Coherent modeling of longitudinal causal effects on binary outcomes.

Analyses of biomedical studies often necessitate modeling longitudinal causal effects. The current focus on personalized medicine and effect heterogeneity makes this task even more challenging. Toward this end, structural nested mean models (SNMMs) are fundamental tools for studying heterogeneous treatment effects in longitudinal studies.

Acyclic Linear SEMs Obey the Nested Markov Property.

The conditional independence structure induced on the observed marginal distribution by a hidden variable directed acyclic graph (DAG) may be represented by a graphical model represented by mixed graphs called maximal ancestral graphs (MAGs). This model has a number of desirable properties, in particular the set of Gaussian distributions can be parameterized by viewing the graph as a path diagram. Models represented by MAGs have been used for causal discovery [22], and identification theory for causal effects [28].

On falsification of the binary instrumental variable model.

Instrumental variables are widely used for estimating causal effects in the presence of unmeasured confounding. The discrete instrumental variable model has testable implications for the law of the observed data. However, current assessments of instrumental validity are typically based solely on subject-matter arguments rather than these testable implications, partly due to a lack of formal statistical tests with known properties.

Identification and estimation of causal effects with outcomes truncated by death.

It is common in medical studies that the outcome of interest is truncated by death, meaning that a subject has died before the outcome could be measured. In this case, restricted analysis among survivors may be subject to selection bias. Hence, it is of interest to estimate the survivor average causal effect, defined as the average causal effect among the subgroup consisting of subjects who would survive under either exposure.

Partial Identification of the Average Treatment Effect Using Instrumental Variables: Review of Methods for Binary Instruments, Treatments, and Outcomes.

Several methods have been proposed for partially or point identifying the average treatment effect (ATE) using instrumental variable (IV) type assumptions. The descriptions of these methods are widespread across the statistical, economic, epidemiologic, and computer science literature, and the connections between the methods have not been readily apparent. In the setting of a binary instrument, treatment, and outcome, we review proposed methods for partial and point identification of the ATE under IV assumptions, express the identification results in a common notation and terminology, and propose a taxonomy that is based on sets of identifying assumptions.

Atmospheric Sampling on Ascension Island Using Multirotor UAVs.

As part of an NERC-funded project investigating the southern methane anomaly, a team drawn from the Universities of Bristol, Birmingham and Royal Holloway flew small unmanned multirotors from Ascension Island for the purposes of atmospheric sampling. The objective of these flights was to collect air samples from below, within and above a persistent atmospheric feature, the Trade Wind Inversion, in order to characterise methane concentrations and their isotopic composition. These parameters allow the methane in the different air masses to be tied to different source locations, which can be further analysed using back trajectory atmospheric computer modelling.

Causal analysis of ordinal treatments and binary outcomes under truncation by death.

It is common that in multi-arm randomized trials, the outcome of interest is "truncated by death," meaning that it is only observed or well-defined conditioning on an intermediate outcome. In this case, in addition to pairwise contrasts, the joint inference for all treatment arms is also of interest. Under a monotonicity assumption we present methods for both pairwise and joint causal analyses of ordinal treatments and binary outcomes in presence of truncation by death.

Accounting for Life-Course Exposures in Epigenetic Biomarker Association Studies: Early Life Socioeconomic Position, Candidate Gene DNA Methylation, and Adult Cardiometabolic Risk.

Recent studies suggest that epigenetic programming may mediate the relationship between early life environment, including parental socioeconomic position, and adult cardiometabolic health. However, interpreting associations between early environment and adult DNA methylation may be difficult because of time-dependent confounding by life-course exposures. Among 613 adult women (mean age = 32 years) of the Jerusalem Perinatal Study Family Follow-up (2007-2009), we investigated associations between early life socioeconomic position (paternal occupation and parental education) and mean adult DNA methylation at 5 frequently studied cardiometabolic and stress-response genes (ABCA1, INS-IGF2, LEP, HSD11B2, and NR3C1).

Are Early-Life Socioeconomic Conditions Directly Related to Birth Outcomes? Grandmaternal Education, Grandchild Birth Weight, and Associated Bias Analyses.

