A Causal Inference Framework for Identifying Critical Windows of Time-Varying Exposures

Abstract

There has been great clinical interest in the concept of ‘critical windows’ or ‘sensitive periods’ of environmental exposures. The concept of a critical window, defined formally in this dissertation, refers to a specific time period during which an individual is more susceptible to developing an outcome in response to a particular exposure than at other times. The statistical methods used to identify these critical windows have several limitations, and applied researchers are often limited to using methods developed for different research questions which can lead to bias, inflated Type I error rates, and low power. Studies on environmental exposures are almost always observational by necessity, and it remains an ongoing challenge to interpret results as the causal effect of intervening on the exposure rather than merely as an association between the exposure and the outcome. Because the methods currently in use have not been previously examined through a causal inference lens, results across studies are difficult to compare, even if the same covariates are used. This dissertation seeks to combine these two areas of interest by proposing a framework for the identification of critical windows from a causal inference perspective. Throughout this work, we demonstrate how different methods should be employed to answer subtly different research questions and compare our methods to existing approaches through simulations where appropriate. In Chapter 1, we introduce our novel flexible CAusaL Identification of Critical windOws - Modified Treatment Policy (CALICO-MTP) framework, extending previous work on using a dose modification scheme to estimate the causal effect of continuous exposures. We propose dividing the concept of critical window identification into three distinct research questions, each addressed with different approaches. These questions are: 1) Curve estimation: what does the exposure-outcome relationship look like over time? 2) Hypothesis testing: is there any time window during which there is an effect of intervening on the exposure? and 3) Window selection: after determining that there is a causal relationship, what is the critical window for that exposure? For the first question, we propose a curve estimation strategy to yield results similar to those of the commonly used distributed lag model (DLM). For the second, we propose estimating the effect of intervening on all biologically plausible windows and combining the p-values using the Aggregated Cauchy Association Test (ACAT), a p-value combination method that accounts for strong correlations between test statistics. For the third, we discuss strategies for selecting the window once the global null has been rejected. We apply these methods to a dataset from Beth Israel Deaconess Medical Center (BIDMC) and compare them to previous results regarding the effect of Nitrogen Dioxide (NO2) exposure on the 32-40 week fetal head circumference as measured by ultrasound45, and we present a novel visualization for the causal effect of intervening on time intervals. In Chapter 2, we present a variant of this framework, CALICO-ADRF, that explicitly models nonlinear dose-response relationships by estimating the Average Dose Response Function (ADRF) for each time window. This nonlinear relationship is particularly relevant for environmental exposures such as metals, where some are necessary minerals at low exposures but act as toxins at high exposure levels, and temperature, which may exhibit a thresholding effect for certain outcomes. We use a scalar test statistic that is the integrated squared derivative of the estimated ADRF to perform global hypothesis testing with Type I error control and improved power compared to the methods of Chapter 1 for biologically-plausible nonlinear dose-response curves. We demonstrate these results looking at the effect of maternal prenatal temperature exposure and birthweight for full-term deliveries in the same BIDMC cohort. In Chapter 3, we present a discussion of causal inference concepts specifically tailored to the methods most commonly used for time-varying environmental exposures, offering a novel perspec- tive for researchers. We present a framework through which the target estimand of different mod- eling approaches can be compared, improving the ability to draw meaningful and comparable conclusions across studies. We explore the different estimands that these methods target and illustrate when these estimands align or diverge depending on the underlying causal structure of the exposure. Finally, we provide guidance for researchers on how to appropriately align their methodological choices with their research questions.Biostatistic