On Modeling and Estimation for the Relative Risk and Risk Difference

A common problem in formulating models for the relative risk and risk difference is the variation dependence between these parameters and the baseline risk, which is a nuisance model. We address this problem by proposing the conditional log odds-product as a preferred nuisance model. This novel nuisance model facilitates maximum-likelihood estimation, but also permits doubly-robust estimation for the parameters of interest. Our approach is illustrated via simulations and a data analysis.Comment: To appear in Journal of the American Statistical Association: Theory and Method

Congenial Causal Inference with Binary Structural Nested Mean Models

Structural nested mean models (SNMMs) are among the fundamental tools for inferring causal effects of time-dependent exposures from longitudinal studies. With binary outcomes, however, current methods for estimating multiplicative and additive SNMM parameters suffer from variation dependence between the causal SNMM parameters and the non-causal nuisance parameters. Estimating methods for logistic SNMMs do not suffer from this dependence. Unfortunately, in contrast with the multiplicative and additive models, unbiased estimation of the causal parameters of a logistic SNMM rely on additional modeling assumptions even when the treatment probabilities are known. These difficulties have hindered the uptake of SNMMs in epidemiological practice, where binary outcomes are common. We solve the variation dependence problem for the binary multiplicative SNMM by a reparametrization of the non-causal nuisance parameters. Our novel nuisance parameters are variation independent of the causal parameters, and hence allows the fitting of a multiplicative SNMM by unconstrained maximum likelihood. It also allows one to construct true (i.e. congenial) doubly robust estimators of the causal parameters. Along the way, we prove that an additive SNMM with binary outcomes does not admit a variation independent parametrization, thus explaining why we restrict ourselves to the multiplicative SNMM

Emerging targets in human lymphoma: targeting the MYD88 mutation

B cell neoplasms co-opt the molecular machinery of normal B cells for their survival. Technological advances in cancer genomics has significantly contributed to uncovering the root cause of aggressive lymphomas, revealing a previously unknown link between TLR signaling and B cell neoplasm. Recurrent oncogenic mutations in MYD88 have been found in 39% of the activated B cell-like subtype of diffuse large B cell lymphoma (ABC DLBCL). Interestingly, 29% of ABC DLBCL have a single amino acid substitution of proline for the leucine at position 265 (L265P), and the exact same variant has also been identified in a number of lymphoid malignancies. The MYD88 L265P variant was recently identified in 90% of Wadenstrom's macroglobulinemia patients. These recent developments warrant the need for novel diagnostic tools as well as targeted therapeutics. In this review, we discuss the physiological functions of MYD88 and focus on its role in B cell lymphomas, evaluating the potential for targeting oncogenic MYD88 in lymphoma

Deep Learning for Galaxy Mergers in the Galaxy Main Sequence

Starburst galaxies are often found to be the result of galaxy mergers. As a result, galaxy mergers are often believed to lie above the galaxy main sequence: the tight correlation between stellar mass and star formation rate. Here, we aim to test this claim. Deep learning techniques are applied to images from the Sloan Digital Sky Survey to provide visual-like classifications for over 340 000 objects between redshifts of 0.005 and 0.1. The aim of this classification is to split the galaxy population into merger and non-merger systems and we are currently achieving an accuracy of 91.5%. Stellar masses and star formation rates are also estimated using panchromatic data for the entire galaxy population. With these preliminary data, the mergers are placed onto the full galaxy main sequence, where we find that merging systems lie across the entire star formation rate - stellar mass plane.Comment: 4 pages, 1 figure. For Proceedings IAU Symposium No. 34

Clustering of Lyman alpha emitters at z ~ 4.5

We present the clustering properties of 151 Lyman alpha emitting galaxies at z ~ 4.5 selected from the Large Area Lyman Alpha (LALA) survey. Our catalog covers an area of 36' x 36' observed with five narrowband filters. We assume that the angular correlation function w(theta) is well represented by a power law A_w = Theta^(-beta) with slope beta = 0.8, and we find A_w = 6.73 +/- 1.80. We then calculate the correlation length r_0 of the real-space two-point correlation function xi(r) = (r/r_0)^(-1.8) from A_w through the Limber transformation, assuming a flat, Lambda-dominated universe. Neglecting contamination, we find r_0 = 3.20 +/- 0.42 Mpc/h. Taking into account a possible 28% contamination by randomly distributed sources, we find r_0 = 4.61 +/- 0.6 Mpc/h. We compare these results with the expectations for the clustering of dark matter halos at this redshift in a Cold Dark Matter model, and find that the measured clustering strength can be reproduced if these objects reside in halos with a minimum mass of 1-2 times 10^11 Solar masses/h. Our estimated correlation length implies a bias of b ~ 3.7, similar to that of Lyman-break galaxies (LBG) at z ~ 3.8-4.9. However, Lyman alpha emitters are a factor of ~ 2-16 rarer than LBGs with a similar bias value and implied host halo mass. Therefore, one plausible scenario seems to be that Lyman alpha emitters occupy host halos of roughly the same mass as LBGs, but shine with a relatively low duty cycle of 6-50%.Comment: 23 pages in preprint format, 4 figures, ApJ accepte

Targeting a therapeutic LIF transgene to muscle via the immune system ameliorates muscular dystrophy.

Many potentially therapeutic molecules have been identified for treating Duchenne muscular dystrophy. However, targeting those molecules only to sites of active pathology is an obstacle to their clinical use. Because dystrophic muscles become extensively inflamed, we tested whether expressing a therapeutic transgene in leukocyte progenitors that invade muscle would provide selective, timely delivery to diseased muscle. We designed a transgene in which leukemia inhibitory factor (LIF) is under control of a leukocyte-specific promoter and transplanted transgenic cells into dystrophic mice. Transplantation diminishes pathology, reduces Th2 cytokines in muscle and biases macrophages away from a CD163+/CD206+ phenotype that promotes fibrosis. Transgenic cells also abrogate TGFβ signaling, reduce fibro/adipogenic progenitor cells and reduce fibrogenesis of muscle cells. These findings indicate that leukocytes expressing a LIF transgene reduce fibrosis by suppressing type 2 immunity and highlight a novel application by which immune cells can be genetically modified as potential therapeutics to treat muscle disease

Modeling and Simulation of the Effects of Cyclic Loading on Articular Cartilage Lesion Formation

We present a model of articular cartilage lesion formation to simulate the effects of cyclic loading. This model extends and modifies the reaction-diffusion-delay model by Graham et al. 2012 for the spread of a lesion formed though a single traumatic event. Our model represents "implicitly" the effects of loading, meaning through a cyclic sink term in the equations for live cells. Our model forms the basis for in silico studies of cartilage damage relevant to questions in osteoarthritis, for example, that may not be easily answered through in vivo or in vitro studies. Computational results are presented that indicate the impact of differing levels of EPO on articular cartilage lesion abatement