Estimation of causal effects using instrumental variables with nonignorable missing covariates: Application to effect of type of delivery NICU on premature infants

Understanding how effective high-level NICUs (neonatal intensive care units that have the capacity for sustained mechanical assisted ventilation and high volume) are compared to low-level NICUs is important and valuable for both individual mothers and for public policy decisions. The goal of this paper is to estimate the effect on mortality of premature babies being delivered in a high-level NICU vs. a low-level NICU through an observational study where there are unmeasured confounders as well as nonignorable missing covariates. We consider the use of excess travel time as an instrumental variable (IV) to control for unmeasured confounders. In order for an IV to be valid, we must condition on confounders of the IV---outcome relationship, for example, month prenatal care started must be conditioned on for excess travel time to be a valid IV. However, sometimes month prenatal care started is missing, and the missingness may be nonignorable because it is related to the not fully measured mother's/infant's risk of complications. We develop a method to estimate the causal effect of a treatment using an IV when there are nonignorable missing covariates as in our data, where we allow the missingness to depend on the fully observed outcome as well as the partially observed compliance class, which is a proxy for the unmeasured risk of complications. A simulation study shows that under our nonignorable missingness assumption, the commonly used estimation methods, complete-case analysis and multiple imputation by chained equations assuming missingness at random, provide biased estimates, while our method provides approximately unbiased estimates. We apply our method to the NICU study and find evidence that high-level NICUs significantly reduce deaths for babies of small gestational age, whereas for almost mature babies like 37 weeks, the level of NICUs makes little difference. A sensitivity analysis is conducted to assess the sensitivity of our conclusions to key assumptions about the missing covariates. The method we develop in this paper may be useful for many observational studies facing similar issues of unmeasured confounders and nonignorable missing data as ours.Comment: Published in at http://dx.doi.org/10.1214/13-AOAS699 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

MicroRNA regulation of prostate cancer desensitization to androgen receptor antagonist drugs during androgen deprivation therapy

The current standard treatment of prostate cancer by androgen deprivation therapy involves using drugs such as bicalutamide (Casodex) to antagonistically block androgen receptors that are normally present within prostate cells. Usually, the therapy is successful in the short run at limiting the growth of prostate cancer. However, in virtually all cases tumors begin to grow aggressively again after several months of treatment and new therapies must be started. The mechanism by which these prostate cells transform from androgen sensitive to androgen independent and anti-androgen resistant is unclear. In this study, we investigated the role of microRNAs, small 15 to 18 nucleotide regulatory RNAs, in regulating the desensitization of prostate cancer cells to the androgen receptor antagonist drug bicalutamide. In order to identify significant microRNAs, quantitative PCR was used to obtain genome-wide microRNA expression levels of 885 human microRNAs at different timepoints for androgen sensitive LNCaP cancer cells treated with bicalutamide and for untreated control cells in tissue culture. Analysis of microRNA expression by clustering analysis and by statistical comparisons of treatment groups resulted in identification of 28 microRNAs that have altered expression in the progression process. In silico target prediction analysis was performed with the microRNAs shown to have altered expression, and a group of genes predicted to be under microRNA regulatory control during cancer progression to resistance was identified. A microRNA expression profile can be useful in developing more effective prognostic and therapeutic tools for prostate cancer

The order-disorder transition in colloidal suspensions under shear flow

We study the order-disorder transition in colloidal suspensions under shear flow by performing Brownian dynamics simulations. We characterize the transition in terms of a statistical property of time-dependent maximum value of the structure factor. We find that its power spectrum exhibits the power-law behaviour only in the ordered phase. The power-law exponent is approximately -2 at frequencies greater than the magnitude of the shear rate, while the power spectrum exhibits the $1 / f$-type fluctuations in the lower frequency regime.Comment: 11 pages, 10 figures, v.2: We have made some small improvements on presentation

Perils and Prospects of Using Aggregate Area Level Socioeconomic Information as a Proxy for Individual Level Socioeconomic Confounders in Instrumental Variables Regression

A frequent concern in making statistical inference for causal effects of a policy or treatment based on observational studies is that there are unmeasured confounding variables. The instrumental variable method is an approach to estimating a causal relationship in the presence of unmeasured confounding variables. A valid instrumental variable needs to be independent of the unmeasured confounding variables. It is important to control for the confounding variable if it is correlated with the instrument. In health services research, socioeconomic status variables are often considered as confounding variables. In recent studies, distance to a specialty care center has been used as an instrument for the effect of specialty care vs. general care. Because the instrument may be correlated with socioeconomic status variables, it is important that socioeconomic status variables are controlled for in the instrumental variables regression. However, health data sets often lack individual socioeconomic information but contain area average socioeconomic information from the US Census, e.g., average income or education level in a county. We study the effects on the bias of the two stage least squares estimates in instrumental variables regression when using an area-level variable as a controlled confounding variable that may be correlated with the instrument. We propose the aggregated instrumental variables regression using the concept of Wald’s method of grouping, provided the assumption that the grouping is independent of the errors. We present simulation results and an application to a study of perinatal care for premature infants

Deep Learning using K-space Based Data Augmentation for Automated Cardiac MR Motion Artefact Detection

Quality assessment of medical images is essential for complete automation of image processing pipelines. For large population studies such as the UK Biobank, artefacts such as those caused by heart motion are problematic and manual identification is tedious and time-consuming. Therefore, there is an urgent need for automatic image quality assessment techniques. In this paper, we propose a method to automatically detect the presence of motion-related artefacts in cardiac magnetic resonance (CMR) images. As this is a highly imbalanced classification problem (due to the high number of good quality images compared to the low number of images with motion artefacts), we propose a novel k-space based training data augmentation approach in order to address this problem. Our method is based on 3D spatio-temporal Convolutional Neural Networks, and is able to detect 2D+time short axis images with motion artefacts in less than 1ms. We test our algorithm on a subset of the UK Biobank dataset consisting of 3465 CMR images and achieve not only high accuracy in detection of motion artefacts, but also high precision and recall. We compare our approach to a range of state-of-the-art quality assessment methods.Comment: Accepted for MICCAI2018 Conferenc