Gamma models for estimating the odds ratio for a skewed biomarker measured in pools and subject to errors.

Measuring a biomarker in pooled samples from multiple cases or controls can lead to cost-effective estimation of a covariate-adjusted odds ratio, particularly for expensive assays. But pooled measurements may be affected by assay-related measurement error (ME) and/or pooling-related processing error (PE), which can induce bias if ignored. Building on recently developed methods for a normal biomarker subject to additive errors, we present two related estimators for a right-skewed biomarker subject to multiplicative errors: one based on logistic regression and the other based on a Gamma discriminant function model. Applied to a reproductive health dataset with a right-skewed cytokine measured in pools of size 1 and 2, both methods suggest no association with spontaneous abortion. The fitted models indicate little ME but fairly severe PE, the latter of which is much too large to ignore. Simulations mimicking these data with a non-unity odds ratio confirm validity of the estimators and illustrate how PE can detract from pooling-related gains in statistical efficiency. These methods address a key issue associated with the homogeneous pools study design and should facilitate valid odds ratio estimation at a lower cost in a wide range of scenarios.

Combining Biomarker Calibration Data to Reduce Measurement Error.

Biomarker assay measurement often consists of a two-stage process where laboratory equipment yields a relative measure which is subsequently transformed to the unit of interest using a calibration curve. The calibration curve establishes the relation between the measured relative units and sample biomarker concentrations using stepped samples of known biomarker concentrations. Samples from epidemiologic studies are often measured in multiple batches or plates, each with independent calibration experiments. Collapsing calibration information across batches before statistical analysis has been shown to reduce measurement error and improves estimation. Additionally, collapsing in practice can also create an additional layer of quality control (QC) and optimization in a part of the laboratory measurement process that is often highly automated. Principled recalibration is demonstrated via. a three-step process of identifying batches where recalibration might be beneficial, forming a collapsed calibration curve and recalibrating identified batches, and using QC data to assess the appropriateness of recalibration. Here, we use inhibin B measured in biospecimens from the BioCycle study using 50 enzyme-linked immunosorbent assay (ELISA) batches (3875 samples) to motivate and display the benefits of collapsing calibration experiments, such as detecting and overcoming faulty calibration experiments, and thus improving assay coefficients of variation from reducing unwanted measurement error variability. Differences in the analysis of inhibin B by testosterone quartile are also demonstrated before and after recalibration. These simple and practical procedures are minor adjustments implemented by study personnel without altering laboratory protocols which could have positive estimation and cost-saving implications especially for population-based studies.

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).

Principled Approaches to Missing Data in Epidemiologic Studies.

Principled methods with which to appropriately analyze missing data have long existed; however, broad implementation of these methods remains challenging. In this and 2 companion papers (Am J Epidemiol. 2018;187(3):576-584 and Am J Epidemiol. 2018;187(3):585-591), we discuss issues pertaining to missing data in the epidemiologic literature. We provide details regarding missing-data mechanisms and nomenclature and encourage the conduct of principled analyses through a detailed comparison of multiple imputation and inverse probability weighting. Data from the Collaborative Perinatal Project, a multisite US study conducted from 1959 to 1974, are used to create a masked data-analytical challenge with missing data induced by known mechanisms. We illustrate the deleterious effects of missing data with naive methods and show how principled methods can sometimes mitigate such effects. For example, when data were missing at random, naive methods showed a spurious protective effect of smoking on the risk of spontaneous abortion (odds ratio (OR) = 0.43, 95% confidence interval (CI): 0.19, 0.93), while implementation of principled methods multiple imputation (OR = 1.30, 95% CI: 0.95, 1.77) or augmented inverse probability weighting (OR = 1.40, 95% CI: 1.00, 1.97) provided estimates closer to the "true" full-data effect (OR = 1.31, 95% CI: 1.05, 1.64). We call for greater acknowledgement of and attention to missing data and for the broad use of principled missing-data methods in epidemiologic research.

Logistic regression with a continuous exposure measured in pools and subject to errors.

In a multivariable logistic regression setting where measuring a continuous exposure requires an expensive assay, a design in which the biomarker is measured in pooled samples from multiple subjects can be very cost effective. A logistic regression model for poolwise data is available, but validity requires that the assay yields the precise mean exposure for members of each pool. To account for errors, we assume the assay returns the true mean exposure plus a measurement error (ME) and/or a processing error (PE). We pursue likelihood-based inference for a binary health-related outcome modeled by logistic regression coupled with a normal linear model relating individual-level exposure to covariates and assuming that the ME and PE components are independent and normally distributed regardless of pool size. We compare this approach with a discriminant function-based alternative, and we demonstrate the potential value of incorporating replicates into the study design. Applied to a reproductive health dataset with pools of size 2 along with individual samples and replicates, the model fit with both ME and PE had a lower AIC than a model accounting for ME only. Relative to ignoring errors, this model suggested a somewhat higher (though still nonsignificant) adjusted log-odds ratio associating the cytokine MCP-1 with risk of spontaneous abortion. Simulations modeled after these data confirm validity of the methods, demonstrate how ME and particularly PE can reduce the efficiency advantage of a pooling design, and highlight the value of replicates in improving stability when both errors are present.