Grandmaternal education may be related to grandchild birth weight (GBW) through maternal early-life development; however, conventional regression models may be endogenously confounded. Alternative models employing explicit structural assumptions may provide incrementally clearer evidence. We used data from the US National Longitudinal Study of Adolescent to Adult Health (1995-2009; 1,681 mother-child pairs) to estimate "direct effects" of grandmaternal educational level (less than high school, high school diploma or equivalent, or college degree) at the time of the mother's birth on GBW, adjusted for maternal life-course factors: maltreatment as a child, education and income as an adult, prepregnancy overweight, and prenatal smoking.

Marginal log-linear parameters for graphical Markov models.

Marginal log-linear (MLL) models provide a flexible approach to multivariate discrete data. MLL parametrizations under linear constraints induce a wide variety of models, including models defined by conditional independences. We introduce a subclass of MLL models which correspond to Acyclic Directed Mixed Graphs (ADMGs) under the usual global Markov property.

GENERAL THEORY FOR INTERACTIONS IN SUFFICIENT CAUSE MODELS WITH DICHOTOMOUS EXPOSURES.

The sufficient-component cause framework assumes the existence of sets of sufficient causes that bring about an event. For a binary outcome and an arbitrary number of binary causes any set of potential outcomes can be replicated by positing a set of sufficient causes; typically this representation is not unique. A sufficient cause interaction is said to be present if within all representations there exists a sufficient cause in which two or more particular causes are all present.

Rescuing loading induced bone formation at senescence.

The increasing incidence of osteoporosis worldwide requires anabolic treatments that are safe, effective, and, critically, inexpensive given the prevailing overburdened health care systems. While vigorous skeletal loading is anabolic and holds promise, deficits in mechanotransduction accrued with age markedly diminish the efficacy of readily complied, exercise-based strategies to combat osteoporosis in the elderly. Our approach to explore and counteract these age-related deficits was guided by cellular signaling patterns across hierarchical scales and by the insight that cell responses initiated during transient, rare events hold potential to exert high-fidelity control over temporally and spatially distant tissue adaptation.

Estimating Optimal Dynamic Regimes: Correcting Bias under the Null: [Optimal dynamic regimes: bias correction].

A dynamic regime provides a sequence of treatments that are tailored to patient-specific characteristics and outcomes. In 2004 James Robins proposed g-estimation using structural nested mean models for making inference about the optimal dynamic regime in a multi-interval trial. The method provides clear advantages over traditional parametric approaches.

Resolving Contested Elections: The Limited Power of Post-Vote Vote-Choice Data.

In close elections, the losing side has an incentive to obtain evidence that the election result is incorrect. Sometimes this evidence comes in the form of court testimony from a sample of invalid voters, and this testimony is used to adjust vote totals (Borders v King County, 2005; Belcher v Mayor of Ann Arbor, 1978). However, while courts may be reluctant to make explicit findings about out-of-sample data (e.

Alleviating Linear Ecological Bias and Optimal Design with Sub-sample Data.

In this paper, we illustrate that combining ecological data with subsample data in situations in which a linear model is appropriate provides three main benefits. First, by including the individual level subsample data, the biases associated with linear ecological inference can be eliminated. Second, by supplementing the subsample data with ecological data, the information about parameters will be increased.

Demystifying optimal dynamic treatment regimes.

A dynamic regime is a function that takes treatment and covariate history and baseline covariates as inputs and returns a decision to be made. Murphy (2003, Journal of the Royal Statistical Society, Series B 65, 331-366) and Robins (2004, Proceedings of the Second Seattle Symposium on Biostatistics, 189-326) have proposed models and developed semiparametric methods for making inference about the optimal regime in a multi-interval trial that provide clear advantages over traditional parametric approaches. We show that Murphy's model is a special case of Robins's and that the methods are closely related but not equivalent.

Rest-inserted loading rapidly amplifies the response of bone to small increases in strain and load cycles.

We hypothesized that a 10-s rest interval (at zero load) inserted between each load cycle would increase the osteogenic effects of mechanical loading near previously identified thresholds for strain magnitude and cycle numbers. We tested our hypothesis by subjecting the right tibiae of female C57BL/6J mice (16 wk, n = 70) to exogenous mechanical loading within a peri-threshold physiological range of strain magnitudes and load cycle numbers using a noninvasive murine tibia loading device. Bone responses to mechanical loading were determined via dynamic histomorphometry.