Estimating the causal effect of a time-varying treatment on time-to-event using structural nested failure time models

In this paper we review an approach to estimating the causal effect of a time-varying treatment on time to some event of interest. This approach is designed for the situation where the treatment may have been repeatedly adapted to patient characteristics, which themselves may also be time-dependent. In this situation the effect of the treatment cannot simply be estimated by conditioning on the patient characteristics, as these may themselves be indicators of the treatment effect. This so-called time-dependent confounding is typical in observational studies. We discuss a new class of failure time models, structural nested failure time models, which can be used to estimate the causal effect of a time-varying treatment, and present methods for estimating and testing the parameters of these models

Resampling-based confidence regions and multiple tests for a correlated random vector

We derive non-asymptotic confidence regions for the mean of a random vector whose coordinates have an unknown dependence structure. The random vector is supposed to be either Gaussian or to have a symmetric bounded distribution, and we observe $n$ i.i.d copies of it. The confidence regions are built using a data-dependent threshold based on a weighted bootstrap procedure. We consider two approaches, the first based on a concentration approach and the second on a direct boostrapped quantile approach. The first one allows to deal with a very large class of resampling weights while our results for the second are restricted to Rademacher weights. However, the second method seems more accurate in practice. Our results are motivated by multiple testing problems, and we show on simulations that our procedures are better than the Bonferroni procedure (union bound) as soon as the observed vector has sufficiently correlated coordinates.Comment: submitted to COL

Semiparametric theory and empirical processes in causal inference

In this paper we review important aspects of semiparametric theory and empirical processes that arise in causal inference problems. We begin with a brief introduction to the general problem of causal inference, and go on to discuss estimation and inference for causal effects under semiparametric models, which allow parts of the data-generating process to be unrestricted if they are not of particular interest (i.e., nuisance functions). These models are very useful in causal problems because the outcome process is often complex and difficult to model, and there may only be information available about the treatment process (at best). Semiparametric theory gives a framework for benchmarking efficiency and constructing estimators in such settings. In the second part of the paper we discuss empirical process theory, which provides powerful tools for understanding the asymptotic behavior of semiparametric estimators that depend on flexible nonparametric estimators of nuisance functions. These tools are crucial for incorporating machine learning and other modern methods into causal inference analyses. We conclude by examining related extensions and future directions for work in semiparametric causal inference

Минералогические исследования в пещерной системе Снежная-Меженного-Иллюзия (Западный Кавказ, Бзыбский хребет): предварительные результаты и направления дальнейших работ

В статье приводятся сведения о минеральном составе водных хемогенных и водных механических отложений в пещерной системе Снежная-Меженного-Иллюзия. В состав водных хемогенных отложений входят Mg- и Sr-содержащий кальцит, арагонит, гипс, гидромагнезит, целестин, стронцианит, доломит, гетит, рутил и циркон. Водные механические отложения сложены преимущественно доломитом, кварцем и кальцитом. В схожих по морфологии и микроклимату частях пещерной системы наблюдаются одинаковые вторичные минералы.У статті наводяться відомості про мінеральний склад водних хемогенних і водних механічних відкладень в печерній системі Сніжна-Меженого-Ілюзія. До складу водних хемогенних відкладень входять кальцит, який містить Mg і Sr, арагоніт, гіпс, гідромагнезіт, целестин, стронціаніт, доломіт, гетит, рутил і циркон. Водні механічні відкладення складені переважно доломітом, кварцом і кальцитом. У схожих за морфологєю та мікрокліматом частинах печерної системи спостерігаються однакові вторинні мінерали.The article presents the preliminary characteristic of the mineral composition of chemogenic formations and clastic deposits of Snezhnaya-Mezhennogo-Illusia cave system. Chemogenic formations are composed by Mg- and Sr-calcite, aragonite, gypsum and hydromagnesite, celestite, strontianite, dolomite, goethite, rutile and zircon. Clastic sediments are composed mainly by dolomite, quartz and calcite. Same secondary minerals are observed in those parts of the cave system that have similar morphology and microclimate

