A Review of Causal Inference Methods for Estimating the Effects of Exposure Change when Incident Exposure Is Unobservable

Research questions on exposure change and health outcomes are both relevant to clinical and policy decision making for public health. Causal inference methods can help investigators answer questions about exposure change when the first or incident exposure is unobserved or not well defined. This review aims to help researchers conceive of helpful causal research questions about exposure change and understand various statistical methods for answering these questions to promote wider adoption of causal inference methods in research on exposure change outside the field of pharmacoepidemiology. Epidemiologic studies examining exposure changes face challenges that can be addressed by causal inference methods, including target trial emulation. However, their application outside the field of pharmacoepidemiology is limited. In this review, we (a) illustrate considerations in defining an exposure change and defining the total and joint effects of an exposure change, (b) provide practical guidance on trial emulation design and data set-up for statistical analysis, (c) demonstrate four statistical methods that can estimate total and/or joint effects (structural conditional mean models, time-dependent matching, inverse probability weighting, and the parametric g-formula), and (d) compare the advantages and limitations of these statistical methods.