Choosing the Right Approach at the Right Time: A Comparative Analysis of Casual Effect Estimation using Confounder Adjustment and Instrumental Variables

In observational studies, unobserved confounding is a major barrier in isolating the average causal effect (ACE). In these scenarios, two main approaches are often used: confounder adjustment for causality (CAC) and instrumental variable analysis for causation (IVAC). Nevertheless, both are subject to untestable assumptions and, therefore, it may be unclear which assumption violation scenarios one method is superior in terms of mitigating inconsistency for the ACE. Although general guidelines exist, direct theoretical comparisons of the trade-offs between CAC and the IVAC assumptions are limited. Using ordinary least squares (OLS) for CAC and two-stage least squares (2SLS) for IVAC, we analytically compare the relative inconsistency for the ACE of each approach under a variety of assumption violation scenarios and discuss rules of thumb for practice. Additionally, a sensitivity framework is proposed to guide analysts in determining which approach may result in less inconsistency for estimating the ACE with a given dataset. We demonstrate our findings both through simulation and an application examining whether maternal stress during pregnancy affects a neonate's birthweight. The implications of our findings for causal inference practice are discussed, providing guidance for analysts for judging whether CAC or IVAC may be more appropriate for a given situation

Frameworks for Estimating Causal Effects in Observational Settings: Comparing Confounder Adjustment and Instrumental Variables

To estimate causal effects, analysts performing observational studies in health settings utilize several strategies to mitigate bias due to confounding by indication. There are two broad classes of approaches for these purposes: use of confounders and instrumental variables (IVs). Because such approaches are largely characterized by untestable assumptions, analysts must operate under an indefinite paradigm that these methods will work imperfectly. In this tutorial, we formalize a set of general principles and heuristics for estimating causal effects in the two approaches when the assumptions are potentially violated. This crucially requires reframing the process of observational studies as hypothesizing potential scenarios where the estimates from one approach are less inconsistent than the other. While most of our discussion of methodology centers around the linear setting, we touch upon complexities in non-linear settings and flexible procedures such as target minimum loss-based estimation (TMLE) and double machine learning (DML). To demonstrate the application of our principles, we investigate the use of donepezil off-label for mild cognitive impairment (MCI). We compare and contrast results from confounder and IV methods, traditional and flexible, within our analysis and to a similar observational study and clinical trial

Development and evaluation of a novel dietary bisphenol A (BPA) exposure risk tool

Background: Exposure to endocrine disrupting chemicals such as bisphenol A (BPA) is primarily from the diet through canned foods. Characterizing dietary exposures can be conducted through biomonitoring and dietary surveys; however, these methods can be time-consuming and challenging to implement. Methods: We developed a novel dietary exposure risk questionnaire to evaluate BPA exposure and compared these results to 24-hr dietary recall data from participants (n = 404) of the Diet Intervention Examining The Factors Interacting with Treatment Success (DIETFITS) study, a dietary clinical trial, to validate questionnaire responses. High BPA exposure foods were identified from the dietary recalls and used to estimate BPA exposure. Linear regression models estimated the association between exposure to BPA and questionnaire responses. A composite risk score was developed to summarize questionnaire responses. Results: In questionnaire data, 65% of participants ate canned food every week. A composite exposure score validated that the dietary exposure risk questionnaire captured increasing BPA exposure. In the linear regression models, utilizing questionnaire responses vs. 24-hr dietary recall data, participants eating canned foods 1–2 times/week (vs. never) consumed 0.78 more servings (p \u3c 0.001) of high BPA exposure foods, and those eating canned foods 3+ times/week (vs. never) consumed 0.89 more servings (p = 0.013) of high BPA exposure foods. Participants eating 3+ packaged items/day (vs. never) consumed 62.65 more total grams of high BPA exposure food (p = 0.036). Conclusions: Dietary exposure risk questionnaires may provide an efficient alternative approach to 24-hour dietary recalls to quantify dietary BPA exposure with low participant burden. Trial registration: The trial was prospectively registered at clinicaltrials.gov as NCT01826591 on April 8, 2013

A Neptune-mass exoplanet in close orbit around a very low-mass star challenges formation models

Stars and planetary system

Autoantibodies against type I IFNs in patients with life-threatening COVID-19

Interindividual clinical variability in the course of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is vast. We report that at least 101 of 987 patients with life-threatening coronavirus disease 2019 (COVID-19) pneumonia had neutralizing immunoglobulin G (IgG) autoantibodies (auto-Abs) against interferon-w (IFN-w) (13 patients), against the 13 types of IFN-a (36), or against both (52) at the onset of critical disease; a few also had auto-Abs against the other three type I IFNs. The auto-Abs neutralize the ability of the corresponding type I IFNs to block SARS-CoV-2 infection in vitro. These auto-Abs were not found in 663 individuals with asymptomatic or mild SARS-CoV-2 infection and were present in only 4 of 1227 healthy individuals. Patients with auto-Abs were aged 25 to 87 years and 95 of the 101 were men. A B cell autoimmune phenocopy of inborn errors of type I IFN immunity accounts for life-threatening COVID-19 pneumonia in at least 2.6% of women and 12.5% of men

Frameworks for estimating causal effects in observational settings: comparing confounder adjustment and instrumental variables

To estimate causal effects, analysts performing observational studies in health settings utilize several strategies to mitigate bias due to confounding by indication. There are two broad classes of approaches for these purposes: use of confounders and instrumental variables (IVs). Because such approaches are largely characterized by untestable assumptions, analysts must operate under an indefinite paradigm that these methods will work imperfectly. In this tutorial, we formalize a set of general principles and heuristics for estimating causal effects in the two approaches when the assumptions are potentially violated. This crucially requires reframing the process of observational studies as hypothesizing potential scenarios where the estimates from one approach are less inconsistent than the other. While most of our discussion of methodology centers around the linear setting, we touch upon complexities in non-linear settings and flexible procedures such as target minimum loss-based estimation and double machine learning. To demonstrate the application of our principles, we investigate the use of donepezil off-label for mild cognitive impairment. We compare and contrast results from confounder and IV methods, traditional and flexible, within our analysis and to a similar observational study and clinical trial