REGRESSION ADJUSTMENT AND STRATIFICATION BY PROPENSTY SCORE IN TREATMENT EFFECT ESTIMATION

Propensity score adjustment of effect estimates in observational studies of treatment is a common technique used to control for bias in treatment assignment. In situations where matching on propensity score is not possible or desirable, regression adjustment and stratification are two options. Regression adjustment is used most often and can be highly efficient, but it can lead to biased results when model assumptions are violated. Validity of the stratification approach depends on fewer model assumptions, but is less efficient than regression adjustment when the regression assumptions hold. To investigate these issues, by simulation we compare stratification and regression adjustments. We consider two stratification approaches; equal frequency classes and an approach the attempts to minimize the mean squared error (MSE) of the treatment effect estimate. The regression approach we consider is a Generalized Additive Model (GAM), that flexibly estimates the relations among propensity score, treatment assignment, and outcome. We find that, under a wide range of plausible data generating distributions, the GAM approach outperforms stratification in treatment effect estimation with respect to bias, variance, and thereby MSE. We illustrate approaches via analysis of data on insurance plan choice and its relation to satisfaction with asthma care

OPTIMAL PROPENSITY SCORE STRATIFICATION

Stratifying on propensity score in observational studies of treatment is a common technique used to control for bias in treatment assignment; however, there have been few studies of the relative efficiency of the various ways of forming those strata. The standard method is to use the quintiles of propensity score to create subclasses, but this choice is not based on any measure of performance either observed or theoretical. In this paper, we investigate the optimal subclassification of propensity scores for estimating treatment effect with respect to mean squared error of the estimate. We consider the optimal formation of subclasses within formation schemes that require either equal frequency of observations within each subclass or equal variance of the effect estimate within each subclass. Under these restrictions, choosing the partition is reduced to choosing the number of subclasses. We also consider an overalll optimal partition that produces an effect estimate with minimum MSE among all partitions considered. To create this stratification, the investigator must choose both the number of subclasses and their placement. Finally, we present a stratified propensity score analysis of data concerning insurance plan choice and its relation to satisfaction with asthma care

LEARNING FROM NEAR MISSES IN MEDICATION ERRORS: A BAYESIAN APPROACH

Medical errors originating in health care facilities are a significant source of preventable morbidity, mortality, and healthcare costs. Voluntary error report systems that collect information on the causes and contributing factors of medi- cal errors regardless of the resulting harm may be useful for developing effective harm prevention strategies. Some patient safety experts question the utility of data from errors that did not lead to harm to the patient, also called near misses. A near miss (a.k.a. close call) is an unplanned event that did not result in injury to the patient. Only a fortunate break in the chain of events prevented injury. We use data from a large voluntary reporting system of 836,174 medication errors from 1999 to 2005 to provide evidence that the causes and contributing factors of errors that result in harm are similar to the causes and contributing factors of near misses. We develop Bayesian hierarchical models for estimating the log odds of selecting a given cause (or contributing factor) of error given harm has occurred and the log odds of selecting the same cause given that harm did not occur. The posterior distribution of the correlation between these two vectors of log-odds is used as a measure of the evidence supporting the use of data from near misses and their causes and contributing factors to prevent medical errors. In addition, we identify the causes and contributing factors that have the highest or lowest log-odds ratio of harm versus no harm. These causes and contributing factors should also be a focus in the design of prevention strategies. This paper provides important evidence on the utility of data from near misses, which constitute the vast majority of errors in our data

The US in Uterus: A Collaborative Autoethnography of Psychologists Advocating for Reproductive Justice

In light of the recent Supreme Court decision to overturn Roe v. Wade, millions of people with uteruses have been forced to navigate precarious access to reproductive care. Although health service psychologists have an ethical responsibility to engage in reproductive justice advocacy, training programs often do not adequately address sexual and reproductive health. Therefore, we sought to better understand how health service psychologists’ personal and professional experiences influence each other and explore the ways in which we as reproductive beings and advocates sustain ourselves amidst tremendous sociopolitical uncertainty. In order to do so, we employed a feminist collaborative autoethnography approach grounded in critical theory. Attending to intersectional identities that help shape diverse expectations and experiences, two early career psychologists and four trainees uncovered 12 domains: barriers in academia; reproductive (dis)empowerment; relational connection; power(lessness) associated with social locations; internalization of sex-negative messages; the influence of sociopolitical climate; burdens related to reproductive rights; evaluations of reproductive justice efforts; component of professional identity; expectations from family and community; overwhelming and exhausting advocacy; and fears of inadequacy. We conclude with limitations and implications for the continued promotion of advocacy through practice and training within and beyond the field of psychology