Collinearity and Causal Diagrams: A Lesson on the Importance of Model Specification.

BACKGROUND: Correlated data are ubiquitous in epidemiologic research, particularly in nutritional and environmental epidemiology where mixtures of factors are often studied. Our objectives are to demonstrate how highly correlated data arise in epidemiologic research and provide guidance, using a directed acyclic graph approach, on how to proceed analytically when faced with highly correlated data. METHODS: We identified three fundamental structural scenarios in which high correlation between a given variable and the exposure can arise: intermediates, confounders, and colliders. For each of these scenarios, we evaluated the consequences of increasing correlation between the given variable and the exposure on the bias and variance for the total effect of the exposure on the outcome using unadjusted and adjusted models. We derived closed-form solutions for continuous outcomes using linear regression and empirically present our findings for binary outcomes using logistic regression. RESULTS: For models properly specified, total effect estimates remained unbiased even when there was almost perfect correlation between the exposure and a given intermediate, confounder, or collider. In general, as the correlation increased, the variance of the parameter estimate for the exposure in the adjusted models increased, while in the unadjusted models, the variance increased to a lesser extent or decreased. CONCLUSION: Our findings highlight the importance of considering the causal framework under study when specifying regression models. Strategies that do not take into consideration the causal structure may lead to biased effect estimation for the original question of interest, even under high correlation.

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.

An efficient design strategy for logistic regression using outcome- and covariate-dependent pooling of biospecimens prior to assay.

Potential reductions in laboratory assay costs afforded by pooling equal aliquots of biospecimens have long been recognized in disease surveillance and epidemiological research and, more recently, have motivated design and analytic developments in regression settings. For example, Weinberg and Umbach (1999, Biometrics 55, 718-726) provided methods for fitting set-based logistic regression models to case-control data when a continuous exposure variable (e.g., a biomarker) is assayed on pooled specimens. We focus on improving estimation efficiency by utilizing available subject-specific information at the pool allocation stage. We find that a strategy that we call "(y,c)-pooling," which forms pooling sets of individuals within strata defined jointly by the outcome and other covariates, provides more precise estimation of the risk parameters associated with those covariates than does pooling within strata defined only by the outcome. We review the approach to set-based analysis through offsets developed by Weinberg and Umbach in a recent correction to their original paper. We propose a method for variance estimation under this design and use simulations and a real-data example to illustrate the precision benefits of (y,c)-pooling relative to y-pooling. We also note and illustrate that set-based models permit estimation of covariate interactions with exposure.

Estimating relative risk of a log-transformed exposure measured in pools.

Pooling biospecimens prior to performing laboratory assays is a useful tool to reduce costs, achieve minimum volume requirements and mitigate assay measurement error. When estimating the risk of a continuous, pooled exposure on a binary outcome, specialized statistical techniques are required. Current methods include a regression calibration approach, where the expectation of the individual-level exposure is calculated by adjusting the observed pooled measurement with additional covariate data. While this method employs a linear regression calibration model, we propose an alternative model that can accommodate log-linear relationships between the exposure and predictive covariates. The proposed model permits direct estimation of the relative risk associated with a log-transformation of an exposure measured in pools. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.

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.

Changes in macronutrient, micronutrient, and food group intakes throughout the menstrual cycle in healthy, premenopausal women.

PURPOSE: It is thought that total energy intake in women is increased during the luteal versus follicular phase of the menstrual cycle; however, less is understood regarding changes in diet composition (i.e., macro- and micronutrient intakes) across the cycle. The aim of this study was to investigate changes in macronutrient, micronutrient, and food group intakes across phases of the menstrual cycle among healthy women, and to assess whether these patterns differ by ovulatory status. METHODS: The BioCycle study (2005-2007) was a prospective cohort study of 259 healthy regularly menstruating women age 18-44 who were followed for up to two menstrual cycles. Dietary intake was measured using 24-h dietary recalls, and food cravings were assessed via questionnaire, up to four times per cycle, corresponding to menses, mid-follicular, expected ovulation, and luteal phases. Linear mixed models adjusting for total energy intake were used to evaluate changes across the cycle. RESULTS: Total protein (P = 0.03), animal protein (P = 0.05), and percent of caloric intake from protein (P = 0.02) were highest during the mid-luteal phase compared to the peri-ovulatory phase. There were also significant increases in appetite, craving for chocolate, craving for sweets in general, craving for salty flavor, and total craving score during the late luteal phase compared to the menstrual, follicular, and ovulatory phases (P < 0.001). CONCLUSIONS: Our findings suggest an increased intake of protein, and specifically animal protein, as well as an increase in reported food cravings, during the luteal phase of the menstrual cycle independent of ovulatory status. These results highlight a plausible link between macronutrient intake and menstrual cycle phase.

