% confidence interval = 0.97, 1.00). These results emphasize the importance of considering the effect of gestational age on time-varying covariates during pregnancy, and the proposed methods offer a convenient mechanism to appropriately analyze such data.See Video Abstract at http://links.lww.com/EDE/B13.

Preconception low-dose aspirin restores diminished pregnancy and live birth rates in women with low-grade inflammation: A secondary analysis of a randomized trial

Context: Inflammation is linked to causes of infertility. Low-dose aspirin (LDA) may improve reproductive success in women with chronic, low-grade inflammation. Objective: To investigate the effect of preconception-initiated LDA on pregnancy rate, pregnancy loss, live birth rate, and inflammation during pregnancy. Design: Stratified secondary analysis of a multicenter, block-randomized, double-blind, placebo-controlled trial. Setting: Four US academic medical centers, 2007 to 2012. Participants: Healthy women aged 18 to 40 years (N = 1228) with one to two prior pregnancy losses actively attempting to conceive. Intervention: Preconception-initiated, daily LDA (81 mg) or matching placebo taken up to six menstrual cycles attempting pregnancy and through 36 weeks’ gestation in women who conceived. Main Outcome Measures: Confirmed pregnancy, live birth, and pregnancy loss were compared between LDA and placebo, stratified by tertile of preconception, preintervention serum high-sensitivity C-reactive protein (hsCRP) (low, <0.70 mg/L; middle, 0.70 to <1.95 mg/L; high, ≥1.95 mg/L). Results: Live birth occurred in 55% of women overall. The lowest pregnancy and live birth rates occurred among the highest hsCRP tertile receiving placebo (44% live birth). LDA increased live birth among high-hsCRP women to 59% (relative risk, 1.35; 95% confidence interval, 1.08 to 1.67), similar to rates in the lower and mid-CRP tertiles. LDA did not affect clinical pregnancy or live birth in the low (live birth: 59% LDA, 54% placebo) or midlevel hsCRP tertiles (live birth: 59% LDA, 59% placebo). Conclusions: In women attempting conception with elevated hsCRP and prior pregnancy loss, LDA may increase clinical pregnancy and live birth rates compared with women without inflammation and reduce hsCRP elevation during pregnancy.

Association of Nausea and Vomiting During Pregnancy With Pregnancy Loss: A Secondary Analysis of a Randomized Clinical Trial

Importance Nausea and vomiting during pregnancy have been associated with a reduced risk for pregnancy loss. However, most prior studies enrolled women with clinically recognized pregnancies, thereby missing early losses. Objective To examine the association of nausea and vomiting during pregnancy with pregnancy loss. Design, Setting, and Participants A randomized clinical trial, Effects of Aspirin in Gestation and Reproduction, enrolled women with 1 or 2 prior pregnancy losses at 4 US clinical centers from June 15, 2007, to July 15, 2011. This secondary analysis was limited to women with a pregnancy confirmed by positive results of a human chorionic gonadotropin (hCG) test. Nausea symptoms were ascertained from daily preconception and pregnancy diaries for gestational weeks 2 to 8. From weeks 12 to 36, participants completed monthly questionnaires summarizing symptoms for the preceding 4 weeks. A week-level variable included nausea only, nausea with vomiting, or neither. Main Outcomes and Measures Peri-implantation (hCG-detected pregnancy without ultrasonographic evidence) and clinically recognized pregnancy losses. Results A total of 797 women (mean [SD] age, 28.7 [4.6] years) had an hCG-confirmed pregnancy. Of these, 188 pregnancies (23.6%) ended in loss. At gestational week 2, 73 of 409 women (17.8%) reported nausea without vomiting and 11 of 409 women (2.7%), nausea with vomiting. By week 8, the proportions increased to 254 of 443 women (57.3%) and 118 of 443 women (26.6%), respectively. Hazard ratios (HRs) for nausea (0.50; 95% CI, 0.32-0.80) and nausea with vomiting (0.25; 95% CI, 0.12-0.51) were inversely associated with pregnancy loss. The associations of nausea (HR, 0.59; 95% CI, 0.29-1.20) and nausea with vomiting (HR, 0.51; 95% CI, 0.11-2.25) were similar for peri-implantation losses but were not statistically significant. Nausea (HR, 0.44; 95% CI, 0.26-0.74) and nausea with vomiting (HR, 0.20; 95% CI, 0.09-0.44) were associated with a reduced risk for clinical pregnancy loss. Conclusions and Relevance Among women with 1 or 2 prior pregnancy losses, nausea and vomiting were common very early in pregnancy and were associated with a reduced risk for pregnancy loss. These findings overcome prior analytic and design limitations and represent the most definitive data available to date indicating the protective association of nausea and vomiting in early pregnancy and the risk for pregnancy loss. Trial Registration clinicaltrials.gov Identifier: NCT00467363.

