25 research outputs found
Estimating treatment importance in multidrug-resistant tuberculosis using Targeted Learning : an observational individual patient data network meta-analysis
Persons with multidrugâresistant tuberculosis (MDRâTB) have a disease resulting from a strain of tuberculosis (TB) that does not respond to at least isoniazid and rifampicin, the two most effective antiâTB drugs. MDRâTB is always treated with multiple antimicrobial agents. Our data consist of individual patient data from 31 international observational studies with varying prescription practices, access to medications, and distributions of antibiotic resistance. In this study, we develop identifiability criteria for the estimation of a global treatment importance metric in the context where not all medications are observed in all studies. With stronger causal assumptions, this treatment importance metric can be interpreted as the effect of adding a medication to the existing treatments. We then use this metric to rank 15 observed antimicrobial agents in terms of their estimated addâon value. Using the concept of transportability, we propose an implementation of targeted maximum likelihood estimation, a doubly robust and locally efficient plugâin estimator, to estimate the treatment importance metric. A clustered sandwich estimator is adopted to compute variance estimates and produce confidence intervals. Simulation studies are conducted to assess the performance of our estimator, verify the double robustness property, and assess the appropriateness of the variance estimation approach
Descents and nodal load in scale-free networks
The load of a node in a network is the total traffic going through it when
every node pair sustains a uniform bidirectional traffic between them on
shortest paths. We show that nodal load can be expressed in terms of the more
elementary notion of a node's descents in breadth-first-search (BFS or
shortest-path) trees, and study both the descent and nodal-load distributions
in the case of scale-free networks. Our treatment is both semi-analytical
(combining a generating-function formalism with simulation-derived BFS
branching probabilities) and computational for the descent distribution; it is
exclusively computational in the case of the load distribution. Our main result
is that the load distribution, even though it can be disguised as a power-law
through subtle (but inappropriate) binning of the raw data, is in fact a
succession of sharply delineated probability peaks, each of which can be
clearly interpreted as a function of the underlying BFS descents. This find is
in stark contrast with previously held belief, based on which a power law of
exponent -2.2 was conjectured to be valid regardless of the exponent of the
power-law distribution of node degrees
Causal graphs for the analysis of genetic cohort data.
The increasing availability of genetic cohort data has led to many genome-wide association studies (GWAS) successfully identifying genetic associations with an ever-expanding list of phenotypic traits. Association, however, does not imply causation, and therefore methods have been developed to study the issue of causality. Under additional assumptions, Mendelian randomization (MR) studies have proved popular in identifying causal effects between two phenotypes, often using GWAS summary statistics. Given the widespread use of these methods, it is more important than ever to understand, and communicate, the causal assumptions upon which they are based, so that methods are transparent, and findings are clinically relevant. Causal graphs can be used to represent causal assumptions graphically and provide insights into the limitations associated with different analysis methods. Here we review GWAS and MR from a causal perspective, to build up intuition for causal diagrams in genetic problems. We also examine issues of confounding by ancestry and comment on approaches for dealing with such confounding, as well as discussing approaches for dealing with selection biases arising from study design