Case-control data analysis for randomly pooled biomarkers.

Pooled study designs, where individual biospecimens are combined prior to measurement via a laboratory assay, can reduce lab costs while maintaining statistical efficiency. Analysis of the resulting pooled measurements, however, often requires specialized techniques. Existing methods can effectively estimate the relation between a binary outcome and a continuous pooled exposure when pools are matched on disease status. When pools are of mixed disease status, however, the existing methods may not be applicable. By exploiting characteristics of the gamma distribution, we propose a flexible method for estimating odds ratios from pooled measurements of mixed and matched status. We use simulation studies to compare consistency and efficiency of risk effect estimates from our proposed methods to existing methods. We then demonstrate the efficacy of our method applied to an analysis of pregnancy outcomes and pooled cytokine concentrations. Our proposed approach contributes to the toolkit of available methods for analyzing odds ratios of a pooled exposure, without restricting pools to be matched on a specific outcome.

Semiparametric regression models for a right-skewed outcome subject to pooling.

Pooling specimens prior to performing laboratory assays has various benefits. Pooling can help to reduce cost, preserve irreplaceable specimens, meet minimal volume requirements for certain lab tests, and even reduce information loss when a limit of detection is present. Regardless of the motivation for pooling, appropriate analytical techniques must be applied in order to obtain valid inference from composite specimens. When biomarkers are treated as the outcome in a regression model, techniques applicable to individually measured specimens may not be valid when measurements are taken from pooled specimens, particularly when the biomarker is positive and right skewed. In this paper, we propose a novel semiparametric estimation method based on an adaptation of the quasi-likelihood approach that can be applied to a right-skewed outcome subject to pooling. We use simulation studies to compare this method with an existing estimation technique that provides valid estimates only when pools are formed from specimens with identical predictor values. Simulation results and analysis of a motivating example demonstrate that, when appropriate estimation techniques are applied to strategically formed pools, valid and efficient estimation of the regression coefficients can be achieved.

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 Akaike's 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 Akaike's Information Criterion to select the best parametric model can help ensure valid inference and promote estimate precision.

Regression for skewed biomarker outcomes subject to pooling.

Epidemiological studies involving biomarkers are often hindered by prohibitively expensive laboratory tests. Strategically pooling specimens prior to performing these lab assays has been shown to effectively reduce cost with minimal information loss in a logistic regression setting. When the goal is to perform regression with a continuous biomarker as the outcome, regression analysis of pooled specimens may not be straightforward, particularly if the outcome is right-skewed. In such cases, we demonstrate that a slight modification of a standard multiple linear regression model for poolwise data can provide valid and precise coefficient estimates when pools are formed by combining biospecimens from subjects with identical covariate values. When these x-homogeneous pools cannot be formed, we propose a Monte Carlo expectation maximization (MCEM) algorithm to compute maximum likelihood estimates (MLEs). Simulation studies demonstrate that these analytical methods provide essentially unbiased estimates of coefficient parameters as well as their standard errors when appropriate assumptions are met. Furthermore, we show how one can utilize the fully observed covariate data to inform the pooling strategy, yielding a high level of statistical efficiency at a fraction of the total lab cost.

A highly efficient design strategy for regression with outcome pooling.

The potential for research involving biospecimens can be hindered by the prohibitive cost of performing laboratory assays on individual samples. To mitigate this cost, strategies such as randomly selecting a portion of specimens for analysis or randomly pooling specimens prior to performing laboratory assays may be employed. These techniques, while effective in reducing cost, are often accompanied by a considerable loss of statistical efficiency. We propose a novel pooling strategy based on the k-means clustering algorithm to reduce laboratory costs while maintaining a high level of statistical efficiency when predictor variables are measured on all subjects, but the outcome of interest is assessed in pools. We perform simulations motivated by the BioCycle study to compare this k-means pooling strategy with current pooling and selection techniques under simple and multiple linear regression models. While all of the methods considered produce unbiased estimates and confidence intervals with appropriate coverage, pooling under k-means clustering provides the most precise estimates, closely approximating results from the full data and losing minimal precision as the total number of pools decreases. The benefits of k-means clustering evident in the simulation study are then applied to an analysis of the BioCycle dataset. In conclusion, when the number of lab tests is limited by budget, pooling specimens based on k-means clustering prior to performing lab assays can be an effective way to save money with minimal information loss in a regression setting.

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.

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.

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.