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Purpose of Review The purpose of this review is to outline the main questions in environmental mixtures research and provide a non-technical explanation of novel or advanced methods to answer these questions. Recent Findings Machine learning techniques are now being incorporated into environmental mixture research to overcome issues with traditional methods. Though some methods perform well on specific tasks, no method consistently outperforms all others in complex mixture analyses, largely because different methods were developed to answer different research questions. We discuss four main questions in environmental mixtures research: (1) Are there specific exposure patterns in the study population? (2) Which are the toxic agents in the mixture? (3) Are mixture members acting synergistically? And, (4) what is the overall effect of the mixture? Summary We emphasize the importance of robust methods and interpretable results over predictive accuracy. We encourage collaboration with computer scientists, data scientists, and biostatisticians in future mixture method development.

Purpose of Review We offer here a review of intraindividual variability in urinary biomarkers for assessing exposure to nonpersistent chemicals. We provide thoughts on how to better evaluate exposure to nonpersistent chemicals. Recent Findings We summarized reported values of intraclass correlation coefficients and found that most values fall into categories that indicate only poor to good reproducibility. Even within the “good” classification, a large percentage of study participants is likely to be misclassified as to their exposure. Summary There is sufficient information to support the statement that studies using only one spot measurement of a nonpersistent chemical will be unreliable. It is unequivocal that multiple samples have to be collected over a period of toxicological relevance and with consideration of exposure patterns. Sponsors of research and researchers themselves should be vocal about ensuring that sufficient resources are made available to properly characterize exposures when studying nonpersistent chemicals. Otherwise, we will continue to see an ever-growing body of literature yielding inconsistent and/or uninterpretable results.

Purpose of Review Children’s environmental health researchers are increasingly interested in identifying time intervals during which individuals are most susceptible to adverse impacts of environmental exposures. We review recent advances in methods for assessing susceptible periods. Recent Findings We identified three general classes of modeling approaches aimed at identifying susceptible periods in children’s environmental health research: multiple informant models, distributed lag models, and Bayesian approaches. Benefits over traditional regression modeling include the ability to formally test period effect differences, to incorporate highly time-resolved exposure data, or to address correlation among exposure periods or exposure mixtures. Summary Several statistical approaches exist for investigating periods of susceptibility. Assessment of susceptible periods would be advanced by additional basic biological research, further development of statistical methods to assess susceptibility to complex exposure mixtures, validation studies evaluating model assumptions, replication studies in different populations, and consideration of susceptible periods from before conception to disease onset.

Background Urinary biomonitoring is widely used to assess environmental chemical exposure; however, a critical gap exists in whether and how to correct for the physiological variation in water content of spot urine samples. Objective The aim of this systematic review is to summarize the available evidence comparing the performance of urinary concentration correction methods used to determine urinary levels of arsenic, cadmium, and mercury. Methods We searched PubMed/MEDLINE, Embase, LILIAC, Web of Science, and TOXNET up to Sept. 5, 2017 for articles evaluating urinary concentration correction methods (e.g., urine creatinine [U-Cre], specific gravity [U-SG], osmolality [U-Osm]) compared to 24-h or timed urine specimens for levels of arsenic, cadmium, and mercury. Data on study design, methods of urine collection, and the performance of selected correction methods were extracted. Results A total of 10 papers met the inclusion criteria. Two papers evaluated the performance of urinary concentration correction methods for arsenic, four for cadmium, three for mercury, and one for multiple metals. The median sample size for arsenic was 105, for cadmium 107, and for mercury 35. The studies were highly heterogeneous in population selection, urine collection, urine quality control, statistical comparison among selected correction methods, and presentation of the results. The median (range) of correlation coefficients comparing each corrected values with corresponding levels of timed urine specimens are 0.74 (0.17–0.92) for un-correction (n = 13), 0.82 (0.52–0.98) for U-Cre (n = 13), and 0.75 (0.28–0.98) (n = 12) for U-SG. Conclusion Findings from limited evidence support that urine creatinine and urine-specific gravity corrections remain practical approaches to correct metal concentrations for urine dilution as compared to 24-h or 12-h urine samples. Further studies with larger sample sizes are needed to clarify this fundamental issue of environmental biomonitoring using spot urine samples in both general and priority populations.

Purpose of Review Incomplete data are a common problem in statistical analysis of environmental epidemiological research. However, many researchers still ignore this complication. We evaluate the performance of two commonly used multiple imputation (MI) methods (fully conditional specification and multivariate normal) for handling missing data and compare them to complete case analysis (CCA) method. We further discuss issues that arise when these methods are being used. Recent Findings MI is a simulation-based approach to deal with incomplete data. In general, MI will perform better then ad hoc techniques such as CCA. MI is an approach which replaces the missing data with plausible values and allows for additional uncertainty due to the missing information caused by the incomplete data. To illustrate this, we use data of 944 women from the Collaborative Perinatal Project and compare estimates between these methods. The goal is to examine if each of two outcomes, birth-weight and spontaneous abortion, in the data set are associated with mothers’ smoking status during pregnancy adjusting for baseline covariates in the model. Summary Results indicate that MI is better suited for handling incomplete data and led to a significant improvement in parameter estimates compared to CCA. The two MI methods produced similar point estimates, but slightly different standard errors.

Purpose of Review In recent years, investigators in a variety of fields have reported that most published findings can not be replicated. This review evaluates the factors contributing to lack of reproducibility, implications for environmental epidemiology, and strategies for mitigation. Recent Findings Although publication bias and other types of selective reporting may contribute substantially to irreproducible results, underpowered analyses and low prevalence of true associations likely explain most failures to replicate novel scientific results. Epidemiologists can counter these risks by ensuring that analyses are well-powered or precise, focusing on scientifically justified hypotheses, strictly controlling type I error rates, emphasizing estimation over statistical significance, avoiding practices that introduce bias, or employing bias analysis and triangulation. Avoidance of p values has no effect on reproducibility if confidence intervals excluding the null are emphasized in a similar manner. Summary Increased attention to exposure mixtures and susceptible subpopulations, and wider use of omics technologies, will likely decrease the proportion of investigated associations that are true associations, requiring greater caution in study design, analysis, and interpretation. Though well intentioned, these recent trends in environmental epidemiology will likely decrease reproducibility if no effective actions are taken to mitigate the risk of spurious findings.