Search

There are now many published applications of causal (structural) models for estimating effects of time-varying exposures in the presence of confounding by earlier exposures and confounders affected by earlier exposures. Results from these models can be highly sensitive to inclusion of lagged and baseline exposure terms for different visits. This sensitivity is often overlooked in practice; moreover, results from these models are not directly comparable to results from conventional time-dependent regression models, because the latter do not estimate the same causal parameter even when no bias is present. We thus explore the implications of including lagged and baseline exposure terms in causal and regression models, using a public data set (Caerphilly Heart Disease Study in the United Kingdom, 1979-1998) relating smoking to cardiovascular outcomes.

Background: The use of machine learning to estimate exposure effects introduces a dependence between the results of an empirical study and the value of the seed used to fix the pseudo-random number generator. Methods: We used data from 10,038 pregnant women and a 10% subsample (N = 1004) to examine the extent to which the risk difference for the relation between fruit and vegetable consumption and preeclampsia risk changes under different seed values. We fit an augmented inverse probability weighted estimator with two Super Learner algorithms: a simple algorithm including random forests and single-layer neural networks and a more complex algorithm with a mix of tree-based, regression-based, penalized, and simple algorithms. We evaluated the distributions of risk differences, standard errors, and P values that result from 5000 different seed value selections. Results: Our findings suggest important variability in the risk difference estimates, as well as an important effect of the stacking algorithm used. The interquartile range width of the risk differences in the full sample with the simple algorithm was 13 per 1000. However, all other interquartile ranges were roughly an order of magnitude lower. The medians of the distributions of risk differences differed according to the sample size and the algorithm used. Conclusions: Our findings add another dimension of concern regarding the potential for "p-hacking," and further warrant the need to move away from simplistic evidentiary thresholds in empirical research. When empirical results depend on pseudo-random number generator seed values, caution is warranted in interpreting these results. [ABSTRACT FROM AUTHOR]

Background: Neighborhood poverty is associated with adiposity in women, though longitudinal designs, annually collected residential histories, objectively collected anthropometric measures, and geographically diverse samples of midlife women remain limited. Objective: To investigate whether longitudinal exposure to neighborhood concentrated poverty is associated with differences in body mass index (BMI) and waist circumference (WC) among 2,328 midlife women (age 42–52 years at baseline) from 6 U.S. cities enrolled in the Study of Women's Health Across the Nation (SWAN) from 1996 to 2007. Methods: Residential addresses and adiposity measures were collected at approximately annual intervals from the baseline visit through a 10-year follow-up. We used census poverty data and local spatial statistics to identify hot-spots of high concentrated poverty areas and cold-spots of low concentrated poverty located within each SWAN site region, and used linear mixed-effect models to estimate percentage differences (95% confidence interval [CI]) in average BMI and WC levels between neighborhood concentrated poverty categories. Results: After adjusting for individual-level sociodemographics, health-related factors, and residential mobility, compared to residents of moderate concentrated poverty communities, women living in site-specific hot-spots of high concentrated poverty had 1.5% higher (95% CI: 0.6, 2.3) BMI and 1.3% higher (95% CI: 0.5, 2.0) WC levels, whereas women living in cold-spots of low concentrated poverty had 0.7% lower (95% CI: −1.2, −0.1) BMI and 0.3% lower (95% CI: −0.8, 0.2) WC. Site-stratified results remained in largely similar directions to overall estimates, despite wide CIs and small sample sizes. Conclusions: Longitudinal exposure to neighborhood concentrated poverty is associated with slightly higher BMI and WC among women across midlife. [ABSTRACT FROM AUTHOR]

Modern longitudinal studies collect feature data at many timepoints, often of the same order of sample size. Such studies are typically affected by dropout and positivity violations. We tackle these problems by generalizing effects of recent incremental interventions (which shift propensity scores rather than set treatment values deterministically) to accommodate multiple outcomes and subject dropout. We give an identifying expression for incremental intervention effects when dropout is conditionally ignorable (without requiring treatment positivity) and derive the nonparametric efficiency bound for estimating such effects. Then we present efficient nonparametric estimators, showing that they converge at fast parametric rates and yield uniform inferential guarantees, even when nuisance functions are estimated flexibly at slower rates. We also study the variance ratio of incremental intervention effects relative to more conventional deterministic effects in a novel infinite time horizon setting, where the number of timepoints can grow with sample size and show that incremental intervention effects yield near-exponential gains in statistical precision in this setup. Finally, we conclude with simulations and apply our methods in a study of the effect of low-dose aspirin on pregnancy outcomes.

Administrative claims databases often do not capture date or fact of death, so studies using these data may inappropriately treat death as a censoring event—equivalent to other withdrawal reasons—rather than a competing event. We examined 1-, 3-, and 5-year inverse-probability-of-treatment weighted cumulative risks of a composite cardiovascular outcome among 34 527 initiators of telmisartan (exposure) and ramipril (referent), who were aged ≥55 years, in Optum (United States) claims data from 2003 to 2020. Differences in cumulative risks of the cardiovascular endpoint due to censoring of death (cause-specific), as compared with treating death as a competing event (subdistribution), increased with greater follow-up time and older age, where event and mortality risks were higher. Among ramipril users, 5-year cause-specific and subdistribution cumulative risk estimates per 100, respectively, were 16.4 (95% CI, 15.3-17.5) and 16.2 (95% CI, 15.1-17.3) among ages 55-64 (difference = 0.2) and were 43.2 (95% CI, 41.3-45.2) and 39.7 (95% CI, 37.9-41.4) among ages ≥75 (difference = 3.6). Plasmode simulation results demonstrated the differences in cause-specific versus subdistribution cumulative risks to increase with increasing mortality rate. We suggest researchers consider the cohort's baseline mortality risk when deciding whether real-world data with incomplete death information can be used without concern. This article is part of a Special Collection on Pharmacoepidemiology. [ABSTRACT FROM AUTHOR]