The news: A bill that allows clinics to sell unproven treatments has been passed in Montana. Under the legislation, doctors can apply for a license to open an experimental treatment clinic and recommend and sell therapies not approved by the Food and Drug Administration (FDA) to their patients. Why it matters: Once it's signed by the governor, the law will be the most expansive in the country in allowing access to drugs that have not been fully tested. The bill allows for any drug produced in the state to be sold in it, providing it has been through phase I clinical trials--but these trials do not determine if the drug is effective. The big picture: The bill was drafted and lobbied for by people interested in extending human lifespans.

Additionally, experiments have shown that depersonalization symptoms can arise as a reaction to alcohol consumption (Raimo et al., 1999), and they are increasingly recognized as a significant prognostic factor in the course of depression (Michal et al., 2024). Despite these findings, little research has explored the mediating role of depersonalization symptoms in the causal pathway from alcohol consumption to depression. In this paper, we propose a methodological framework to evaluate the indirect effect of alcohol consumption on depression, with depersonalization acting as a mediator. To ground our analysis, we use data from a cross-sectional survey conducted during the COVID-19 pandemic by Dom ınguez-Espinosa et al. (2023) as a running example. In observational studies, the population average causal effect (ACE) and the natural indirect effect (NIE) are the most commonly used measures of total and mediation effects, respectively, to compare the outcomes of different intervention policies. For instance, in our running example, these two measures compare the depression outcomes between individuals engaging in hazardous versus non-hazardous alcohol consumption. However, clinical practice imposes ethical constraints, as healthcare professionals would not prescribe harmful levels of alcohol consumption. As a result, hypothetical interventions involving dangerous exposure levels are unrealistic. To address this situation with potentially harmful exposure, Hubbard and Van der Laan (2008) propose the population intervention effect (PIE), which contrasts outcomes between the natural population and a hypothetical population where no one is exposed to the harmful exposure level.

The joint probabilities of potential outcomes are fundamental components of causal inference in the sense that (i) if they are identifiable, then the causal risk is also identifiable, but not vise versa (Pearl, 2009; Tian and Pearl, 2000) and (ii) they enable us to evaluate the probabilistic aspects of "necessity", "sufficiency", and "necessity and sufficiency", which are important concepts of successful explanation (Watson, et al., 2020). However, because they are not identifiable without any assumptions, various assumptions have been utilized to evaluate the joint probabilities of potential outcomes, e.g., the assumption of monotonicity (Pearl, 2009; Tian and Pearl, 2000), the independence between potential outcomes (Robins and Richardson, 2011), the condition of gain equality (Li and Pearl, 2019), and the specific functional relationships between cause and effect (Pearl, 2009). Unlike existing identification conditions, in order to evaluate the joint probabilities of potential outcomes without such assumptions, this paper proposes two types of novel identification conditions using covariate information. In addition, when the joint probabilities of potential outcomes are identifiable through the proposed conditions, the estimation problem of the joint probabilities of potential outcomes reduces to that of singular models and thus they can not be evaluated by standard statistical estimation methods. To solve the problem, this paper proposes a new statistical estimation method based on the augmented Lagrangian method and shows the asymptotic normality of the proposed estimators. Given space constraints, the proofs, the details on the statistical estimation method, some numerical experiments, and the case study are provided in the supplementary material.

IMPORTANCE Women are underdiagnosed and undertreated for cardiovascular disease. Automatic quantification of breast arterial calcification on screening mammography can identify women at risk for cardiovascular disease and enable earlier treatment and management of disease. OBJECTIVE To determine whether artificial-intelligence based automatic quantification of BAC from screening mammograms predicts cardiovascular disease and mortality in a large, racially diverse, multi-institutional population, both independently and beyond traditional risk factors and ASCVD scores. DESIGN, SETTING, AND PARTICIPANTS Retrospective cohort study of 116,135 women from two healthcare systems (Emory Healthcare and Mayo Clinic Enterprise) who had screening mammograms and either experienced a major adverse cardiovascular event, death, or had at least 5 years of clinical follow-up. BAC was quantified using a novel transformer-based neural network architecture for semantic segmentation. BAC severity was categorized into four groups (no BAC, mild, moderate, and severe), with outcomes assessed using Kaplan-Meier analysis and Cox proportional-hazards models. MAIN OUTCOMES AND MEASURES Major Adverse Cardiovascular Events (MACE), including acute myocardial infarction, stroke, heart failure, and all-cause mortality, adjusted for traditional risk factors and Atherosclerotic CVD (ASCVD) risk scores. RESULTS BAC severity was independently associated with MACE after adjusting for cardiovascular risk factors, with increasing hazard ratios from mild (HR 1.18-1.22),

Predictive modelling is vital to guide preventive efforts. Whilst large-scale prospective cohort studies and a diverse toolkit of available machine learning (ML) algorithms have facilitated such survival task efforts, choosing the best-performing algorithm remains challenging. Benchmarking studies to date focus on relatively small-scale datasets and it is unclear how well such findings translate to large datasets that combine omics and clinical features. We sought to benchmark eight distinct survival task implementations, ranging from linear to deep learning (DL) models, within the large-scale prospective cohort study UK Biobank (UKB). We compared discrimination and computational requirements across heterogenous predictor matrices and endpoints. Finally, we assessed how well different architectures scale with sample sizes ranging from n = 5,000 to n = 250,000 individuals. Our results show that discriminative performance across a multitude of metrices is dependent on endpoint frequency and predictor matrix properties, with very robust performance of (penalised) COX Proportional Hazards (COX-PH) models. Of note, there are certain scenarios which favour more complex frameworks, specifically if working with larger numbers of observations and relatively simple predictor matrices. The observed computational requirements were vastly different, and we provide solutions in cases where current implementations were impracticable. In conclusion, this work delineates how optimal model choice is dependent on a variety of factors, including sample size, endpoint frequency and predictor matrix properties, thus constituting an informative resource for researchers working on similar datasets. Furthermore, we showcase how linear models still display a highly effective and scalable platform to perform risk modelling at scale and suggest that those are reported alongside non-linear ML models.

