Reducing bias through directed acyclic graphs

<p>Abstract</p> <p>Background</p> <p>The objective of most biomedical research is to determine an unbiased estimate of effect for an exposure on an outcome, i.e. to make causal inferences about the exposure. Recent developments in epidemiology have shown that traditional methods of identifying confounding and adjusting for confounding may be inadequate.</p> <p>Discussion</p> <p>The traditional methods of adjusting for "potential confounders" may introduce conditional associations and bias rather than minimize it. Although previous published articles have discussed the role of the causal directed acyclic graph approach (DAGs) with respect to confounding, many clinical problems require complicated DAGs and therefore investigators may continue to use traditional practices because they do not have the tools necessary to properly use the DAG approach. The purpose of this manuscript is to demonstrate a simple 6-step approach to the use of DAGs, and also to explain why the method works from a conceptual point of view.</p> <p>Summary</p> <p>Using the simple 6-step DAG approach to confounding and selection bias discussed is likely to reduce the degree of bias for the effect estimate in the chosen statistical model.</p

Lithium distribution across the membrane of motoneurons in the isolated frog spinal cord

Lithium sensitive microelectrodes were used to investigate the transmembrane distribution of lithium ions (Li+) in motoneurons of the isolated frog spinal cord. After addition of 5 mmol·l–1 LiCl to the bathing solution the extracellular diffusion of Li+ was measured. At a depth of 500 m, about 60 min elapsed before the extracellular Li+ concentration approached that of the bathing solution. Intracellular measurements revealed that Li+ started to enter the cells soon after reaching the motoneuron pool and after up to 120 min superfusion, an intra — to extracellular concentration ratio of about 0.7 was obtained. The resting membrane potential and height of antidromically evoked action potentials were not altered by 5 mmol·l–1 Li+

Sexualized drug use ("chemsex') and high-risk sexual behaviours in HIV-positive men who have sex with men

Objectives The incidence of sexually transmitted infections (STI s) and HIV infection remains high in gay, bisexual, and other men who have sex with men (MSM ) in the UK , and sexualized drug use (“chemsex”) and injecting drug use (“slamsex”) may play a part in this. We aimed to characterize HIV ‐positive MSM engaging in chemsex/slamsex and to assess the associations with self‐reported STI diagnoses and sexual behaviours. Methods Data from a 2014 survey of people attending HIV clinics in England and Wales were linked to clinical data from national HIV surveillance records and weighted to be nationally representative. Multivariable logistic regression assessed the associations of chemsex and slamsex with self‐reported unprotected anal intercourse (UAI ), serodiscordant UAI (sdUAI ) (i.e. UAI with an HIV ‐negative or unknown HIV status partner), sdUAI with a detectable viral load (>50 HIV ‐1 RNA copies/mL ), hepatitis C, and bacterial STI s. Results In the previous year, 29.5% of 392 sexually active participants engaged in chemsex, and 10.1% in slamsex. Chemsex was significantly associated with increased odds of UAI [adjusted odds ratio (AOR ) 5.73; P < 0.001], sdUAI (AOR 2.34; P < 0.05), sdUAI with a detectable viral load (AOR 3.86; P < 0.01), hepatitis C (AOR 6.58; P < 0.01), and bacterial STI diagnosis (AOR 2.65; P < 0.01). Slamsex was associated with increased odds of UAI (AOR 6.11; P < 0.05), hepatitis C (AOR 9.39; P < 0.001), and bacterial STI diagnosis (AOR 6.11; P < 0.001). Conclusions Three in ten sexually active HIV ‐positive MSM engaged in chemsex in the past year, which was positively associated with self‐reported depression/anxiety, smoking, nonsexual drug use, risky sexual behaviours, STI s, and hepatitis C. Chemsex may therefore play a role in the ongoing HIV and STI epidemics in the UK

Analysing causal structures with entropy

A central question for causal inference is to decide whether a set of correlations fit a given causal structure. In general, this decision problem is computationally infeasible and hence several approaches have emerged that look for certificates of compatibility. Here we review several such approaches based on entropy. We bring together the key aspects of these entropic techniques with unified terminology, filling several gaps and establishing new connections regarding their relation, all illustrated with examples. We consider cases where unobserved causes are classical, quantum and post-quantum and discuss what entropic analyses tell us about the difference. This has applications to quantum cryptography, where it can be crucial to eliminate the possibility of classical causes. We discuss the achievements and limitations of the entropic approach in comparison to other techniques and point out the main open problems.Comment: 19 (+3) pages, 5 (+1) figures. A few minor updates and corrections. There is a small error in the published version of this manuscript: the claim in the last sentence of Section 2(a)(ii) should be restricted to four variables. This is correct in the arXiv versio

