Revisiting the g-null paradox

The parametric g-formula is an approach to estimating causal effects of sustained treatment strategies from observational data. An often cited limitation of the parametric g-formula is the g-null paradox: a phenomenon in which model misspecification in the parametric g-formula is guaranteed under the conditions that motivate its use (i.e., when identifiability conditions hold and measured time-varying confounders are affected by past treatment). Many users of the parametric g-formula know they must acknowledge the g-null paradox as a limitation when reporting results but still require clarity on its meaning and implications. Here we revisit the g-null paradox to clarify its role in causal inference studies. In doing so, we present analytic examples and a simulation-based illustration of the bias of parametric g-formula estimates under the conditions associated with this paradox. Our results highlight the importance of avoiding overly parsimonious models for the components of the g-formula when using this method

Regression Analysis of a Disease Onset Distribution Using Diagnosis Data

We consider methods for estimating the effect of a covariate on a disease onset distribution when the observed data structure consists of right-censored data on diagnosis times and current status data on onset times amongst individuals who have not yet been diagnosed. Dunson and Baird (2001) approached this problem using maximum likelihood, under the assumption that the ratio of the diagnosis and onset distributions is monotonic non-decreasing. As an alternative, we propose a two-step estimator, an extension of the approach of van der Laan, Jewell and Petersen (1997) in the single sample setting, that is computationally much simpler and requires no assumptions on this ratio. A simulation study is performed comparing estimates obtained from these two approaches, as well as that from a standard current status analysis that ignores diagnosis data. Results indicate that the Dunson and Baird estimator outperforms the two-step estimator when the monotonicity assumption holds, but the reverse is true when the assumption fails. The simple current status estimator loses only a small amount of precision in comparison to the two-step procedure but requires monitoring time information for all individuals. In the data that motivated this work, a study of uterine fibroids and chemical exposure to dioxin, the monotonicity assumption is seen to fail. Here, the two-step and current status estimators both show no significant association between the level of dioxin exposure and the hazard for onset of uterine fibroids; the two-step estimator of the relative hazard associated with increasing levels of exposure has the least estimated variance amongst the three estimators considered

Variance Estimation in Inverse Probability Weighted Cox Models

Inverse probability weighted Cox models can be used to estimate marginal hazard ratios under different treatments interventions in observational studies. To obtain variance estimates, the robust sandwich variance estimator is often recommended to account for the induced correlation among weighted observations. However, this estimator does not incorporate the uncertainty in estimating the weights and tends to overestimate the variance, leading to inefficient inference. Here we propose a new variance estimator that combines the estimation procedures for the hazard ratio and weights using stacked estimating equations, with additional adjustments for the sum of non-independent and identically distributed terms in a Cox partial likelihood score equation. We prove analytically that the robust sandwich variance estimator is conservative and establish the asymptotic equivalence between the proposed variance estimator and one obtained through linearization by Hajage et al., 2018. In addition, we extend our proposed variance estimator to accommodate clustered data. We compare the finite sample performance of the proposed method with alternative methods through simulation studies. We illustrate these different variance methods in an inverse probability weighted application to estimate the marginal hazard ratio for postoperative hospitalization under sleeve gastrectomy versus Roux-en-Y gastric bypass in a large medical claims and billing database. To facilitate implementation of the proposed method, we have developed an R package ipwCoxCSV

Mediation analysis for a survival outcome with time-varying exposures, mediators, and confounders

We propose an approach to conduct mediation analysis for survival data with time-varying exposures, mediators, and confounders. We identify certain interventional direct and indirect effects through a survival mediational g-formula and describe the required assumptions. We also provide a feasible parametric approach along with an algorithm and software to estimate these effects. We apply this method to analyze the Framingham Heart Study data to investigate the causal mechanism of smoking on mortality through coronary artery disease. The risk ratio of smoking 30 cigarettes per day for ten years compared with no smoking on mortality is 2.34 (95 % CI = (1.44, 3.70)). Of the overall effect, 7.91% (95% CI: = 1.36%, 19.32%) is mediated by coronary artery disease. The survival mediational g-formula constitutes a powerful tool for conducting mediation analysis with longitudinal data

Occurrence of steroid sex hormones in the Cache la Poudre River: and pathways for the removal in the environment

