Identifying the consequences of dynamic treatment strategies: A decision-theoretic overview

We consider the problem of learning about and comparing the consequences of dynamic treatment strategies on the basis of observational data. We formulate this within a probabilistic decision-theoretic framework. Our approach is compared with related work by Robins and others: in particular, we show how Robins's 'G-computation' algorithm arises naturally from this decision-theoretic perspective. Careful attention is paid to the mathematical and substantive conditions required to justify the use of this formula. These conditions revolve around a property we term stability, which relates the probabilistic behaviours of observational and interventional regimes. We show how an assumption of 'sequential randomization' (or 'no unmeasured confounders'), or an alternative assumption of 'sequential irrelevance', can be used to infer stability. Probabilistic influence diagrams are used to simplify manipulations, and their power and limitations are discussed. We compare our approach with alternative formulations based on causal DAGs or potential response models. We aim to show that formulating the problem of assessing dynamic treatment strategies as a problem of decision analysis brings clarity, simplicity and generality.Comment: 49 pages, 15 figure

Assumptions of IV Methods for Observational Epidemiology

Instrumental variable (IV) methods are becoming increasingly popular as they seem to offer the only viable way to overcome the problem of unobserved confounding in observational studies. However, some attention has to be paid to the details, as not all such methods target the same causal parameters and some rely on more restrictive parametric assumptions than others. We therefore discuss and contrast the most common IV approaches with relevance to typical applications in observational epidemiology. Further, we illustrate and compare the asymptotic bias of these IV estimators when underlying assumptions are violated in a numerical study. One of our conclusions is that all IV methods encounter problems in the presence of effect modification by unobserved confounders. Since this can never be ruled out for sure, we recommend that practical applications of IV estimators be accompanied routinely by a sensitivity analysis.Comment: Published in at http://dx.doi.org/10.1214/09-STS316 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Bayesian weak lensing tomography: Reconstructing the 3D large-scale distribution of matter with a lognormal prior

We present a Bayesian reconstruction algorithm that infers the three-dimensional large-scale matter distribution from the weak gravitational lensing effects measured in the image shapes of galaxies. The algorithm is designed to also work with non-Gaussian posterior distributions which arise, for example, from a non-Gaussian prior distribution. In this work, we use a lognormal prior and compare the reconstruction results to a Gaussian prior in a suite of increasingly realistic tests on mock data. We find that in cases of high noise levels (i.e. for low source galaxy densities and/or high shape measurement uncertainties), both normal and lognormal priors lead to reconstructions of comparable quality, but with the lognormal reconstruction being prone to mass-sheet degeneracy. In the low-noise regime and on small scales, the lognormal model produces better reconstructions than the normal model: The lognormal model 1) enforces non-negative densities, while negative densities are present when a normal prior is employed, 2) better traces the extremal values and the skewness of the true underlying distribution, and 3) yields a higher pixel-wise correlation between the reconstruction and the true density.Comment: 23 pages, 12 figures; updated to match version accepted for publication in PR

Characteristics of direct human impacts on the rivers Karun and Dez in lowland south-west Iran and their interactions with earth surface movements

Two of the primary external factors influencing the variability of major river systems, over river reach scales, are human activities and tectonics. Based on the rivers Karun and Dez in south-west Iran, this paper presents an analysis of the geomorphological responses of these major rivers to ancient human modifications and tectonics. Direct human modifications can be distinguished by both modern constructions and ancient remnants of former constructions that can leave a subtle legacy in a suite of river characteristics. For example, the ruins of major dams are characterised by a legacy of channel widening to 100's up to c. 1000 m within upstream zones that can stretch to channel distances of many kilometres upstream of former dam sites, whilst the legacy of major, ancient, anthropogenic river channel straightening can also be distinguished by very low channel sinuosities over long lengths of the river course. Tectonic movements in the region are mainly associated with young and emerging folds with NW–SE and N–S trends and with a long structural lineament oriented E–W. These earth surface movements can be shown to interact with both modern and ancient human impacts over similar timescales, with the types of modification and earth surface motion being distinguishable. This paper examines the geomorphological evidence and outlines the processes involved in the evolution of these interactions through time. The analysis shows how interactions between earth surface movements and major dams are slight, especially after ancient dam collapse. By contrast, interactions between earth surface movements and major anthropogenic river channel straightening are shown to be a key factor in the persistence of long, near-straight river courses. Additionally, it is suggested that artificial river development, with very limited river channel lateral migration, may promote incision across an active fold at unusually long distances from the fold “core” and may promote markedly increased sinuosity across a structural lineament

Afatinib use in recurrent epithelial ovarian carcinoma.

•Genomic tumor testing is an important tool in guiding treatment for gynecologic malignancies.•Targetable mutations may lead to new therapies in gynecologic cancer treatment.•Her2/neu mutations in serous ovarian carcinomas can be targeted with ERBB2 inhibitors.•Afatinib shows promising response rates in lung cancers carrying Her2/neu mutations.•Afatinib may be effective in serous ovarian tumors exhibiting Her2/neu or ERBB2 mutations