Biomarkers are widely used in perinatal epidemiology to examine the health effects of environmental chemical exposures during pregnancy. These measurements take the form of chemical concentrations measured in blood, urine, or other biospecimens. Biomarkers have the advantage of providing objective estimates of chemical exposures from multiple sources. However, they are difficult to handle at the data analysis stage. We review recent trends and developments in the statistical analysis of biomarkers with particular emphasis on exposure assessment and multivariable modeling. Six statistical challenges are presented in the recent literature: (1) the analysis of biomarkers that fall below the limit of detection, (2) adjustment for dilution-dependent sample variation, (3) handling repeated biomarker measurements within a single pregnancy, (4) accounting for heterogeneity in biomarker levels between chemicals within the same chemical class, (5) variable selection and shrinkage for biomarkers in the same class, and finally, (6) dimension reduction strategies including the sum-of-chemical approach. The analysis of biomarkers of environmental chemical exposures remains immensely difficult, and the proper application of emerging statistical techniques requires input from experts in diverse disciplines. We highlight specific gaps in the literature where innovation in statistical methods is required.