November 2009.Includes bibliographical references.Some chemicals have the apparent ability to disrupt normal endocrine system functions after exposure to concentrations so small that they are difficult to detect in the environment. In recent years, these so-called "endocrine disruptors" have become the subject of intensive scientific research. In wildlife, most of the evidence for endocrine disruption has come from studies on species living in, or closely associated with, aquatic environments. Reported effects of endocrine disruption include abnormal blood hormone levels, masculinization of females, feminization of males, altered sex ratios, intersexuality, and reduced fertility and fecundity. Among suspected endocrine disruptors, exogenous steroid sex hormones generally have the highest potencies for disrupting normal steroid sex hormone functions. In a national reconnaissance study conducted by the U.S. Geological Survey (USGS) from 1999 to 2000, steroid sex hormones were detected at varying concentrations and frequencies in water samples from 139 stream sites located in 30 states. Other studies have detected steroid sex hormones in surface waters throughout the world, including Colorado. Potential sources of steroid sex hormones in the environment include sewage treatment plants, septic systems, animal feeding operations, rangeland grazing, paper mills, aquaculture, and agricultural operations where manure and biosolids are applied as fertilizers. The objectives of this study were to investigate the presence of steroid sex hormones in northern Colorado's Cache la Poudre River, to determine the potential for steroid sex hormone biodegradation and photodegradation under natural conditions, and to characterize the mobility of selected steroid sex hormones in agricultural fields using a rainfall simulator. The study determined that steroid sex hormones are present in the Cache la Poudre River, at concentrations ranging from 0.6 ng L−1 (epitestosterone) to 22.6 ng L−1 (estrone). The study also determined that testosterone, progesterone, and 17β-estradiol can be degraded by manure-borne bacteria, and that testosterone degradation is faster under aerobic conditions and at higher temperatures (i.e., 37°C vs. 22°C), but little affected by changes in pH (from 6 to 7.5) or glucose amendments. In ultraviolet light λ > 340 nm, the study observed direct photodegradation of testosterone and progesterone, and indirect photodegradation of testosterone and 17β-estradiol in the presence of Elliot soil humic acid. On the other hand, in ultraviolet light λ > 310 nm, direct photodegradation of androstenedione was substantially faster than direct photodegradation of testosterone in ultraviolet light λ > 310 nm, and no indirect photodegradation observed. The study detected and identified three testosterone biodegradation products (dehydrotestosterone, androstenedione, and androstadienedione), and detected several products of testosterone and androstenedione photodegradation which appear to retain their steroid structure, and possibly their endocrine disrupting potential. Finally, the study generally observed that androgen runoff concentrations follow runoff rates and decrease after successive rainfall events, while runoff concentrations of other analytes (e.g., estrone) peak after the maximum runoff rate and first rainfall event. Sample and data analysis from the study are continuing, and comprehensive finding and recommendations are expected after the date of this report.Financed in part by the U.S. Department of the Interior, Geological Survey, through the Colorado Water Institute

Translating Liaison Librarians to the Scientific Community

Objective: This study explores the root causes that undermine successful collaborations between scientists and their library liaisons to improve outreach to this population. Methods: This paper uses the Five Whys Technique to explore the reasons why many scientists are unaware of the breadth of services offered by liaison librarians. Existing outreach strategies that address these obstacles are interpreted through the lens of implementation science theories and process models, including Normalization Process Theory. Results: A total of four recommendations—two for liaison librarians and two for libraries as institutions—are provided to enhance the perceived value of liaison services. The recommendations for individuals include aiming to understand scientists’ needs more comprehensively and actively increasing the visibility of services that respond to those needs. Those for libraries focus on cross-functional teams and new forms of assessment. Conclusions: These recommendations emphasize the benefits of collaboration to liaisons, to library programs at large, and to the faculty that liaisons serve. Implementation science can help librarians to understand why certain outreach strategies bring success, and how new services can be implemented more effectively

Considering Questions Before Methods in Dementia Research With Competing Events and Causal Goals

Studying causal exposure effects on dementia is challenging when death is a competing event. Researchers often interpret death as a potential source of bias, although bias cannot be defined or assessed if the causal question is not explicitly specified. Here we discuss 2 possible notions of a causal effect on dementia risk: the “controlled direct effect” and the “total effect.” We provide definitions and discuss the “censoring” assumptions needed for identification in either case and their link to familiar statistical methods. We illustrate concepts in a hypothetical randomized trial on smoking cessation in late midlife, and emulate such a trial using observational data from the Rotterdam Study, the Netherlands, 1990–2015. We estimated a total effect of smoking cessation (compared with continued smoking) on 20-year dementia risk of 2.1 (95% confidence interval: −0.1, 4.2) percentage points and a controlled direct effect of smoking cessation on 20-year dementia risk had death been prevented of −2.7 (95% confidence interval: −6.1, 0.8) percentage points. Our study highlights how analyses corresponding to different causal questions can have different results, here with point estimates on opposite sides of the null. Having a clear causal question in view of the competing event and transparent and explicit assumptions are essential to interpreting results and potential bias.</p