Conventional survival metrics, such as Harrell's concordance index and the Brier Score, rely on the independent censoring assumption for valid inference in the presence of right-censored data. However, when instances are censored for reasons related to the event of interest, this assumption no longer holds, as this kind of dependent censoring biases the marginal survival estimates of popular nonparametric estimators. In this paper, we propose three copula-based metrics to evaluate survival models in the presence of dependent censoring, and design a framework to create realistic, semi-synthetic datasets with dependent censoring to facilitate the evaluation of the metrics. Our empirical analyses in synthetic and semi-synthetic datasets show that our metrics can give error estimates that are closer to the true error, mainly in terms of predictive accuracy.

Long-term causal inference is an important but challenging problem across various scientific domains. To solve the latent confounding problem in long-term observational studies, existing methods leverage short-term experimental data. Ghassami et al. propose an approach based on the Conditional Additive Equi-Confounding Bias (CAECB) assumption, which asserts that the confounding bias in the short-term outcome is equal to that in the long-term outcome, so that the long-term confounding bias and the causal effects can be identified. While effective in certain cases, this assumption is limited to scenarios with a one-dimensional short-term outcome. In this paper, we introduce a novel assumption that extends the CAECB assumption to accommodate temporal short-term outcomes. Our proposed assumption states a functional relationship between sequential confounding biases across temporal short-term outcomes, under which we theoretically establish the identification of long-term causal effects. Based on the identification result, we develop an estimator and conduct a theoretical analysis of its asymptotic properties. Extensive experiments validate our theoretical results and demonstrate the effectiveness of the proposed method.

ABSTRACT 2 Background: The search for new biomarkers that allow an early diagnosis in sepsis has become a necessity in medicine. The objective of this study is to identify potential protein biomarkers of differential expression between sepsis and non - infectious systemic inflamm atory response syndrome (NISIRS). Methods: Prospective observational study of a cohort of septic patients activated by the Sepsis Code and patients admitted with NISIRS, during the period 2016 - 2017. A mass spectrometry - based approach was used to analyze the plasma proteins in the enrolled subjects . Subsequently, using recursive feature elimination (RFE) classification and cross - validation with a vector classifier, an association of these proteins in patients with sepsis compared to patients with NISIRS. The protein - protein interaction netwo rk was analyzed with String software. Results: A total of 277 patients (141 with sepsis and 136 with NISIRS) were included. Conclusion: There are proteomic patterns associated with sepsis compared to NISIRS with different strength of association. Advances in understanding these protein changes may allow for the identification of new biomarkers or therapeutic targets in the future. Key words: Sepsis, Septic shock, SIRS, Proteomics, Omics, Diagnosis INTRODUCTION 3 Sepsis is known as a clinical syndrome where life - threatening organ dysfunction occurs due to a dysregulated host response to infection.

A.1 Proof of Theorem 1 From Conditions 1 and 2 in Theorem 1, by the consistency property, we have p(x S, Q = M S, (A.11) where the notation " " stands for a transposed vector/matrix. Since P is invertible from Condition 3 in Theorem 1, from equation (A.11), R is given as the solution of the simultaneous linear equation From equation (A.12), since we have QP P = S from equation (A.11), and the first column of S is given as (p(u A.2 Proof of Theorem 2 From Conditions 4 and 5 of Theorem 2, by the consistency property, we have p(y The equation (B.5) is the condition in which the first column of (Θ Once we obtain the estimator R as the solution of the optimization problem (B.6), the estimator of u = (p(u Similarly, we can estimate causal risk difference as the difference between the second and third components of û. S, (B.17) thus, we have S. Because it means that S is a solution of the following minimization problem 1 The equations (B.19) and (B.20) are the conditions in which the first row of P As we can see immediately, for the zero Θ of the estimating equation (B.28), the any row permutated matrix Π Θ is also the solution of the same estimating equation, where Π is the permutation matrix. Therefore, we find the row permutated matrix ΠΘ, which achieve the smallest losses and adopt the matrix as the estimator of S. Once we obtain the estimator Θ as the solution of the optimization problem (B.21), the estimator of u = (p(u B.3 Asymptotic normality Following Yuan and Jennrich [5], we show the asymptotic normality of the estimators from Algorithm 2. In this section, we investigate more properties of our proposed estimators through more numerical experiments in addition to Section 5. Letting X, Y, Z, W, and U be discrete variables, we consider the causal diagrams shown in Figure 1, where the joint probabilities of (X, Y, Z, W, U) are given according Table C.1. As seen from Table C.2, the sample means of p(u In addition, the outliers would occur when it is difficult to judge that Condition 6 holds from observed data.