Asymptotic Learning Curve and Renormalizable Condition in Statistical Learning Theory

Bayes statistics and statistical physics have the common mathematical structure, where the log likelihood function corresponds to the random Hamiltonian. Recently, it was discovered that the asymptotic learning curves in Bayes estimation are subject to a universal law, even if the log likelihood function can not be approximated by any quadratic form. However, it is left unknown what mathematical property ensures such a universal law. In this paper, we define a renormalizable condition of the statistical estimation problem, and show that, under such a condition, the asymptotic learning curves are ensured to be subject to the universal law, even if the true distribution is unrealizable and singular for a statistical model. Also we study a nonrenormalizable case, in which the learning curves have the different asymptotic behaviors from the universal law

Nonparametric Methods for Doubly Robust Estimation of Continuous Treatment Effects

Continuous treatments (e.g. doses) arise often in practice, but many available causal effect estimators are limited by either requiring parametric models for the effect curve, or by not allowing doubly robust covariate adjustment. We develop a novel kernel smoothing approach that requires only mild smoothness assumptions on the effect curve and still allows for misspecification of either the treatment density or outcome regression. We derive asymptotic properties and give a procedure for data‐driven bandwidth selection. The methods are illustrated via simulation and in a study of the effect of nurse staffing on hospital readmissions penalties

Phosphate and fibroblast growth factor 23 in diabetes

Diabetes is associated with a strongly elevated risk of cardiovascular disease, which is even more pronounced in patients with diabetic nephropathy. Currently available guideline-based efforts to correct traditional risk factors are only partly able to attenuate this risk, underlining the urge to identify novel treatment targets. Emerging data point towards a role for disturbances in phosphate metabolism in diabetes. In this review, we discuss the role of phosphate and the phosphate-regulating hormone fibroblast growth factor 23 (FGF23) in diabetes. We address deregulations of phosphate metabolism in patients with diabetes, including diabetic ketoacidosis. Moreover, we discuss potential adverse consequences of these deregulations, including the role of deregulated phosphate and glucose as drivers of vascular calcification propensity. Finally, we highlight potential treatment options to correct abnormalities in phosphate and FGF23. While further studies are needed to more precisely assess their clinical impact, deregulations in phosphate and FGF23 are promising potential target in diabetes and diabetic nephropathy

Hysterectomy Does Not Cause Constipation

PURPOSE: This study was designed to evaluate the risk on development and persistence of constipation after hysterectomy. METHODS: We conducted a prospective, observational, multicenter study with three-year follow-up in 13 teaching and nonteaching hospitals in the Netherlands. A total of 413 females who underwent hysterectomy for benign disease other than symptomatic uterine prolapse were included. All patients underwent vaginal hysterectomy, subtotal abdominal hysterectomy, or total abdominal hysterectomy. A validated disease-specific quality-of-life questionnaire was completed before and three years after surgery to assess the presence of constipation. RESULTS: Of the 413 included patients, 344 (83 percent) responded at three-year follow-up. Constipation had developed in 7 of 309 patients (2 percent) without constipation before surgery and persisted in 16 of 35 patients (46 percent) with constipation before surgery. Preservation of the cervix seemed to be associated with an increased risk of the development of constipation (relative risk, 6.6; 95 percent confidence interval, 1.3-33.3; P=0.02). Statistically significant risk factors for the persistence of constipation could not be identified. CONCLUSIONS: Hysterectomy does not seem to cause constipation. In nearly half of the patients reporting constipation before hysterectomy, this symptom will disappear

Non parametric estimation of the structural expectation of a stochastic increasing function

International audienceThis article introduces a non parametric warping model for functional data. When the outcome of an experiment is a sample of curves, data can be seen as realizations of a stochastic process, which takes into account the variations between the different observed curves. The aim of this work is to define a mean pattern which represents the main behaviour of the set of all the realizations. So, we define the structural expectation of the underlying stochastic function. Then, we provide empirical estimators of this structural expectation and of each individual warping function. Consistency and asymptotic normality for such estimators are proved