环境流行病学中的曝露与疾病发生之间的关系是一个重要问题。通常，测量到大量的这些曝露源，发现或者只有少数曝露源传播风险，或者每个曝露源传播的风险很小，但是总体上来说，这些曝露源可能会构成一个相当大的疾病风险。而且，这些曝露源的效应可能是非线性的。我们提出了一种潜在函数方法，该方法假设每个曝露源的个体效应可以被描述为一系列未观测到的函数之一，其中潜在函数的数量小于或等于曝露源的数量。我们提出了贝叶斯方法来拟合具有大量曝露源的模型，并表明现有的贝叶斯组LASSO方法是所提出的模型的一种特殊情况。我们开发了一个高效的马尔可夫链蒙特卡洛采样算法来进行贝叶斯推断。我们使用偏差信息准则选择了一个合适的非线性潜在函数的数量。我们使用模拟研究证明了该方法的优良性能。此外，我们展示了只需要几个潜在函数曲线即可表示复杂的曝露关系。我们通过对一大型农民队列中农药累积曝露对癌症风险的影响进行的分析来阐明了所提出的方法。© 2023 John Wiley & Sons, Ltd.

Understanding the relationships between exposure and disease incidence is an important problem in environmental epidemiology. Typically, a large number of these exposures are measured, and it is found either that a few exposures transmit risk or that each exposure transmits a small amount of risk, but, taken together, these may pose a substantial disease risk. Further, these exposure effects can be nonlinear. We develop a latent functional approach, which assumes that the individual effect of each exposure can be characterized as one of a series of unobserved functions, where the number of latent functions is less than or equal to the number of exposures. We propose Bayesian methodology to fit models with a large number of exposures and show that existing Bayesian group LASSO approaches are a special case of the proposed model. An efficient Markov chain Monte Carlo sampling algorithm is developed for carrying out Bayesian inference. The deviance information criterion is used to choose an appropriate number of nonlinear latent functions. We demonstrate the good properties of the approach using simulation studies. Further, we show that complex exposure relationships can be represented with only a few latent functional curves. The proposed methodology is illustrated with an analysis of the effect of cumulative pesticide exposure on cancer risk in a large cohort of farmers.© 2023 John Wiley & Sons, Ltd.