arXiv subjects

Chase D. Latour

Publications and source records attributed to Chase D. Latour.

5 recordsLinked to original sources

arXiv2026

Early Pregnancy Treatment Decisions: Designing Perinatal Pharmacoepidemiology Studies using Real-World Data

Research on the use of medications during pregnancy has two primary goals: to detect signals that medications may be harmful to a pregnant individual or fetus, and to support better treatment of pregnant people who require pharmacotherapy. Target trial emulation has been proposed as an approach to estimate the effects of interventions in real world data, with recent extensions to the pregnancy setting. This approach focuses on aligning eligibility and treatment initiation with start of follow up, which is particularly desirable given methodological challenges specific to pregnancy, such as right and left censoring and truncation, competing events, differences in gestational length, and varying etiologically susceptible periods. While previous work on target trial emulation in pregnancy has focused on initiation versus non-initiation of point treatments such as vaccines or antibiotics, this paper focuses on research questions regarding changes to pregestational treatment regimes, and aims to highlight opportunities and approaches to designing studies that align with relevant time points during early pregnancy at which treatment decisions occur in clinical practice. Using the example of treatment for type 2 diabetes mellitus, we review methods for identifying pregnancy episodes in routinely collected healthcare data, introduce possible time zero candidates, and discuss analytic approaches that minimize potential bias due to selection and immortal person time.

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arXiv2026

Comparing causal estimands from sequential nested versus single point target trials: A simulation study

Sequential nested trial (SNT) emulation is a powerful approach for maximizing precision and avoiding time-related biases. However, there exists little discussion about the implied causal estimands in comparison to a real-world single point trial. We used Monte Carlo simulation to compare treatment effect estimates from an SNT emulation that re-indexed patients annually and a SNT emulation with a treatment decision design to the estimates from a single point trial. We generated 5,000 cohorts of 5,000 people with 3 years of follow-up. For the single point trial, patients were randomized to initiate or not initiate treatment at Visit 1. For the SNT emulations, simulated patients could contribute up to two index dates. When disease severity did not modify the treatment effect, both SNT approaches returned treatment effect estimates identical to the single point trial. In the presence of treatment effect modification by disease severity, both SNT approaches returned treatment effect estimates that diverged from the single point trial even after confounding-adjustment. These findings underscore the difficulties of interpreting causal estimands from a SNT emulation: the target population does not correspond to a single time point trial. Such implications are important for communicating study results for evidence-based decision-making.

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arXiv2025

Bias in studies of prenatal exposures using real-world data due to pregnancy identification method

Background: Researchers typically identify pregnancies in healthcare data based on observed outcomes (e.g., delivery). This outcome-based approach misses pregnancies that received prenatal care but whose outcomes were not recorded (e.g., at-home miscarriage), potentially inducing selection bias in effect estimates for prenatal exposures. Alternatively, prenatal encounters can be used to identify pregnancies, including those with unobserved outcomes. However, this prenatal approach requires methods to address missing data. Methods: We simulated 10,000,000 pregnancies and estimated the total effect of initiating treatment on the risk of preeclampsia. We generated data for 36 scenarios in which we varied the effect of treatment on miscarriage and/or preeclampsia; the percentage with missing outcomes (5% or 20%); and the cause of missingness: (1) measured covariates, (2) unobserved miscarriage, and (3) a mix of both. We then created three analytic samples to address missing pregnancy outcomes: observed deliveries, observed deliveries and miscarriages, and all pregnancies. Treatment effects were estimated using non-parametric direct standardization. Results: Risk differences (RDs) and risk ratios (RRs) from the three analytic samples were similarly biased when all missingness was due to unobserved miscarriage (log-transformed RR bias range: -0.12-0.33 among observed deliveries; -0.11-0.32 among observed deliveries and miscarriages; and -0.11-0.32 among all pregnancies). When predictors of missingness were measured, only the all pregnancies approach was unbiased (-0.27-0.33; -0.29-0.03; and -0.02-0.01, respectively). Conclusions: When all missingness was due to miscarriage, the analytic samples returned similar effect estimates. Only among all pregnancies did bias decrease as the proportion of missingness due to measured variables increased.

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arXiv2024

Healthy Live Births Should be Considered as Competing Events when Estimating the Total Effect of Prenatal Medication Use on Pregnancy Outcomes

Pregnancy loss is recognized as an important competing event in studies of prenatal medication use. However, a healthy live birth also precludes subsequent adverse pregnancy outcomes, yet these events are often censored. Using Monte Carlo simulation, we examine bias that results from failure to account for healthy live birth as a competing event in estimates of the total effect of prenatal medication use on pregnancy outcomes. We simulated data for 12 trials estimating the effect of antihypertensive initiation versus non-initiation on two outcomes: (1) composite fetal death or severe prenatal preeclampsia and (2) small-for-gestational-age (SGA) live birth. We used time-to-event methods to estimate absolute risks, risk differences and risk ratios. For the composite outcome, we conducted two analyses where non-preeclamptic live birth was (1) a censoring event and (2) a competing event. For SGA live birth, we conducted three analyses where fetal death and non-SGA live birth were (1) censoring events, (2) a competing event and censoring event, respectively; and (3) competing events. In all analyses, censoring healthy live births led to inflated absolute risk estimates as well as bias and imprecise treatment effect estimates. Studies of prenatal exposures on pregnancy outcomes should analyze healthy live births as competing risks to estimate unbiased total treatment effects.

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arXiv2023

Treatments for pregestational chronic conditions during pregnancy: emulating a target trial with a treatment decision design

As a solution to methodologic challenges inherent to estimating causal effects of exposures in early pregnancy, we suggest emulating a target trial using a treatment decision design, wherein time zero is centered around clinical landmarks where treatment decisions may occur, such as the date of preconception counseling or prenatal care initiation. These ideas are illustrated via protocols for two target trials in large administrative databases, antidepressant use for pre-existing depressive disorder and antihypertensive medication use for mild-to-moderate chronic hypertension. Careful consideration of these issues is critical to the identification of the causal effects of early-pregnancy pharmacotherapies on pregnancy outcomes.

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