Antidepressants and Breast and Ovarian Cancer Risk: A Review of the Literature and Researchers\u27 Financial Associations with Industry

Background Antidepressant (AD) use has been purported to increase the risk of breast and ovarian cancer, although both epidemiological and pre-clinical studies have reported mixed results [1]–[6]. Previous studies in a variety of biomedical fields have found that financial ties to drug companies are associated with favorable study conclusions [7]. Methods and Findings We searched English-language articles in MEDLINE, PsychINFO, the Science Citations Index and the Cochrane Central Register of Controlled Clinical Trials (through November 2010). A total of 61 articles that assessed the relationship between breast and ovarian cancer and AD use and articles that examined the effect of ADs on cell growth were included. Multi-modal screening techniques were used to investigate researchers\u27 financial ties with industry. A random effects meta-analysis was used to pool the findings from the epidemiological literature. Thirty-three percent (20/61) of the studies reported a positive association between ADs and cancer. Sixty-seven percent (41/61) of the studies reported no association or antiproliferative effect. The pooled odds ratio for the association between AD use and breast/ovarian cancer in the epidemiologic studies was 1.11 (95% CI, 1.03–1.20). Researchers with industry affiliations were significantly less likely than researchers without those ties to conclude that ADs increase the risk of breast or ovarian cancer. (0/15 [0%] vs 20/46 [43.5%] (Fisher\u27s Exact test P = 0.0012). Conclusions Both the pre-clinical and clinical data are mixed in terms of showing an association between AD use and breast and ovarian cancer. The possibility that ADs may exhibit a bi-phasic effect, whereby short-term use and/or low dose antidepressants may increase the risk of breast and ovarian cancer, warrants further investigation. Industry affiliations were significantly associated with negative conclusions regarding cancer risk. The findings have implications in light of the 2009 USPSTF guidelines for breast cancer screening and for the informed consent process

Think Outside the Color Box: Probabilistic Target Selection and the SDSS-XDQSO Quasar Targeting Catalog

We present the SDSS-XDQSO quasar targeting catalog for efficient flux-based quasar target selection down to the faint limit of the Sloan Digital Sky Survey (SDSS) catalog, even at medium redshifts (2.5 <~ z <~ 3) where the stellar contamination is significant. We build models of the distributions of stars and quasars in flux space down to the flux limit by applying the extreme-deconvolution method to estimate the underlying density. We convolve this density with the flux uncertainties when evaluating the probability that an object is a quasar. This approach results in a targeting algorithm that is more principled, more efficient, and faster than other similar methods. We apply the algorithm to derive low-redshift (z < 2.2), medium-redshift (2.2 <= z 3.5) quasar probabilities for all 160,904,060 point sources with dereddened i-band magnitude between 17.75 and 22.45 mag in the 14,555 deg^2 of imaging from SDSS Data Release 8. The catalog can be used to define a uniformly selected and efficient low- or medium-redshift quasar survey, such as that needed for the SDSS-III's Baryon Oscillation Spectroscopic Survey project. We show that the XDQSO technique performs as well as the current best photometric quasar-selection technique at low redshift, and outperforms all other flux-based methods for selecting the medium-redshift quasars of our primary interest. We make code to reproduce the XDQSO quasar target selection publicly available

The Mechanism Underlying Transient Weakness in Myotonia Congenita

In addition to the hallmark muscle stiffness, patients with recessive myotonia congenita (Becker disease) experience debilitating bouts of transient weakness that remain poorly understood despite years of study. We performed intracellular recordings from muscle of both genetic and pharmacologic mouse models of Becker disease to identify the mechanism underlying transient weakness. Our recordings reveal transient depolarizations (plateau potentials) of the membrane potential to -25 to -35 mV in the genetic and pharmacologic models of Becker disease. Both Na + and Ca 2+ currents contribute to plateau potentials. Na + persistent inward current (NaPIC) through Na V 1.4 channels is the key trigger of plateau potentials and current through Ca V 1.1 Ca 2+ channels contributes to the duration of the plateau. Inhibiting NaPIC with ranolazine prevents the development of plateau potentials and eliminates transient weakness in vivo. These data suggest that targeting NaPIC may be an effective treatment to prevent transient weakness in myotonia congenita

