Causal inference for continuous-time processes when covariates are observed only at discrete times

Most of the work on the structural nested model and g-estimation for causal inference in longitudinal data assumes a discrete-time underlying data generating process. However, in some observational studies, it is more reasonable to assume that the data are generated from a continuous-time process and are only observable at discrete time points. When these circumstances arise, the sequential randomization assumption in the observed discrete-time data, which is essential in justifying discrete-time g-estimation, may not be reasonable. Under a deterministic model, we discuss other useful assumptions that guarantee the consistency of discrete-time g-estimation. In more general cases, when those assumptions are violated, we propose a controlling-the-future method that performs at least as well as g-estimation in most scenarios and which provides consistent estimation in some cases where g-estimation is severely inconsistent. We apply the methods discussed in this paper to simulated data, as well as to a data set collected following a massive flood in Bangladesh, estimating the effect of diarrhea on children's height. Results from different methods are compared in both simulation and the real application.Comment: Published in at http://dx.doi.org/10.1214/10-AOS830 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Detecting periodicity in experimental data using linear modeling techniques

Fourier spectral estimates and, to a lesser extent, the autocorrelation function are the primary tools to detect periodicities in experimental data in the physical and biological sciences. We propose a new method which is more reliable than traditional techniques, and is able to make clear identification of periodic behavior when traditional techniques do not. This technique is based on an information theoretic reduction of linear (autoregressive) models so that only the essential features of an autoregressive model are retained. These models we call reduced autoregressive models (RARM). The essential features of reduced autoregressive models include any periodicity present in the data. We provide theoretical and numerical evidence from both experimental and artificial data, to demonstrate that this technique will reliably detect periodicities if and only if they are present in the data. There are strong information theoretic arguments to support the statement that RARM detects periodicities if they are present. Surrogate data techniques are used to ensure the converse. Furthermore, our calculations demonstrate that RARM is more robust, more accurate, and more sensitive, than traditional spectral techniques.Comment: 10 pages (revtex) and 6 figures. To appear in Phys Rev E. Modified styl

Surrogate-assisted network analysis of nonlinear time series

The performance of recurrence networks and symbolic networks to detect weak nonlinearities in time series is compared to the nonlinear prediction error. For the synthetic data of the Lorenz system, the network measures show a comparable performance. In the case of relatively short and noisy real-world data from active galactic nuclei, the nonlinear prediction error yields more robust results than the network measures. The tests are based on surrogate data sets. The correlations in the Fourier phases of data sets from some surrogate generating algorithms are also examined. The phase correlations are shown to have an impact on the performance of the tests for nonlinearity.Comment: 9 pages, 5 figures, Chaos (http://scitation.aip.org/content/aip/journal/chaos), corrected typo

Modulation of Thermoelectric Power of Individual Carbon Nanotubes

Thermoelectric power (TEP) of individual single walled carbon nanotubes (SWNTs) has been measured at mesoscopic scales using a microfabricated heater and thermometers. Gate electric field dependent TEP-modulation has been observed. The measured TEP of SWNTs is well correlated to the electrical conductance across the SWNT according to the Mott formula. At low temperatures, strong modulations of TEP were observed in the single electron conduction limit. In addition, semiconducting SWNTs exhibit large values of TEP due to the Schottky barriers at SWNT-metal junctions.Comment: to be published in Phys. Rev. Let

Results of an aqueous source term model for a radiological risk assessment of the Drigg LLW Site, U.K.

A radionuclide source term model has been developed which simulates the biogeochemical evolution of the Drigg low level waste (LLW) disposal site. The DRINK (DRIgg Near field Kinetic) model provides data regarding radionuclide concentrations in groundwater over a period of 100,000 years, which are used as input to assessment calculations for a groundwater pathway. The DRINK model also provides input to human intrusion and gaseous assessment calculations through simulation of the solid radionuclide inventory. These calculations are being used to support the Drigg post closure safety case. The DRINK model considers the coupled interaction of the effects of fluid flow, microbiology, corrosion, chemical reaction, sorption and radioactive decay. It represents the first direct use of a mechanistic reaction-transport model in risk assessment calculations

