Understanding the complex association between chemical exposure and disease risk is important for understanding the relationship between environmental exposure and disease incidence. In some cases, disease risk may be influenced by a few of many chemical exposures at a sufficiently high dose. In other cases, small doses of many chemicals may transmit increased risk (so called “low dose additivity”). Statistical approaches are needed that allow investigators to identify complex associations that include both these extreme types of dose-response. Different types of latent class models will be discussed to analyze this type of data. The first approach jointly models the relationship between chemical exposure and disease risk through latent risk classes that link the two processes together (Zhang, et al, Biostatistics, 2012; Hwang, et al. Biometrics, 2019). In the second approach, we model the relationship by introducing latent trajectories that govern the cumulative dose-response relationship of individual chemicals on disease incidence. The methods are illustrated with data from population-based studies with gynecological and cancer outcomes. Of focus will be an analysis from the Agricultural Health Study (AHS), an NCI study investigating the relationship between pesticide use in farmers and their associated cancer risk.