Using negative controls to adjust for unmeasured confounding bias in time series studies

Unmeasured confounding threatens the validity of observational studies. Negative control variables (NCs) are variables that either do not cause the outcome of interest or are not caused by the exposure of interest and are increasingly available from emerging sensing technologies and digitized health records. Under appropriate assumptions, NCs can be used to adjust for unmeasured confounding bias. This Primer explains the assumptions and implementation of NCs for unmeasured confounding bias adjustment. Among the method's broad applications in public health research, time series studies of environmental exposures - air pollution, wildfires and heat - and health outcomes are focused on. Three types of unmeasured confounding in time series studies are considered: time-invariant confounders with time-invariant confounding effects; time-invariant confounders with time-modified confounding effects; and time-varying confounders with immediate and/or lagged confounding effects. For each type of confounding, guidance is provided on how to select NCs using several case studies. Finally, challenges and opportunities are described, to help catalyse additional methodological developments. Negative control variables (NCs) are variables that do not cause the outcome of interest and are not caused by the exposure of interest. This Primer describes how to use NCs to adjust for unmeasured confounding bias, for example in environmental or public health studies.