Optimal Restricted Estimation for More Efficient Longitudinal Causal Inference

Efficient semiparametric estimation of longitudinal causal effects is often analytically or computationally intractable. We propose a novel restricted estimation approach for increasing efficiency, which can be used with other techniques, is straightforward to implement, and requires no additional modeling assumptions

Electric Field Modulation of Galvanomagnetic Properties of Mesoscopic Graphite

Electric field effect devices based on mesoscopic graphite are fabricated for galvanomagnetic measurements. Strong modulation of magneto-resistance and Hall resistance as a function of gate voltage is observed as sample thickness approaches the screening length. Electric field dependent Landau level formation is detected from Shubnikov de Haas oscillations in magneto-resistance. The effective mass of electron and hole carriers has been measured from the temperature dependant behavior of these oscillations.Comment: 4 pages, 4 figures included, submitted to Phys. Rev. Let

A haplome alignment and reference sequence of the highly polymorphic Ciona savignyi genome

The high degree of polymorphism in the genome of the sea squirt Ciona savignyi complicated the assembly of sequence contigs, but a new alignment method results in a much improved sequence

Elastic energy of polyhedral bilayer vesicles

In recent experiments [M. Dubois, B. Dem\'e, T. Gulik-Krzywicki, J.-C. Dedieu, C. Vautrin, S. D\'esert, E. Perez, and T. Zemb, Nature (London) Vol. 411, 672 (2001)] the spontaneous formation of hollow bilayer vesicles with polyhedral symmetry has been observed. On the basis of the experimental phenomenology it was suggested [M. Dubois, V. Lizunov, A. Meister, T. Gulik-Krzywicki, J. M. Verbavatz, E. Perez, J. Zimmerberg, and T. Zemb, Proc. Natl. Acad. Sci. U.S.A. Vol. 101, 15082 (2004)] that the mechanism for the formation of bilayer polyhedra is minimization of elastic bending energy. Motivated by these experiments, we study the elastic bending energy of polyhedral bilayer vesicles. In agreement with experiments, and provided that excess amphiphiles exhibiting spontaneous curvature are present in sufficient quantity, we find that polyhedral bilayer vesicles can indeed be energetically favorable compared to spherical bilayer vesicles. Consistent with experimental observations we also find that the bending energy associated with the vertices of bilayer polyhedra can be locally reduced through the formation of pores. However, the stabilization of polyhedral bilayer vesicles over spherical bilayer vesicles relies crucially on molecular segregation of excess amphiphiles along the ridges rather than the vertices of bilayer polyhedra. Furthermore, our analysis implies that, contrary to what has been suggested on the basis of experiments, the icosahedron does not minimize elastic bending energy among arbitrary polyhedral shapes and sizes. Instead, we find that, for large polyhedron sizes, the snub dodecahedron and the snub cube both have lower total bending energies than the icosahedron

Using Case Description Information to Reduce Sensitivity to Bias for the Attributable Fraction Among the Exposed

The attributable fraction among the exposed (\textbf{AF}$_e$), also known as the attributable risk or excess fraction among the exposed, is the proportion of disease cases among the exposed that could be avoided by eliminating the exposure. Understanding the \textbf{AF}$_e$ for different exposures helps guide public health interventions. The conventional approach to inference for the \textbf{AF}$_e$ assumes no unmeasured confounding and could be sensitive to hidden bias from unobserved covariates. In this paper, we propose a new approach to reduce sensitivity to hidden bias for conducting statistical inference on the \textbf{AF}$_e$ by leveraging case description information. Case description information is information that describes the case, e.g., the subtype of cancer. The exposure may have more of an effect on some types of cases than other types. We explore how leveraging case description information can reduce sensitivity to bias from unmeasured confounding through an asymptotic tool, design sensitivity, and simulation studies. We allow for the possibility that leveraging case definition information may introduce additional selection bias through an additional sensitivity parameter. The proposed methodology is illustrated by re-examining alcohol consumption and the risk of postmenopausal invasive breast cancer using case description information on the subtype of cancer (hormone-sensitive or insensitive) using data from the Women's Health Initiative (WHI) Observational Study (OS).Comment: 30 pages, 8 tables, 1 figur