Multiple Imputation for Incomplete Data in Epidemiologic Studies

Epidemiologic studies are frequently susceptible to missing information. Omitting observations with missing variables remains a common strategy in epidemiologic studies, yet this simple approach can often severely bias parameter estimates of interest if the values are not missing completely at random. Even when missingness is completely random, complete-case analysis can reduce the efficiency of estimated parameters, because large amounts of available data are simply tossed out with the incomplete observations. Alternative methods for mitigating the influence of missing information, such as multiple imputation, are becoming an increasing popular strategy in order to retain all available information, reduce potential bias, and improve efficiency in parameter estimation. In this paper, we describe the theoretical underpinnings of multiple imputation, and we illustrate application of this method as part of a collaborative challenge to assess the performance of various techniques for dealing with missing data (Am J Epidemiol. 2018;187(3):568-575). We detail the steps necessary to perform multiple imputation on a subset of data from the Collaborative Perinatal Project (1959-1974), where the goal is to estimate the odds of spontaneous abortion associated with smoking during pregnancy.

Inverse-Probability-Weighted Estimation for Monotone and Nonmonotone Missing Data

Missing data is a common occurrence in epidemiologic research. In this paper, 3 data sets with induced missing values from the Collaborative Perinatal Project, a multisite US study conducted from 1959 to 1974, are provided as examples of prototypical epidemiologic studies with missing data. Our goal was to estimate the association of maternal smoking behavior with spontaneous abortion while adjusting for numerous confounders. At the same time, we did not necessarily wish to evaluate the joint distribution among potentially unobserved covariates, which is seldom the subject of substantive scientific interest. The inverse probability weighting (IPW) approach preserves the semiparametric structure of the underlying model of substantive interest and clearly separates the model of substantive interest from the model used to account for the missing data. However, IPW often will not result in valid inference if the missing-data pattern is nonmonotone, even if the data are missing at random. We describe a recently proposed approach to modeling nonmonotone missing-data mechanisms under missingness at random to use in constructing the weights in IPW complete-case estimation, and we illustrate the approach using 3 data sets described in a companion article (Am J Epidemiol. 2018;187(3):568-575).

Maternal Weight Gain During Pregnancy: Comparing Methods to Address Bias Due to Length of Gestation in Epidemiological Studies.

BACKGROUND 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. METHODS 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. RESULTS 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%. CONCLUSIONS 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.

Positing, fitting, and selecting regression models for pooled biomarker data

Pooling biospecimens prior to performing lab assays can help reduce lab costs, preserve specimens, and reduce information loss when subject to a limit of detection. Because many biomarkers measured in epidemiological studies are positive and right-skewed, proper analysis of pooled specimens requires special methods. In this paper, we develop and compare parametric regression models for skewed outcome data subject to pooling, including a novel parameterization of the gamma distribution that takes full advantage of the gamma summation property. We also develop a Monte Carlo approximation of Akaikes Information Criterion applied to pooled data in order to guide model selection. Simulation studies and analysis of motivating data from the Collaborative Perinatal Project suggest that using Akaikes Information Criterion to select the best parametric model can help ensure valid inference and promote estimate precision.

Serum caffeine and paraxanthine concentrations and menstrual cycle function: correlations with beverage intakes and associations with race, reproductive hormones, and anovulation in the BioCycle Study

BACKGROUND Clinicians often recommend limiting caffeine intake while attempting to conceive; however, few studies have evaluated the associations between caffeine exposure and menstrual cycle function, and we are aware of no previous studies assessing biological dose via well-timed serum measurements. OBJECTIVES We assessed the relation between caffeine and its metabolites and reproductive hormones in a healthy premenopausal cohort and evaluated potential effect modification by race. DESIGN Participants (n = 259) were followed for ≤2 menstrual cycles and provided fasting blood specimens ≤8 times/cycle. Linear mixed models were used to estimate associations between serum caffeine biomarkers and geometric mean reproductive hormones, whereas Poisson regression was used to assess risk of sporadic anovulation. RESULTS The highest compared with the lowest serum caffeine tertile was associated with lower total testosterone [27.9 ng/dL (95% CI: 26.7, 29.0 ng/dL) compared with 29.1 ng/dL (95% CI: 27.9, 30.3 ng/dL), respectively] and free testosterone [0.178 ng/mL (95% CI: 0.171, 0.185 ng/dL) compared with 0.186 ng/mL (95% CI: 0.179, 0.194 ng/dL), respectively] after adjustment for age, race, percentage of body fat, daily vigorous exercise, perceived stress, depression, dietary factors, and alcohol intake. The highest tertiles compared with the lowest tertiles of caffeine and paraxanthine were also associated with reduced risk of anovulation [adjusted RRs (aRRs): 0.39 (95% CI: 0.18, 0.87) and 0.40 (95% CI: 0.18, 0.87), respectively]. Additional adjustment for self-reported coffee intake did not alter the reproductive hormone findings and only slightly attenuated the results for serum caffeine and paraxanthine and anovulation. Although reductions in the concentrations of total testosterone and free testosterone and decreased risk of anovulation were greatest in Asian women, there was no indication of effect modification by race. CONCLUSION Caffeine intake, irrespective of the beverage source, may be associated with reduced testosterone and improved menstrual cycle function in healthy premenopausal women.