Graphical Markov models, unifying results and their interpretation

Graphical Markov models combine conditional independence constraints with graphical representations of stepwise data generating processes.The models started to be formulated about 40 years ago and vigorous development is ongoing. Longitudinal observational studies as well as intervention studies are best modeled via a subclass called regression graph models and, especially traceable regressions. Regression graphs include two types of undirected graph and directed acyclic graphs in ordered sequences of joint responses. Response components may correspond to discrete or continuous random variables and may depend exclusively on variables which have been generated earlier. These aspects are essential when causal hypothesis are the motivation for the planning of empirical studies. To turn the graphs into useful tools for tracing developmental pathways and for predicting structure in alternative models, the generated distributions have to mimic some properties of joint Gaussian distributions. Here, relevant results concerning these aspects are spelled out and illustrated by examples. With regression graph models, it becomes feasible, for the first time, to derive structural effects of (1) ignoring some of the variables, of (2) selecting subpopulations via fixed levels of some other variables or of (3) changing the order in which the variables might get generated. Thus, the most important future applications of these models will aim at the best possible integration of knowledge from related studies.Comment: 34 Pages, 11 figures, 1 tabl

Understanding human functioning using graphical models

<p>Abstract</p> <p>Background</p> <p>Functioning and disability are universal human experiences. However, our current understanding of functioning from a comprehensive perspective is limited. The development of the International Classification of Functioning, Disability and Health (ICF) on the one hand and recent developments in graphical modeling on the other hand might be combined and open the door to a more comprehensive understanding of human functioning. The objective of our paper therefore is to explore how graphical models can be used in the study of ICF data for a range of applications.</p> <p>Methods</p> <p>We show the applicability of graphical models on ICF data for different tasks: Visualization of the dependence structure of the data set, dimension reduction and comparison of subpopulations. Moreover, we further developed and applied recent findings in causal inference using graphical models to estimate bounds on intervention effects in an observational study with many variables and without knowing the underlying causal structure.</p> <p>Results</p> <p>In each field, graphical models could be applied giving results of high face-validity. In particular, graphical models could be used for visualization of functioning in patients with spinal cord injury. The resulting graph consisted of several connected components which can be used for dimension reduction. Moreover, we found that the differences in the dependence structures between subpopulations were relevant and could be systematically analyzed using graphical models. Finally, when estimating bounds on causal effects of ICF categories on general health perceptions among patients with chronic health conditions, we found that the five ICF categories that showed the strongest effect were plausible.</p> <p>Conclusions</p> <p>Graphical Models are a flexible tool and lend themselves for a wide range of applications. In particular, studies involving ICF data seem to be suited for analysis using graphical models.</p

A hidden two-locus disease association pattern in genome-wide association studies

<p>Abstract</p> <p>Background</p> <p>Recent association analyses in genome-wide association studies (GWAS) mainly focus on single-locus association tests (marginal tests) and two-locus interaction detections. These analysis methods have provided strong evidence of associations between genetics variances and complex diseases. However, there exists a type of association pattern, which often occurs within local regions in the genome and is unlikely to be detected by either marginal tests or interaction tests. This association pattern involves a group of correlated single-nucleotide polymorphisms (SNPs). The correlation among SNPs can lead to weak marginal effects and the interaction does not play a role in this association pattern. This phenomenon is due to the existence of unfaithfulness: the marginal effects of correlated SNPs do not express their significant joint effects faithfully due to the correlation cancelation.</p> <p>Results</p> <p>In this paper, we develop a computational method to detect this association pattern masked by unfaithfulness. We have applied our method to analyze seven data sets from the Wellcome Trust Case Control Consortium (WTCCC). The analysis for each data set takes about one week to finish the examination of all pairs of SNPs. Based on the empirical result of these real data, we show that this type of association masked by unfaithfulness widely exists in GWAS.</p> <p>Conclusions</p> <p>These newly identified associations enrich the discoveries of GWAS, which may provide new insights both in the analysis of tagSNPs and in the experiment design of GWAS. Since these associations may be easily missed by existing analysis tools, we can only connect some of them to publicly available findings from other association studies. As independent data set is limited at this moment, we also have difficulties to replicate these findings. More biological implications need further investigation.</p> <p>Availability</p> <p>The software is freely available at <url>http://bioinformatics.ust.hk/hidden_pattern_finder.zip</url>.</p

The Doctrine of Specific Etiology

Modern medicine is often said to have originated with nineteenth century germ theory, which attributed diseases to bacterial contagions. The success of this theory is often associated with an underlying principle referred to as the “doctrine of specific etiology.” This doctrine refers to specificity at the level of disease causation or etiology. While the importance of this doctrine is frequently emphasized in the philosophical, historical, and medical literature, these sources lack a clear account of the types of specificity that it involves and why exactly they matter. This paper argues that the nineteenth century germ theory model involves two types of specificity at the level of etiology. One type receives significant attention in the literature, but its influence on modern medicine has been misunderstood. A second type is present in this model, but it has been completely overlooked in the extant literature. My analysis clarifies how these types of specificity led to a novel conception of etiology that continues to figure in medicine today