Nearest Neighbor Networks: clustering expression data based on gene neighborhoods

<p>Abstract</p> <p>Background</p> <p>The availability of microarrays measuring thousands of genes simultaneously across hundreds of biological conditions represents an opportunity to understand both individual biological pathways and the integrated workings of the cell. However, translating this amount of data into biological insight remains a daunting task. An important initial step in the analysis of microarray data is clustering of genes with similar behavior. A number of classical techniques are commonly used to perform this task, particularly hierarchical and K-means clustering, and many novel approaches have been suggested recently. While these approaches are useful, they are not without drawbacks; these methods can find clusters in purely random data, and even clusters enriched for biological functions can be skewed towards a small number of processes (e.g. ribosomes).</p> <p>Results</p> <p>We developed Nearest Neighbor Networks (NNN), a graph-based algorithm to generate clusters of genes with similar expression profiles. This method produces clusters based on overlapping cliques within an interaction network generated from mutual nearest neighborhoods. This focus on nearest neighbors rather than on absolute distance measures allows us to capture clusters with high connectivity even when they are spatially separated, and requiring mutual nearest neighbors allows genes with no sufficiently similar partners to remain unclustered. We compared the clusters generated by NNN with those generated by eight other clustering methods. NNN was particularly successful at generating functionally coherent clusters with high precision, and these clusters generally represented a much broader selection of biological processes than those recovered by other methods.</p> <p>Conclusion</p> <p>The Nearest Neighbor Networks algorithm is a valuable clustering method that effectively groups genes that are likely to be functionally related. It is particularly attractive due to its simplicity, its success in the analysis of large datasets, and its ability to span a wide range of biological functions with high precision.</p

Temporal course of cognitive and behavioural changes in motor neuron diseases

Background Cognitive and behavioural dysfunction may occur in people with motor neuron disease (MND), with some studies suggesting an association with the C9ORF72 repeat expansion. Their onset and progression, however, is poorly understood. We explored how cognition and behaviour change over time, and whether demographic, clinical and genetic factors impact these changes. Methods Participants with MND were recruited through the Phenotype-Genotype-Biomarker study. Every 3–6 months, the Edinburgh Cognitive and Behavioural ALS Screen (ECAS) was used to assess amyotrophic lateral sclerosis (ALS) specific (executive functioning, verbal fluency, language) and ALS non-specific (memory, visuospatial) functions. Informants reported on behaviour symptoms via semi-structured interview. Results Participants with neuropsychological data at ≥3 visits were included (n=237, mean age=59, 60% male), of which 18 (8%) were C9ORF72 positive. Baseline cognitive impairment was apparent in 18 (8%), typically in ALS specific domains, and associated with lower education, but not C9ORF72 status. Cognition, on average, remained stable over time, with two exceptions: (1) C9ORF72 carriers declined in all ECAS domains, (2) 8%–9% of participants with baseline cognitive impairment further declined, primarily in the ALS non-specific domain, which was associated with less education. Behavioural symptoms were uncommon. Conclusions In this study, cognitive dysfunction was less common than previously reported and remained stable over time for most. However, cognition declines longitudinally in a small subset, which is not entirely related to C9ORF72 status. Our findings raise questions about the timing of cognitive impairment in MND, and whether it arises during early clinically manifest disease or even prior to motor manifestations

A Simple Likelihood Method for Quasar Target Selection

We present a new method for quasar target selection using photometric fluxes and a Bayesian probabilistic approach. For our purposes we target quasars using Sloan Digital Sky Survey (SDSS) photometry to a magnitude limit of g=22. The efficiency and completeness of this technique is measured using the Baryon Oscillation Spectroscopic Survey (BOSS) data, taken in 2010. This technique was used for the uniformly selected (CORE) sample of targets in BOSS year one spectroscopy to be realized in the 9th SDSS data release. When targeting at a density of 40 objects per sq-deg (the BOSS quasar targeting density) the efficiency of this technique in recovering z>2.2 quasars is 40%. The completeness compared to all quasars identified in BOSS data is 65%. This paper also describes possible extensions and improvements for this techniqueComment: Updated to accepted version for publication in the Astrophysical Journal. 10 pages, 10 figures, 3 table